What are ETL Tools?

Extract, transform, and load a data management process for collecting data from multiple sources to support discovery, analysis, reporting, and decision-making. ETL tools are instruments that automate the process of turning raw data into information that can deliver actionable business intelligence. They extract data from underlying sources, transform data to satisfy the data models enterprise repositories, and load data into its target destination.

“Transform” is perhaps the most important part of ETL: Making sure all data is in the proper type and format for its intended use. The term has been around since the 1970s and typically has referred to data warehousing, but now is also used to power Big Data analytics applications.

Also read: Best Big Data Tools & Software for Analytics

Choosing ETL Tools

There are a variety of factors that determine which ETL tool suits your needs best. Let’s explore some of the most relevant ones.

Business goals

Your business goals are the most vital consideration when choosing ETL tools. The data integration needs of the business require ETL tools that ensure speed, flexibility, and effectiveness.

Use case

Client use cases determine what kind of ETL tools to implement. For instance, where the implementation covers different use cases or involves different cloud options, modern ETL approaches trump older ETL approaches.

Capabilities

A good ETL tool should not only be flexible enough to read and write data regardless of location but also enable users to switch providers without long delays.

Integration

An organization’s scope and frequency of integration efforts determine the kind of ETL tools they require. Organizations with more intensive tasks may require more integrations daily. They should ensure the tools they choose satisfy their integration needs.

Data sources

Data sources determine the type of ETL tools to be implemented, as some organizations may need to work with only structured data while others may have to consider both structured and unstructured data or specific data types.

Budget

Considering your budget as you research prospective ETL solutions is crucial, as costs can rise considerably with ETL tools that need lots of data mapping and manual coding. Knowing not only the ETL tool but what supporting activities you will be required to pay for is key to ensuring you get the right ETL tool working optimally.

Top ETL Tools

Here are our picks for the top ETL tools based on our survey and analysis of the market.

Oracle Data Integrator

Oracle Data Integrator (ODI) is a comprehensive data integration platform that encompasses data integration requirements such as high-volume, high-performance batch loads, SOA-enabled data services, and event-driven trickle-feed integration processes. It is part of Oracle’s data integration suite of solutions for data quality, cloud data, metadata management, and big data preparation.

Oracle Data Integrator offers support for both unstructured and structured data and is available as both an enterprise ETL tool and a cloud-based ETL tool.

Key Differentiators

• High-Performance Data Transformation: ODI offers high-performance data transformation through powerful ETL that minimizes the performance impact on source systems. It also lowers cost by using the power of the database system CPU and memory to carry out transformations instead of using independent ETL transformation servers.
• Out-of-the-Box Integrations: The Enterprise Edition of ODI provides a comprehensive selection of prebuilt connectors. Its modular design offers developers greater flexibility when connecting diverse systems.
• Heterogeneous System Support: ODI offers heterogeneous system support with integrations for big data, popular databases and other technologies.

Cons: ODI may require advanced IT skills for data manipulation, as implementation may prove to be complex. Licensing also may prove to be expensive for smaller organizations and teams. Furthermore, it lacks the drag-and-drop features characteristic of other ETL tools.

Azure Data Factory

Azure Data Factory simplifies hybrid data integration through a serverless and fully managed integration service that allows users to integrate all their data.

The service provides more than 90 built-in connectors at no extra cost and allows users to simply construct not only ETL processes but also ELT processes, transforming the data in the data warehouse. These processes can be constructed through coding or through an intuitive code-free environment. The tool also improves overall efficiency through autonomous ETL processes and improved insights across teams.

Key Differentiators

• Code-Free Data Flows: Azure Data Factory offers a data integration and transformation layer that accelerates data transformation across users’ digital transformation initiatives. Users can prepare data, build ETL and ELT processes, and orchestrate and monitor pipelines code-free. Intelligent intent-driven mapping automates copy activities to transform faster.
• Built-in Connectors: Azure Data Factory provides one pay-as-you-go service to save users from the challenges of cost, time, and the number of solutions associated with ingesting data from multiple and heterogeneous sources. It offers over 90 built-in connectors and underlying network bandwidth of up to 5 Gbps throughput.
• Modernize SSIS in a Few Clicks: Data Factory enables organizations to rehost and extend SSIS in a handful of clicks.

Con: The tool supports some data hosted outside of Azure, but it primarily focuses on building integration pipelines connecting to Azure and other Microsoft resources in general. This is a limitation for users running most of their workloads outside of Azure.

Talend Open Studio

Talend helps organizations understand the data they have, where it is, and its usage by providing them with the means to measure the health of their data and evaluate how much their data supports their business objectives.

Talend Open Studio is a powerful open-source ETL tool designed to enable users to extract, standardize and transform datasets into a consistent format for loading into third-party applications. Through its numerous built-in business intelligence tools, it can provide value to direct marketers.

Key Differentiators

• Graphical Conversion Tools: Talend’s graphical user interface (GUI) enables users to easily map data between source and destination areas by selecting the required components from the palette and placing them into the workspace.
• Metadata Repository: Users can reuse and repurpose work through a metadata repository to improve both efficiency and productivity over time.
• Database SCD Tools: Tracking slowly changing dimensions (SCD) can be helpful for keeping a record of historical changes within an enterprise. For databases such as MSSQL, MySQL, Oracle, DB2, Teradata, Sybase, and more, this feature is built-in.

Cons: Installation and configuration can take a significant amount of time due to the modular nature of the tool. Additionally, to realize its full benefits, users may be required to upgrade to the paid version.

Informatica PowerCenter

Informatica is a data-driven company passionate about creating and delivering solutions that expedite data innovations. PowerCenter is Informatica’s data integration product, which is a metadata-driven platform with the goals of improving the collaboration between business and IT teams and streamlining data pipelines.

Informatica enables enterprise-class ETL for on-premises data integration while providing top-class ETL, ELT, and elastic Spark-based data processing for every cloud data integration needed through artificial intelligence (AI)-powered cloud-native data integration.

Key Differentiators

• PowerCenter Integration Service: PowerCenter Integration Service assists to read and manage the integration’s workflow, which in turn delivers multiple integrations according to the needs of the organization.
• Optimization Engine: Informatica’s Optimization Engine sends users’ data processing tasks to the most cost-effective destination, whether traditional ETL, Spark serverless processing, cloud ecosystem pushdown, or cloud data warehouse pushdown. This ensures the right processing is chosen for the right job, ensuring controlled and optimized costs.
• Advanced Data Transformation: Informatica PowerCenter offers advanced data transformation to help unlock the value of non-relational data through exhaustive parsing of JSON, PDF, XML, Internet of Things (IoT), machine data, and more.

Con: For higher volumes, the computational resource requirement may be high.

Microsoft SSIS

Microsoft SQL Server Integration Services (SSIS) is a platform for developing enterprise-grade data transformation and integration solutions to solve complex business problems.

Integration Services can be used to handle these problems by downloading or copying files, loading data warehouses, managing SQL data and objects, and cleansing and mining data. SSIS can extract data from XML files, Flat files, SQL databases, and more. Through a GUI, users can build packages and perform integrations and transformations.

Key Differentiators

• Transformations: SSIS offers a rich set of transformations such as business intelligence (BI), row, rowset, split and join, auditing, and custom transformations.
• SSIS Designer: SSIS Designer is a graphical tool that can be used to build and maintain Integration Service packages. Users can use it to construct the control flow and data flows in a package as well as to add event handlers to packages and their objects.
• Built-in Data Connectors: SSIS supports diverse built-in data connectors that enable users to establish connections with data sources through connection managers.

Cons: SSIS has high CPU memory usage and performance issues with bulk data workloads. The tool also requires technical expertise, as the manual deployment process can be complex.

AWS Glue

AWS Glue is a serverless data integration service that simplifies the discovery, preparation, and combination of data for analytics, application development, and machine learning. It possesses the data integration capabilities that enterprises require to analyze their data and put it to use in the shortest time possible. ETL developers and data engineers can visually build, execute, and monitor ETL workflows through AWS Glue Studio.

Key Differentiators

• ETL Jobs at Scale: AWS Glue enables users to simply run and manage ETL jobs at scale, as it automates a significant part of the effort required for data integration.
• ETL Jobs Without Coding: Through AWS Glue Studio, users can visually create, execute, and monitor AWS ETL jobs. They can create ETL jobs that move and transform data through a drag-and-drop editor, and AWS Glue will automatically generate the code.
• Event-Driven ETL Pipelines: AWS Glue enables users to build event-driven ETL pipelines, as Glue can run ETL jobs as new data arrives.

Con: Since AWS Glue is made for AWS console and its products, it makes it difficult to use for other technologies.

Integrate.io

Integrate.io is a data integration solution and ETL provider that offers customers all the tools they require to customize their data flows and deliver better data pipelines for improved insights and customer relationships. This ETL service is compatible with data lakes and connects with most major data warehouses, proving that it is one of the most flexible ETL tools available.

Key Differentiators

• Rapid, Low-Code Implementation: Integrate.io enables users to transform their data with little to no code, offering them the flexibility that alleviates the complexities of dependence on extensive coding or manual data transformations.
• Reverse ETL: Integrate.io’s low-code Reverse ETL platform enables users to convert their data warehouses into the heartbeats of their organizations by providing actionable data across users’ teams. Users can focus less on data preparation and more on actionable insights.
• Single Source of Truth: Users have the ability to combine their data from all of their sources and send them a single destination with Integrate.io. A single source of truth for customer data enables organizations to save time, optimize their insights, and improve their market opportunities.

Con: The tool does not support on-premises solutions.

Hevo Data

Hevo Data is a no-code data pipeline that simplifies the ETL process and enables users to load data from any data source, including software-as-a-service (SaaS) applications, databases, streaming services, cloud storage, and more.

Hevo offers over 150 data sources, with more than 40 of them available for free. The tool also enriches and transforms data into a format ready for analysis without users writing a single line of code.

Key Differentiators

• Near Real-Time Replication: Near real-time replication is available to users of all plans. For database sources, it is available via pipeline prioritization, while for SaaS sources, it is dependent on API (application programming interface) call limits.
• Built-in Transformations: Hevo allows users to format their data on the fly with its drag-and-drop preload transformations and to generate analysis-ready data in their warehouses using post-load transformation.
• Reliability at Scale: Hevo provides top-class fault-tolerant architecture with the ability to scale with low latency and zero data loss.

Con: Some users report that Hevo is slightly complex, especially concerning operational support.

Comparing the Top ETL Tools

Read next: Top Data Quality Tools & Software

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To address those concerns, Microsoft recently released its Responsible AI Standard and made a number of changes, the most noteworthy of which is to retire the company’s emotional recognition AI technology.

Responsible AI

Microsoft’s new policy contains a number of major announcements.

• New customers must apply for access to use facial recognition operations in Azure Face API, Computer Vision and Video Indexer, and existing customers have one year to apply and be approved for continued access to the facial recognition services.
• Microsoft’s policy of Limited Access adds use case and customer eligibility requirements to access the services.
• Facial detection capabilities—including detecting blur, exposure, glasses, head pose, landmarks, noise, occlusion, and facial bounding box—will remain generally available and do not require an application.

The centerpiece of the announcement is that the software giant “will retire facial analysis capabilities that purport to infer emotional states and identity attributes such as gender, age, smile, facial hair, hair, and makeup.”

Microsoft noted that “the inability to generalize the linkage between facial expression and emotional state across use cases, regions, and demographics…opens up a wide range of ways they can be misused—including subjecting people to stereotyping, discrimination, or unfair denial of services.”

Also read: AI Suffers from Bias—But It Doesn’t Have To

Moving Away from Facial Analysis

There are a number of reasons why major IT players have been moving away from facial recognition technologies, including limiting law enforcement access to the technology.

Fairness concerns

Automated facial analysis and facial recognition software have always generated controversy. Combine this with the often inherent societal biases of AI systems and the potential to exacerbate issues of bias intensifies. Many commercial facial analysis systems today inadvertently exhibit bias in categories such as race, age, culture, ethnicity and gender. Microsoft’s Responsible AI Standard implementation aims to help the company get ahead of potential issues of bias through its outlined Fairness Goals and Requirements.

Appropriate use controls

Regardless of Azure AI Custom Neural Voice’s boundless potential in entertainment, accessibility and education, it could also be greatly misused to deceive listeners by impersonating speakers. Microsoft’s Responsible AI program, plus the Sensitive Users review process essential to the Responsible AI Standard, reviewed its Facial Recognition and Custom Neural Voice technologies to develop a layered control framework. By limiting these technologies and implementing these controls, Microsoft hopes to safeguard the technologies and users from misuse while ensuring that their implementations are of value.

Lack of consensus on emotions

Microsoft’s decision to do away with public access to the emotion recognition and facial characteristics identification features of its AI is due to the lack of a distinct consensus on the definition of emotions. Experts from within and outside the company have pointed out the effect of this lack of consensus on emotion recognition technology products, as they generalize inferences across demographics, regions and use cases. This hinders the ability of the technology to provide appropriate solutions to the problems it aims to solve and ultimately impacts its trustworthiness.

The skepticism associated with the technology comes from its disputed efficacy and justification for its use. Human rights groups contend that emotion AI is discriminatory and manipulative. One study found that emotion AI constantly identified White subjects to have more positive emotions than Black subjects across two different facial recognition software platforms.

Intensifying privacy concerns

There is increasing scrutiny of facial recognition technologies and their unethical use for public surveillance and mass face detection without consent. Even though facial analysis collects generic data that is kept anonymous—such as Azure Face’s service that infers identity attributes like gender, hair, age, and more—anonymization does not alleviate ever-growing privacy concerns. Aside from consenting to such technologies, subjects may often harbor concerns about how the data collected by these technologies is stored, protected and used.

Also read: What Does Explainable AI Mean for Your Business?

Facial Detection and Bias

Algorithmic bias sees machine learning algorithms portray the biases of either their creators or their input data. The large-scale usage of these models in our technology-dependent lives means that their use cases are at risk of adopting and proliferating mass-produced biases.

Facial detection technologies struggle to produce accurate results in use cases involving women, dark-skinned people and older adults, as it is common to find these technologies being trained by facial image datasets dominated by Caucasian subjects. Bias in facial analysis and facial recognition technologies yields real-life consequences, such as the following examples.

Inaccuracy

Regardless of the strides that facial detection technologies have taken, bias often yields inaccurate results. Studies show that face detection technologies generally perform better with lighter skin complexions. One study reports findings of the identification of lighter-skinned males having a maximum error rate of 0.8% compared to up to 34.7% for dark-skinned women.

The failures in recognizing the faces of dark-skinned people have led to instances where the technology has been used wrongly by law enforcement. In February 2019, a Black man was accused of not only shoplifting but also attempting to hit a police officer with a car even though he was forty miles away from the scene of the crime at the time. He spent 10 days in jail and his defense cost him $5,000.

Since the case was dismissed for lack of evidence in November 2019, the man is suing the authorities involved for false arrest, imprisonment and civil rights violation. In a similar case, another man was wrongfully arrested as a result of inaccuracy in facial recognition. Such inaccuracies raise concerns about how many wrongful arrests and convictions may have taken place.

Several vendors of the technology, such as IBM, Amazon, and Microsoft, are aware of such limitations in areas like law enforcement and the implication of the technology for racial injustice and have moved to prevent potential misuse of their software. Microsoft’s policy prohibits the use of its Azure Face by or for state police in the United States.

Decision making

It is not uncommon to find facial analysis technology being used to assist in the evaluation of video interviews with job candidates. These tools influence recruiters’ hiring decisions using data they generate by analyzing facial expressions, movements, choice of words, and vocal tone. Such use cases are meant to lower hiring costs and increase efficiency by expediting the screening and recruitment of new hires.

However, failure to train such algorithms on datasets that are both large enough and diverse enough introduces bias. Such bias may deem certain people to be more suitable for employment than others. False positives or negatives may be the determinants of the employment of an unsuitable candidate as well as the rejection of the most suitable one. As long as they contain bias, the same results will likely be experienced in any similar context where the technology is used to make decisions based on people’s faces.

What’s Next for Facial Analysis?

All of this doesn’t mean that Microsoft is discarding its facial analysis and recognition technology entirely, as the company recognizes that these features and capabilities can yield value in controlled accessibility contexts. Microsoft’s biometric systems such as facial recognition will be limited to partners and customers of managed services. The availability of facial analysis will continue to be available to users until June 30, 2023, via the Limited Access arrangement.

Limited Access only applies to users working directly with the Microsoft accounts team. Microsoft has provided a list of approved Limited Access use cases here. Users have until then to submit applications for approval to continue using the technology. Such systems will also be limited to use cases that are deemed acceptable. Additionally, a code of conduct and guardrails will be used to ensure authorized users do not misuse the technology.

The Computer Vision and Video Indexer celebrity recognition features are also subject to Limited Access. Video Indexer’s face identification also falls under Limited. Customers will no longer have general access to facial recognition from these two services, in addition to Azure Face API.

As a result of its review, Microsoft announced, “We are undertaking responsible data collections to identify and mitigate disparities in the performance of the technology across demographic groups and assessing ways to present this information in a way that would be insightful and actionable for our customers.”

Read next: Best Machine Learning Software

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Data lineage empowers data scientists, engineers, and data teams with granular visibility of data dynamics to help them and their organizations keep tabs on errors, carry out system migrations, execute process changes with lower risk, and more.

One challenge is documenting all that, and that’s where data lineage tools come in handy.

What is the Best Way to Document Data Lineage?

Data lineage documentation is a resource-intensive process in terms of personnel, funds, time, etc. There are two methods of documenting data lineage; descriptive and automated. Each method has its benefits and drawbacks, and each can be optimal depending on the nature of the data lineage project.

Descriptive data lineage uses a manual process to describe data lineage, and as such it has a large time and effort requirement. That’s where automated data lineage comes in.

Automated data lineage reduces time lost in the collection, storage and processing of chunks of data because it replaces manual processes plus human error with intelligent operations, software and even AI. Automated data lineage eases root cause analysis, helps organizations achieve and maintain compliance, ensures data accuracy and simplifies BI system migration.

Also see the Top Data Catalog Tools & Software

Best Data Lineage Tools

Here are some of the top data lineage tools, based on our analysis and review of the market.

Alation

Alation is an automated data lineage tool that offers a wide range of data intelligence solutions like data search and discovery, data stewardship, data governance, analytics, and transformation. It is AI-driven, with an advanced behavioral analysis engine to generate actionable insights.

Moreover, Alation profiles data and examines usage to ensure users have the accurate insights and offers insight into data usage by users. It also has built-in collaboration capabilities.

Key Differentiators

• Behavioral Intelligence: Alation improves users’ productivity through behavioral intelligence by indexing a broad array of data sources such as cloud data lakes, relational databases, and file systems using machine learning.
• Seamless Collaboration: The collaboration capabilities offered by Alation empower enterprises to make better decisions by providing enterprise-wide access to credible data to foster seamless collaboration on data assets with knowledge capture and integrated communication.
• Deep Connectivity: Alation streamlines analytics by providing connectivity to many popular business intelligence (BI) tools and data sources through an Open Connector SDK (software development kit) and APIs (application programming interfaces).
• Guided Navigation: The quality and confidence of analytics is bettered by surfacing flags, recommendations, and policies as data consumers search using natural language or make queries in Alation’s intelligent SQL editor.

Con: Processing complex queries and large amounts of data impacts performance.

Pricing: Even though Alation offers a weekly live demo, you would have to contact their sales team for pricing information.

See the Top DataOps Tools

Atlan

Atlan is a modern data workspace that aims to empower data democratization within organizations while maintaining security and governance standards. The platform offers capabilities in areas such as data lineage and governance, data quality and profiling, data cataloging and discovery, and data exploration and integration.

In addition, Atlan has automated data profiling, discovery, an intuitive search interface and more features that help data teams free up huge chunks of their time to improve their productivity and replace tedious manual tasks.

Key Differentiators

• Auto-Classification of PII: Atlan’s algorithms automatically classify personally identifiable information (PII) and sensitive data such as customer names, physical and email addresses, and credit card information.
• Column-Level Access Controls: Atlan empowers users to create granular access controls, ranging from individual columns to BI dashboards, by databases and schemas as well as tag-based data asset grouping.
• Downstream Integrations: With Atlan, customers have access to out-of-the-box integrations with Power BI, Sisense, Tableau, and other popular BI tools.
• SQL Parsing to Automate Lineage: Atlan automatically parses through users’ SQL query logs in their BI tools and data warehouses to create data lineage visuals.

Con: Atlan has limited data transformation nodes.

Pricing: As pricing information is not publicly available, you would have to contact Atlan. They also have a free trial upon request.

OvalEdge

OvalEdge is a data catalog engineered for end-to-end data governance, quick and credible analytics, and privacy compliance. It has a cost-effective on-premises data catalog and governance tool set that crawls the BI platforms, databases, data lakes, and ETL (extract, transform, load) tools of an organization to build an easy-access, smart inventory of their data assets.

Additionally, OvalEdge offers a discovery platform that analysts, regardless of experience, can use to discover data and generate influential insights quickly. It also uses advanced algorithms and machine learning to automatically organize data.

Key Differentiators

• Source Code Parsing: OvalEdge algorithms parse numerous kinds of source code to automatically build data lineage before it is enhanced by experts who learn, validate, and correct the lineage entries.
• Automation: OvalEdge allows its users to access its data lineage by using back-end algorithms to automate various processes.
• Lineage Depiction at Object Level: As OvalEdge considers report files and tables as objects, OvalEdge’s graphical user interface (GUI) depicts the lineage at object level.
• Tracking Data at System Level: OvalEdge enables users to track the flow of data at system level, cutting across ERP systems, data warehouses, and reporting systems.

Con: Some UX (user experience) features can introduce a learning curve to the tool.

Pricing: The Essential package offers manual lineage features and starts at $15,600 per year. The Professional and Enterprise packages offer automated lineage features, but you would have to contact OvalEdge sales.

Collibra

Collibra is a data intelligence company with a cloud-based platform that merges business and IT to impart a data-driven culture to today’s digital enterprise. It has a data dictionary that documents the technical metadata of an organization and its usage. It also describes data structure, relationship to other data, format, origin, and use, providing a searchable repository for users.

Moreover, Collibra Data Lineage automatically maps relationships between applications, systems, and reports to ensure enterprises enjoy a context-rich view of data.

Key Differentiators

• Automated Lineage Extraction: Collibra uses native linear harvesters to allow users to automatically extract and maintain lineage from source systems, ETL tools, BI tools, and SQL dialects and save time that would have gone into manually maintaining and documenting lineage.
• Business Lineage: With an interactive lineage diagram depicting summary lineage, Collibra enables users to trace data flows from source to destination. Users can access data lineage throughout the enterprise to enhance the visibility and credibility of data.
• Detailed Technical Lineage: Users can swiftly understand the effects of potential changes through comprehensive technical lineage at the transformation, table, column, and SQL query levels.
• Indirect Lineage: Collibra allows users to view direct data flows and indirect relationships to provide a full picture of data dependencies.

Cons: The UI (user interface) may be clunky and unintuitive. Additionally, data lineage capabilities need to mature to better handle customer needs.

Pricing: Contact Collibra, as pricing information is not publicly available.

Also read: Top 9 Data Modeling Tools & Software

Octopai

Octopai is an automated data intelligence platform that offers data and analytics teams multilayered data catalog, data discovery, and data lineage, which allow organizations to trace their assets, understand their data flows, and add credibility to their resources. As a cross-platform metadata management automation tool, it enables its users to discover and govern shared metadata.

Additionally, Octopai automatically gathers metadata—from databases, ETL and reporting tools—which is stored in a centralized repository. A smart engine searches all metadata to quickly present results.

Key Differentiators

• Cross-System Lineage: Cross-system lineage employs augmented and automated methods to reflect data flows and dependencies to provide a comprehensive cross-system view of data landscapes. This enables users to understand the impact of a process change, a broken process, as well as high-level data flow visualization.
• End-to-End Column Lineage: End-to-end column lineage shows column-to-column level lineage connecting systems, from the point of entry to the data landscape to reporting and analytics. This helps data teams understand the impact of changes to source system columns and carry out root cause analysis and column-level data-flow visualization.
• Inner-Systems Lineage: Octopai helps users take a deep dive into details of a particular system through inner-systems lineage to provide column-level visibility from understanding the data flow and logic of each column, regardless of the complexity of the object, process, or report.

Cons: The UI may come off as clunky, and the product may present a learning curve for business users.

Pricing: Contact the vendor for current pricing information.

Datameer

Datameer provides a data engineering and analytics life cycle platform that enables data teams to directly transform and model data in their cloud warehouses using either SQL code or a no-code UI to handle complex data analytics projects.

Featuring 70+ source connectors, for ingestion of unstructured, semi-structured and unstructured data, Datameer goes beyond data ingestion to data preparation, exploration and consumption. With its interactive spreadsheet-like interface, Datameer users can blend, transform, and enrich complex data.

Key Differentiators

• Trusted Deployment: Datameer provides automated documentation, metadata enrichment, and a built-in semantic layer to encourage responsible self-service. Datameer maintains lineage and documentation throughout users’ project life cycles.
• SQL and No Code: Datameer fosters collaboration for teams using SQL and no-code to transform, explore, and publish new data models for analytics, machine learning, and reporting. SQL allows users to define transformations in native SQL syntax while no-code uses a library of prebuilt functions and operations to expedite development. Datameer’s modular design enables users to combine both SQL and no code.
• Advanced Capabilities: With other capabilities such as an enterprise-grade platform that scales to satisfy business need, easy data connectivity, dynamic schemas for fast analytic modeling, and an analyst-friendly spreadsheet-like interface, Datameer qualifies as a modern data engineering solution.

Cons: Datameer may experience lags when dealing with large data volumes.

Pricing: Even though Datameer offers a demo and a 14-day free trial, pricing information is only available after contacting Datameer.

Comparison of Data Lineage Tools

Choosing Data Lineage Tools

Your choice of data lineage tools will be influenced by several factors. You want a tool with the ability to not only trace but also verify data history, as tractability makes sure you have high-quality data. Immutability should also come into consideration, as it brings credibility to data lineage tools.

Data lineage tools with collaboration features ensure remote teams collaborate on shared data. They also ensure different types of teams across the enterprise are involved in the data journey. Additionally, you should consider how easily integrated the tool is with third-party applications, as there are many tools and stages involved in a data journey.

For your unique use case, always consider the tools with the features that satisfy your needs and are within your budget. Finally, make good use of hands-on demos and free trials, as they provide an opportunity to get first-hand experience of your shortlisted tools.

Read next: Top Data Quality Tools & Software

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Even though data wrangling is the most time-intensive component of data processing, it yields indispensable insights that impact future uses of the data.

What is Data Wrangling?

Data wrangling is the process of cleaning, restructuring, and organizing raw data into a more processed format to make it suitable for analysis and visualization purposes. It helps data teams to improve and expedite decision-making processes, which leads to faster, more accurate, and higher-quality insights for enterprises. It also empowers enterprises to tackle more complex data faster.

Data wrangling tools can be used to generate financial insights, improve data quality, carry out customer behavior analysis, uncover data trends, and detect fraud among other uses.

Data Wrangling Benefits

• Easy Data Handling: Data wrangling transforms raw and unstructured data into usable formats. It also enriches data to make it more useful and helps data offer more impactful insights.
• A Better Understanding of Audiences: When enterprises aggregate different data sources, they can better understand their end users to allow them to drive the right advertising campaigns and content strategies.
• Simpler and More Efficient Data Analysis: The wrangling and transformation of raw data empowers enterprises to swiftly and efficiently perform analysis on complex data.
• Impactful Visualization: Wrangled data is easier to visualize and can be exported to any visual analytics platform for analysis.
• Standardized Data: Enterprises can use automated data integration tools as data wrangling approaches to clean and transform source data into a standardized format. Depending on end requirements, this format is reusable and can allow enterprises to perform cross-dataset analytics.
• Data Usability: By transforming data into a usable format, data wrangling ensures data is compatible with the target system. Accurately wrangled data also ensures quality data is available for downstream analysis.
• Faster Decision-Making: Management teams can make decisions much faster, as data wrangling aims to provide the highest quality output to influence decision-making in the shortest time.

Also read: Tips for Processing Real-Time Data in a Data Center

Data Wrangling Process

As much as data wrangling can be a fluid process, it involves the steps of data extraction, structuring, cleaning, enriching, and validating.

Data extraction

Even though data extraction may not be considered part of the typical data wrangling process, it is crucial. Before transforming data, you have to first collect it. You have to plan on what data you require and determine the sources. Unstructured data can be pulled from sources such as a third-party repository, social media, websites, and more.

Data extraction empowers the consolidation, processing, and refinement of data before transformation. After retrieving heterogeneous data from disparate sources, you would need to compile the data sources to be comprehensible and examinable to reveal trends and patterns in the data.

Exploratory data analysis (EDA)

Exploratory data analysis involves carrying out a set of initial investigations to determine the main characteristics of data, often through graphics and summary statistics. Exploratory data analysis does not have to be carried out as the second step of data wrangling, as it is determined by the condition of the dataset and the work needed.

While dealing with unstructured data, EDA has multiple techniques. The first is quantitative data analysis, which utilizes mathematical and statistical analysis to provide discrete values and results. With this type of analysis, EDA uses visualization techniques like dimensionality reduction to reveal relationships between data variables. This makes it easy to discover patterns and handle anomalies and outliers.

For unstructured text data, qualitative data analysis mainly applies. Qualitative data analysis techniques require social intelligence and contextual reasoning as well as intuition as opposed to mathematical formulae. Artificial intelligence and machine learning techniques also assist with predictive analysis and revealing insights from data.

Data structuring

As data is in an unstructured format while newly collected, it lacks a definite structure. The lack of an existing model causes it to be disorganized and require restructuring to suit the analytical model an enterprise has deployed.

At this stage, the dataset would need to be parsed, as unstructured data contains lots of text in addition to numbers, dates, codes, and more. Parsing allows pertinent information to be extracted from data.

Data structuring may also involve some aspect of feature engineering, where columns or rows may be split to improve accessibility, user-friendliness, and analysis.

Data cleaning

Even though data cleaning is a single step of the wrangling process, it is often a complex process. Raw, unstructured data is often full of errors that need to be remedied before the next stage. A great focus of this stage is the detection and correction of rogue data, such as inaccurate, incomplete, redundant, and wrongly formatted data.

The data cleaning stage prevents skewed results from developing during analysis by removing outliers in the dataset. Cleaning the dataset also improves the data quality and consistency by changing null values and standardizing the data format.

Additionally, to make data easier to handle, cleaning corrects typos and structural errors, fixes duplicates, and standardizes systems of measurement. Algorithms can be applied to clean data using Python and R.

See the Top Data Quality Tools & Software

Data enriching

At this stage, you are required to evaluate what is in the data to determine whether to upscale or downsample data or carry out data augmentation. Although, this step is only necessary if the data falls short of specified requirements.

The goal of data enrichment is to improve or increase the accuracy of data analysis, which can be achieved by combining your data with supplementary data from various sources to assemble more data points. For example, lead enrichment can help fill gaps in your data about your customers.

Data validating

Data validation involves taking iterative programming steps to verify the quality, accuracy, authenticity, security, and consistency of data after processing. Any data quality issues are serviced to allow them to be addressed by the correct transformations.

For instance, you may have to check whether attributes are normally distributed in the dataset or whether fields in the dataset are precise through cross-checking data. Comparison of the attributes of data with defined rules is handled by pre-programmed scripts.

Data publishing

This step signals that data is ready for analytics, as the data is already wrangled. Data should be made accessible to end users like data engineers, data analysts, and data scientists by depositing it into a new database or architecture.

End users can use this published data to generate further insights, power business intelligence, machine learning, and more, opening up endless possibilities for deriving value from data.

Choosing Data Wrangling Tools

Your choice of data wrangling tools will be dependent on your data processing and organizing needs. It will also depend on your cleaning and consolidation requirements and your approach to getting insights from data.

While Excel spreadsheets are the most basic structuring tool for data wrangling, there are a variety of tools, which offer more sophistication and the ability to extract data from PDFs to be able to work with CSV. Moreover, some operational features of the Python and R languages enable data wrangling.

So, before making a decision on which data wrangling tool you will use, be sure to determine what features you need and how different tools compare.

Read next: Best Data Analytics Tools for Analyzing & Presenting Data

The post Best Approaches to Wrangling Unstructured Data appeared first on IT Business Edge.

Why is Data Quality Important?

The success of many businesses today is impacted by the quality of their data, from data collection to analytics. As such, it is important for data to be available in a form that is fit for use to ensure a business is competitive.

Quality data produces insights that can be trusted, reducing the waste of organizational resources and, therefore, impacting the efficiency and profitability of an organization. Maintaining high data quality standards also helps organizations satisfy different local and international regulatory requirements.

How do Data Quality Tools Work?

Data quality tools analyze information to identify obsolete, ambiguous, incomplete, incorrect, or wrongly formatted data. They profile data and then correct or cleanse data using predetermined guidelines with methods for modification, deletion, appending, and more.

Also read: Data Literacy is Key for Successful Digital Transformation

Best Data Quality Tools & Software

DemandTools

DemandTools is a versatile and secure data quality software platform that allows users to speedily clean and maintain customer relationship management (CRM) data. It also provides users with correct report-ready data that boosts the effectiveness of their revenue operations.

Key Differentiators

• Data Quality Assessment: Through the Asses module, DemandTools helps users recognize the degree of strength or weakness of their data to determine where they should focus remediation efforts. Unactionable, Insufficient, Limited, Acceptable, and Validified are five data quality categories which allow users to understand the overall state of their data.
• Duplicate Management: DemandTools helps its customers to discover, remove, and prevent duplicate records from misleading various teams within the organization, thus complicating their customer journeys. Duplicate management happens through modules such as Dedupe, which cleans up existing duplicates; Convert, which keeps lead queues duplicate-free; and DupeBlocker, which is a Salesforce duplicate blocker.
• Data Migration Management: DemandTools ensures the integrity of data is maintained as it enters and exits Salesforce. It uses modules such as Import, Export, Match, Delete, and Undelete.
• Email Verification: Users can verify email addresses in their CRM to ensure they have an effective line of communication with their customers. And lead and contact email addresses can be verified in bulk.

Con: A majority of the tool is designed around Salesforce.

Pricing: Base pricing begins at $10 per CRM license. You can contact the vendor for a personalized quote.

Openprise

Openprise is a no-code platform that empowers users to automate many sales and marketing processes to reap the value of their revenue operations (RevOps) investments. As a data quality tool, Openprise allows users to cleanse and format data, normalize values, carry out deduplication, segment data, and enrich and unify data.

Key Differentiators

• Openprise Data Cleansing and Automation Engine: Openprise ensures data is usable for users’ key systems through aggregation, enrichment, and transformation of data. Openprise’s focus goes beyond sales systems to offer flexibility to their customers. Integration with users’ marketing and sales systems enables Openprise to push clean data and results to these systems to deliver greater value.
• Openprise Bots: Users can deploy automated bots to monitor and clean data in real time to ensure data is always in the best condition.
• Normalized Field Values: Data is normalized to customers’ specifications to smoothen segmentation and reporting. It standardizes company names, phone numbers, and country and state fields among others.
• Deduplication: Users can dedupe contacts, accounts, and leads. It has prebuilt recipes designed involving best practices users can take advantage of. They can also modify dedupe logic to customize the deduplication process to their needs.

Con: The user interface (UI) can be overwhelming, especially to new users.

Pricing: The Professional package starts at $24K per year for up to 250K records. For the Enterprise package and further pricing information, contact Openprise.

RingLead

RingLead is a cloud-based data orchestration platform that takes in data from many sources to enrich, deduplicate, segment, cleanse, normalize, and route. The processes help to enhance data quality, set off automated workflows, and inform go-to-market actions.

Key Differentiators

• RingLead Cleanse: RingLead Cleanse detects and removes duplicates in users’ data through proprietary duplicate merging technology. Users can clean CRM and marketing automation data through deduplication of people, contacts, leads, etc. RingLead Cleanse can also link people to accounts, normalize data structure, segment data into groups, and get rid of bad data.
• RingLead Enrich: The purpose of RingLead Enrich’s data quality workflow engine is to be the central point of users’ sales and marketing technology stack. Users can configure batch and real-time enrichment into their sales and marketing and data operations workflows. They can also integrate their internal systems and data ingestion processes with third-party data sources, optimizing ROI from third-party data enrichment.
• RingLead Route: Users can achieve validation, enhancement, segmentation, normalization, matching, linking, and routing of new leads, accounts, opportunities, contacts, and more in one flow, making RingLead a fast and accurate lead routing solution.

Con: The UI has a learning curve.

Pricing: Contact RingLead for custom pricing information.

Melissa Data Quality Suite

Melissa Data Quality Suite combines address management and data quality to ensure businesses keep their data clean. Melissa’s data quality tools clean, rectify, and verify names, phone numbers, email addresses, and more at their point of entry.

Key Differentiators

• Address Verification: Users can validate, format, and standardize the addresses of over 240 countries and territories in real time to prevent errors such as spelling mistakes, incorrect postal codes and house numbers, and formatting errors.
• Name Verification: Global Name identifies, genderizes, and parses more than 650K ethnically diverse names using intelligent recognition. It can also differentiate between name formats from different languages and countries and can parse full names, handle name strings, and flag vulgar and fake names.
• Phone Verification: Melissa Global Phone can validate callable phone numbers, determine their accuracy for the region, and verify and correct phone numbers at their point of entry to ensure users populate their databases with correct information. It also ensures the numbers are live and identifies the dominant languages in numbers’ regions.
• Email Verification: To prevent blacklisting and high bounce rates and to improve deliverability and response rates, Melissa Global Email Verification carries out email checks to fix and validate domains, spelling, and syntax. It also tests the SMTP (Simple Mail Transfer Protocol) to globally validate email addresses.

Cons: Address updates could be more frequent, and address validation can be resource-intensive and time-consuming.

Pricing: Base pricing is at $750 per year for 50K address validations. Contact Melissa for a free quote.

Talend

Talend Data Quality ensures trusted data is available in every type of integration, effectively enhancing performance and bettering sales while reducing costs. It enriches and protects data and ensures data is always available.

Key Differentiators

• Intuitive Interface: Talend Data Quality cleans, profiles, and masks data in real time, using machine learning to support recommendations for handling data quality matters. As a result, its interface is intuitive, convenient, and self-service, making it effective for not only technical but also business users.
• Talend Trust Score: The built-in Talend Trust Score provides users with instant, explainable, and actionable evaluations of confidence to separate cleansed datasets from those that need more cleansing.
• Talend Data Quality Service (DQS): With Talend DQS, organizations with limited data quality skills, talent, and resources can implement data quality best practices up to three times as fast as they would have by themselves. Talend DQS is a managed service that helps users constantly monitor and manage their data at scale as well as track and visualize data quality KPIs (key performance indicators).
• Asset Protection and Compliance: To protect personally identifiable information (PII) from unauthorized individuals, Talend Data Quality allows users to selectively share data with trusted users.

Cons: It can be memory-intensive.

Pricing: Contact Talend Sales for more information on pricing.

WinPure Clean & Match

WinPure Clean & Match carries out data cleansing and data matching to improve the accuracy of consumer or business data. This data quality tool features cleaning, deduplicating, and correcting functions ideal for databases, CRMs, mailing lists and spreadsheets among others.

Key Differentiators

• WinPure CleanMatrix: WinPure CleanMatrix gives users an easy yet sophisticated method to carry out numerous data cleaning processes on their data. It is divided into seven parts, with each part responsible for a data cleansing task.
• One-Click Data Cleaning Mode: Clean & Match has a one-click data cleaning feature that processes all the clean options across various columns simultaneously.
• Data Profiling Tool: The data profiling tool scans each data list and gives more than 30 statistics. It uses red and amber to highlight potential data quality issues like dots, hyphens, and leading or trailing spaces. These issues can be fixed with a single click.

Cons: It has a learning curve.

Pricing: It features a free version, but base pricing starts at $999 per license for one desktop for the Small Business package. For Pro Business and Enterprise packages, contact the vendor.

How the Data Quality Tools Compare

Choosing a Data Quality Tool

Before selecting a data quality tool for your use case, it is important to consider your data challenges. Implementing a solution that partly or barely addresses your data challenges results in ineffective data management initiatives and impacts overall business success.

It is also important to understand the scope and limits of data quality tools to ensure they are effective. You should also consider the differentiators and weaknesses of the tools in consideration and align them with your goals. Finally, use free trials and demos where available for a hands-on experience.

Read next: Top Data Mining Tools for Enterprise

The post Top Data Quality Tools & Software 2022 appeared first on IT Business Edge.

5G and Edge Computing

5G and edge computing are technologies that can capitalize on a symbiotic relationship to empower a new generation of smart devices and applications. Through its increased performance, 5G can enhance edge computing applications by reducing latency, bettering application response times, and improving the ability of enterprises to collect and process data.

The number of edge devices increases every day, with their capabilities continuously evolving. Internet of Things (IoT) devices are also becoming more sophisticated, as they can collect more types of data. The data generated by these devices fuels the need for actionable insights to help enterprises stay atop of trends, forecast new products and services, and create a competitive advantage.

Human beings generate more than 2.5 exabytes of data daily. Imagine remotely sending approximately 1.7 megabytes per second for each person on earth to be processed centrally.

This would result in strained network resources, which yields performance degradation due to latency, roundtrip delays, and poor use of bandwidth. This data deluge, the struggles of moving it, and the inefficiencies of remote data processing reinforce the need for 5G and edge computing to be leveraged together.

Additionally, more responsibility is being placed on edge devices as the COVID-19 pandemic brought about a shift to traditional workforce patterns. And with the ever-increasing quality of edge computing use cases and the data requirements these implementations have, a shorter control loop is necessary to satisfy the need for near real-time responsiveness.

As such, 5G is a network infrastructure that can support and enable the increasing complexity and specialization of edge computing.

Also read: Best Enterprise 5G Network Providers 2022

Benefits of the Relationship Between 5G and Edge Computing

Ultra-low latency use cases

Combining 5G and edge computing is critical in attaining ultra-low latency in various edge devices and use cases.

Considering the increasing need for high reliability and ultra-low latency communications for use cases in smart factories, healthcare, intelligent transportation, smart grids, and entertainment and media among others, pairing 5G and edge computing enables such ultra-low latency applications to reach their full effectiveness.

Near real-time performance

Leveraging the combination of 5G and edge computing helps enterprises collect and process massive volumes of real-time data to optimize various operational systems and improve productivity and customer experiences. Enterprises can process and analyze data in the environments that yield the most value.

Carrying out processing and analysis close to where data was created brings enterprises close to near real-time performance for mission-critical applications.

Improved bandwidth usage

The relationship between 5G and edge computing impacts the success of 5G network technology. Edge computing helps ensure 5G is feasible when dealing with millions of devices connected to a 5G network.

In the absence of edge computing, all these devices would be transmitting data directly to the cloud. This would, in turn, push the bandwidth requirement for transmission to the cloud to an overwhelming level and counter the effectiveness of a 5G network.

Advancement of emerging technologies

High-speed connectivity coupled with data processing at the edge is critical for the advancement of technologies, such as artificial intelligence (AI), machine learning (ML), augmented reality (AR), and virtual reality (VR). The advancement of these technologies is important, as they have the potential to revolutionize entire industries and enable boundless innovation as entirely new applications are made feasible.

Specifically, by moving compute closer to data, 5G and edge computing improve the ability to innovate, as this opens up the ability to infuse AI and machine learning into edge solutions, which opens up new possibilities for use cases and business models. It also opens up the possibilities for IoT solutions.

The pairing of 5G and edge computing and their impact on AI, ML, and IoT makes smart cities more feasible and provides a foundation to innovate further as compute and network challenges are minimized.

Another example of an area where limited deployment has the potential to be erased by 5G and edge computing is telemedicine. Industrial automation also stands to benefit from much more effective and creative solutions. Manufacturing could finally realize a truly intelligent and integrated supply chain to improve efficiency.

Also read: 5G and AI: Ushering in New Tech Innovation

Potential Drawbacks

• Greater attack surface: As 5G edge use cases become more ubiquitous, the attack surface becomes larger. This is seen as worthwhile to threat actors, as the likelihood of a successful attack is increased.
• Complexity: Enterprises may be drawn to the use cases of the technologies but fail to grasp the regulatory requirements; financial implications; and potential technical issues, such as massive scale, rate of change, and variability.
• Modular Ecosystem: The connectivity ecosystem proves to be challenging to navigate due to its increasingly modular nature. As such, there are many solutions to consider with different costs and varying levels of performance and control.

Enterprise Use Cases

Augmented reality and virtual reality

Enterprise consumers can enjoy more immersive real-time collaboration, as employees in different locations can collaborate on and manipulate the same virtual objects. Smart glasses can also help revolutionize maintenance, repairs, and operations as well as relay instructions to employees using AR to help them correctly carry out tasks.

AR and VR headsets can be used to train new employees. They can learn how to carry out various roles and tasks with minimal errors. 5G edge enables AR and VR in sales and marketing, allowing prospective clients to enjoy immersive virtual previews of products and services. For example, users can enjoy virtual tours of real-world properties or locations as well as virtually try out fashion and cosmetic products.

Manufacturing

Edge computing and 5G combine to improve oil and gas, food and beverage, and consumer goods manufacturing. Edge computing can be implemented at distribution and remote pumping sites. These sites can be connected to a main autonomous system using 5G. Infrastructure can also be upgraded to ensure these sites can handle 5G data requirements.

Another edge computing and 5G deployment involves monitoring environmental controls of food and beverage items in transit to maintain the quality of perishable products. Centralized production analytics can be replaced with distributed edge systems in consumer goods manufacturing. These edge systems can use a private network to connect to supply partners.

Moving Forward with 5G and Edge Computing

Enterprises can start by understanding the value and implications of 5G and edge computing from a technological as well as business perspective. They can then identify challenges or opportunities that 5G and edge computing can help them overcome or capitalize on.

At this point, developing a 5G and edge computing strategy will help ensure the intended use cases are aligned not only with the enterprise but also with the technologies. This also helps the enterprises to effectively implement use cases and make sure the technology is naturally evolving with the implementation.

Read next: Edge AI: The Future of Artificial Intelligence and Edge Computing

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Is a Cybersecurity Certification Worth It?

The rise in the sophistication of cyber crime and data breaches has raised the stakes for organizations, as it is apparent that it is expensive to not only recover from but also react to cyberattacks. As organizations seek to stay a step ahead of threat actors and prevent cyberattacks, the value of cybersecurity professionals has been on the rise.

As a result, there is surging demand for cybersecurity talent today as enterprises prioritize cybersecurity. This skill gap makes cybersecurity a profitable career field and makes cybersecurity certification worth it.

To kickstart your cybersecurity journey, as well as re-skill or upskill, here are the top cybersecurity certifications to consider in 2022.

Also read: Best Cybersecurity Training & Courses for Employees 2022

Top Cybersecurity Certifications

Certified Information Systems Security Professional (CISSP)

As one of the most in-demand credentials in the industry, the CISSP certification is proof that you can successfully design, implement, and manage top-quality cybersecurity programs.

The domains covered by the CISSP exam include security and risk management, software development security, asset security, security operations, security architecture and engineering, security assessment and testing, communication and network security, and identity and access management.

The CISSP certification is provided by (ISC)², which is an international non-profit cybersecurity organization that validates your expertise and provides (ISC)² membership, which comes with exclusive resources, tools, and networking opportunities.

Cost: The exam registration fee is $749 USD. Additionally, certified members pay an annual maintenance fee of $125 USD during the anniversary of their certification date.

Duration: The exam requires at least five years of amassed work experience in at least two of the domains mentioned earlier. However, the training time required to pass the examination depends on the method chosen.

The classroom-based training lasts five days, while self-paced learning can take up to two to three months with four to five hours of study a day. The training duration of self-paced learning is influenced by the candidate’s experience, learning ability, and schedule.

To prepare for the Certified Information Systems Security Professional exam, check out these courses!

Potential opportunities

Such an advanced certification can help you qualify for roles such as:

• Chief information security officer
• Chief information officer
• Director of security
• Security architect
• Senior security consultant

Certified Ethical Hacker (CEH)

The Certified Ethical Hacking certification, provided by the EC-Council, shows that professionals are equipped with skills to legally and safely bypass an organization’s security systems to expose vulnerabilities before threat actors do. It teaches up-to-date commercial-grade hacking techniques, tools, and methodologies used by hackers and security professionals.

Cost: There is a non-refundable $100 USD application fee along with a fee of either $950 USD if taking the exam remotely or $1,199 USD for an exam through a Pearson VUE testing center.

Duration: The standard training course should be at least a 40-hour program over five days, and the exam is four hours with 125 questions.

Potential opportunities

The certification can launch you into jobs like:

• Penetration testing
• Cybersecurity engineering
• Threat intelligence analysis
• Cybersecurity architect

To prepare for the Certified Ethical Hacker (CEH) exam, check out these courses!

Also read: Top 10 Cybersecurity Jobs for 2022

Certified Information Systems Auditor (CISA)

CISA is a foundational certification for entry-level and mid-career professionals seeking to have a successful IT career. The certification validates your expertise and ability to implement risk-based approaches to planning, executing, and reporting on audit duties. It also backs up your competency in implementing privacy by design into technology, processes, and products to cost-effectively align with organizational goals and needs.

The certification is provided by ISACA, a global IT association, which requires five or more years in IS/IT assurance, audit, security, or control.

Cost: The exam costs $760 USD for non-members and $575 USD for members.

Duration: Since an exam’s eligibility period is up to 12 months after scheduling, the duration of training is flexible enough to be based on your goals and schedule. Although, the training program can take only eight days to complete the course.

To prepare for the Certified Information Systems Auditor (CISA) exam, check out these courses!

Potential opportunities

The CISA certification can help you in careers such as:

• Information security analyst
• IT security engineer
• IT audit manager
• Risk-based audit planning
• Cybersecurity auditor

GIAC Security Essentials Certification (GSEC)

The GSEC credential, provided by Global Information Assurance Certification (GIAC), goes beyond validating a practitioner’s competency of information security through a grasp of concepts and standard terminology. The certification endorses the competency of a practitioner with regards to hands-on roles in IT systems, with a specific focus on security tasks.

It covers areas such as penetration testing, vulnerability scanning, cryptography, Linux and Windows security, web communication, virtualization, cloud, and endpoint security among others.

Cost: A fee of $2,499 USD covers the exam as well as a pair of practice tests.

Duration: The exam has between 106 to 180 questions and has a time limit between four to five hours. It is web-based and administered either on-site or remotely through a proctored environment. In-person, training can take as little as six days.

To prepare for the GIAC Security Essentials Certification (GSEC) exam, check out these courses!

Potential opportunities

The GSEC certification is useful for positions like:

• Security administrator
• Penetration tester
• Forensic analyst
• Security manager
• Software development engineer

Also read: Edge Computing Emerges as Next Big Cybersecurity Challenge

Systems Security Certified Practitioner (SSCP)

SSCP, offered by (ISC)², is a globally-recognized certification in advanced security and operations that proves practitioners’ knowledge and skills in the monitoring, implementation, and administration of IT infrastructure through policies, procedures, and best practices. It is perfect for managers, directors, IT admins, and professionals with hands-on operational security roles such as network security professionals.

As an advanced certification, the course requires at least a year of paid work experience in at least one of the SSCP domains.

Cost: The cost of the exam is $249 USD.

Duration: Classroom-based training takes five days, and the exam has 125 multiple choice questions with a three-hour time limit.

To prepare for the Systems Security Certified Practitioner (SSCP) exam, check out these courses!

Potential opportunities

The SSCP certification is great for candidates seeking careers in:

• Threat intelligence analyst
• Security engineer
• DevOps engineer
• Network analyst
• Systems administrator

Security+

The CompTIA Security+ certification, provided by CompTIA, is globally recognized for verifying that a practitioner has the baseline skills required to carry out core security functions and add value in an IT security environment.

Security+ is a popular certification that places emphasis on hands-on practical skills to make sure the security professional is equipped to solve numerous security problems. It also keeps up with the latest trends and techniques to ensure professionals are supplemented with up-to-date skills.

Cost: The CompTIA Security+ exam voucher costs $381 USD.

Duration: The exam has a maximum of 90 questions, which are either multiple-choice or performance-based. Online learning for this course exceeds 40 hours’ worth of content.

To prepare for the CompTIA Security+ certification, check out these courses!

Potential opportunities

The Security+ certification is great for positions such as:

• Security administrator
• Systems administrator
• Network engineer
• Cloud engineer
• IT project manager

CompTIA Advanced Security Practitioner (CASP+)

CASP+, provided by CompTIA, targets senior security engineers and security architects responsible for spearheading and improving the preparedness of their enterprises’ cybersecurity.

It is an advanced-level certification that encompasses both security engineering and architecture. And it equips professionals with the ability to determine how to implement solutions within agreed-upon cybersecurity policies and frameworks.

Cost: The exam voucher costs $480 USD.

Duration: The self-paced online learning module has 20 lessons with performance-based questions, and the exam has a 165-minute time limit with 90 questions.

To prepare for the CompTIA Advanced Security Practitioner (CASP+) certification, check out these courses!

Potential opportunities

Those with a CASP+ certificate can excel in careers like:

• Senior security engineer
• Security architect
• Security analyst
• Vulnerability analyst

Certified Information Security Manager (CISM)

The CISM credential, offered by ISACA, is ideal for those who want certified expertise in program development and management, incident and risk management, and information security governance.

It is a certification targeting professionals with competence and a minimum of five years of experience in information systems/information technology security and control who seek to transition to managerial roles.

Cost: The exam costs $575 USD for ISACA members and $760 USD for non-members.

Duration: The time commitment involved is dependent on the applicant’s training method and their level of experience. The examination is four hours long with 150 multiple-choice questions.

To prepare for the Certified Information Security Manager (CISM) certification, check out these courses!

Potential opportunities

The CISM certification is ideal for those seeking work as a:

• Director of information security
• Information risk consultant
• IT manager
• Data governance manager

Read next: Cybersecurity Awareness for Employees: Best Practices

The post Best Cybersecurity Certifications of 2022 appeared first on IT Business Edge.

What is Cyber Security Awareness?

Cybersecurity awareness goes beyond just having knowledge of cyber threats to also involve training, policies, actions, and habits that improve the security posture and culture in an organization. It involves being aware of cyber security best practices and proactive approaches to handle cyberattacks. Cybersecurity awareness also includes clearly understanding the impact of digital attacks on an individual, employees, an organization, and customers.

Why is Cybersecurity Training Important?

Plenty of cyberattacks have a social engineering phishing angle. Employees are often exposed to social engineering attacks such as phishing, and without the right cybersecurity awareness, they may be unaware of not only how to deal with them, but also unaware of the threat entirely.

As such, reducing such attacks ultimately by mitigating human error reduces the risk of being breached as data breaches are costly. Cybersecurity awareness training empowers staff throughout an organization to not only identify various threats but also handle them to add a layer of protection to the organization and improve their security hygiene and culture.

Also read: Cybersecurity Awareness for Employees: Best Practices

Best Cyber Security Training & Courses

ESET

ESET Cybersecurity Awareness Training adds a layer of security to your business by educating employees to steer clear of online scams, recognize phishing and apply internet best practices. It is a 90-minute course with interactive sessions and gamified quizzes to keep trainees engaged.

Key Differentiators

• 90-minute gamified course. ESET Cybersecurity Awareness Training provides a 90-minute gamified course that has trainees select their character and take upon the role of a virtual IT technician. The virtual technician assists team members to answer cybersecurity questions.
• Video-format learning. To ensure the training is not only engaging but also fun, ESET Cybersecurity Awareness Training presents learning content in video format. Additionally, mobile-friendly content enables employees to learn on the go.
• Easy-to-use phishing simulator. This security awareness training platform presents trainees with more than 150 phishing emails that are ready for use. The templates provide greater efficiency than designing phishing emails from scratch.
• Engaging minigames. The ESET Cybersecurity Awareness Training has trainees play captivating minigames as opposed to having users come up with answers to easy quizzes. Characters are accorded badges and reputation points, which may challenge employees to resit classes to attain better scores.

Pricing: Starts at $250 USD per 10 employees.

CybSafe

CybSafe Certified Skills Awareness Training is a brief and interactive course with threat-based modules and content specific to not only discipline but also role. The learning tracks go beyond company compliance requirements by providing modules that contribute to trainees’ professional development.

Key Differentiators

• Applied machine learning. CybSafe uses applied machine learning to understand the best approach to learning for trainees. IT helps understand how a trainee learns best. Applied machine learning also determines what keeps trainees engaged and at what times they are most engaged, to improve the value of the course.
• Tailored learning experience. CybSafe offers trainees the chance to fully explore their areas of strength through a tailored learning experience.
• Data-driven personalization. Going beyond a tailored learning experience, CybSafe leverages data to understand and adapt to a trainee’s learning needs to improve their security behaviors.

Pricing: Awareness training is part of all the CybSafe packages, with a free version available in the Starter package.

Living Security

Living Security is a comprehensive Human Risk Management solution that aims to turn human risk into proactive defense in a business. With Living Security, you can identify and quantify the highest risk in departments, regions, and people then use fun, engaging, and innovative up-to-date content to transform them into strengths.

Key Differentiators

• Microlearning modules. Living Security’s learning modules maintain engagement among trainees through brief learning modules. These modules are between 1 to 5 minutes long, with a to-the-point approach. Some of the topics covered include remote work, data classification, password hygiene, and phishing.
• Immersive training series. Living Security empowers you with game-based training experiences to engage and train teams across the enterprise. With an enterprise-wide leaderboard, you can list rewards and keep track of competitions and employee scores, regardless of where your teams are located.
• Enterprise Phishing Simulator. You can proactively and safely examine and quantify human vulnerability by sending harmless phishing attacks to your staff.

Pricing: A demo is available upon request but you shall have to contact Living Security for specific pricing information.

Cofense

Cofense offers an expansive online security awareness training for employees, for free, to empower staff to improve their security practices. This allows them to protect themselves, their devices, and their organizations from cyberthreats. The free training modules are available for download regardless of whether you are a Cofense user or not. Cofense also offers more targeted, premium training and tools to deal with threats like phishing.

Key Differentiators

• Cofense PhishMe. Cofense PhishMe teaches users about actual phishing threats facing their enterprises. It takes an experiential learning approach to instill more intelligent email behavior, transforming vulnerability into strength.
• Customizable phishing awareness training program. Users can configure a full-year training program with a handful of clicks through Cofense’s Smart Suggest capability. Smart Suggest uses advanced algorithms and best practices to suggest scenarios.
• Microlearning modules. CBFree Microlearning is characterized by short interactive modules with questions at the end.

Pricing: There is a complimentary plan using CBFree. Cofense Vision and PhishMe require you to message the vendor for a quote. However, PhishMe offers a demo.

KnowBe4

KnowBe4 offers an enterprise awareness training program with a diversified and modern approach that integrates gripping interactive web-based training, baseline testing with mock attacks as well as constant assessment through simulated phishing, smishing, and vishing attacks.

Key Differentiators

• Integrated platform. An easy-to-use graphical user interface displays all of KnowBe4’s integrated functions to allow you to carry out simulated attacks and kickstart training campaigns in minutes. It also has completely customizable templates, simulated attachments, and landing pages.
• Random attack delivery. KnowBe4 allows you to choose from more than 10,000 believable phishing spread over a typical workday. Each employee gets a different phishing email at a different time. The “Current Events” template ensures phishing messages are up to date.
• Advanced enterprise reporting. KnowBe4’s reporting capabilities offer visibility into the whole enterprise’s security awareness performance. The enterprise-level reporting provides insights into training and phishing simulation data spanning over a specified duration.
• Risk scoring. The Virtual Risk Officer feature helps users identify risk at various levels and implement their security awareness plan using data-driven decisions.

Pricing: For a quote, contact KnowBe4.

NINJIO

NINJIO uses engaging, less than five-minute Hollywood-style micro-learning videos in their cybersecurity awareness training solution to enable organizations, staff, families, and individuals to fight back against cyber threats. NINJIO teaches people how to prevent and avoid being hacked thus strengthening the first line of defense against cybercrime.

Key Differentiators

• Animated episodes. NINJIO releases short microlearning episodes every month to keep users at par with the cybersecurity landscape. These episodes educate users on the newest threats, breaches, and incidents.
• Hollywood writing. NINJIO’s episodes are kept engaging by a writer who belongs to the Writers Guild of America and has experience producing and writing tens of episodes for popular TV content.
• Gamified content. NINJIO introduces an aspect of competition to improve user engagement. A leaderboard encourages employees to quickly complete episodes and pass quizzes with minimal retries as they aim to earn more points and secure top spot.
• Friends and family use rights. A NINJIO subscription allows you to add seven friends and family members as recipients of NINJIO episodes at no extra cost.

Pricing: For pricing information, contact NINJIO.

Proofpoint

Proofpoint provides a distinctive people-centric approach to security awareness training to greatly reduce malware infections and phishing attacks. It turns users into a robust line of defense against various cyberattacks. Proofpoint uses targeted education to ensure users know how to react in the face of real threats.

Key Differentiators

• Targeted education driven by threat intelligence. Proofpoint offers fully customizable content that uses Learning Science Principles to keep learners engaged and influence their behavior. The content includes video, interactive, and game-based training modules.
• Phishing simulations. Using a people-centric approach, Proofpoint provides insights to understand who is susceptible to phishing attacks as well as who is vulnerable in other crucial security awareness domains. Through Very Attacked People (VAP) reports in addition to simulated attacks, the effectiveness of the awareness program is improved.
• PhishAlarm. Empower staff to flag suspicious email content with Proofpoint’s PhishAlarm Email Add-In. Proofpoint threat intelligence analyzes messages as attachments and URLs are sandboxed. Admins have the option to automatically remediate the messages and provide customized feedback to the user who identified the email.

Pricing: Proofpoint has a demo and a free trial. Further pricing information is only available through contacting Proofpoint.

Hook Security

Hook Security allows users to carry out a fully automated security awareness training program that entertains staff, mitigates risk, and improves security hygiene throughout the company. Users can run automated phishing tests and receive actionable reports to better the security awareness of a company.

Key Differentiators

• Automated phishing testing. With Hook Security, you can deploy a phishing test campaign in minutes. Monthly automated phishing simulations provide valuable education and training to vulnerable staff.
• Instant Training Moments. Instant Training Moments help employees immediately learn from the phishing tests they fell victim to.
• Real-time reporting and insights. Hook Security’s security awareness training allows users to monitor their progress in real-time through real-time reporting. They automatically receive monthly reports, enjoy real-time reporting dashboards, and can track their course completions.
• Easy to launch and automate. You can set up security awareness programs in minutes and extend extra training to employees who require it the most. Hook Security offers automated course enrollments, course reminders, and reports.

Pricing: Hook Security offers both a free trial and a demo. For organizations that need phishing simulations only, Hook Security charges $1.10 USD per user per month. The Standard package costs $1.60 USD per user per month for organizations seeking an introduction to security awareness training. Hook + charges $2 USD per user per month for organizations seeking to establish an exhaustive security-aware culture.

How Cybersecurity Awareness Training Solutions Compare

The Right Training for You

To choose the training solution that is right for your team, you should evaluate your cybersecurity awareness needs. It is also crucial to consider the ability levels of your team to avoid struggling to get everyone up to speed as a result of implementing the wrong training solution. The ideal solution should steer clear of jargon and focus on key training areas. Finally, you should also consider prioritizing usable and enjoyable training for your users to better the effectiveness of training.

Read next: Cybersecurity Awareness for Employees: Best Practices

The post Best Cybersecurity Training & Courses for Employees 2022 appeared first on IT Business Edge.

Cybersecurity is the practice of securing critical systems and data from digital attacks. Its strategies offer protection by accounting for potential threats from both within and without an organization. A few examples of domains addressed by cybersecurity include network security, critical infrastructure security, cloud security, application security, and information security.

What do Cybersecurity Companies Provide?

Cybersecurity companies provide a wide variety of software and services today. Below are some examples:

• Security information and event management (SIEM): SIEM describes a group of tools and services used to discover cybersecurity attacks as well as data breaches.
• Endpoint detection and response (EDR): EDR solutions seek to provide next-generation endpoint security by combining layers of threat detection, prevention, and response in one solution.
• Managed detection and response (MDR): Cybersecurity companies offer MDR as security as a service since it seeks to empower organizations to expand or replace their in-house security teams. MDR offers expertise and tools that organizations need to be safe against cyber threats.
• IDS and IPS tools: Intrusion detection systems (IDS) detect malicious activity and send alerts but are incapable of attack prevention. However, intrusion prevention systems (IPS) respond to specific attacks by impeding traffic and malicious activities.
• Antivirus: To help users detect, prevent, and remove malware, some cybersecurity companies offer antivirus software.
• Cloud security: Cybersecurity companies help businesses ensure their data is safe and ever available as more and more organizations take up the cloud.
• Threat intelligence: Using state-of-the-art tools and methods, some cybersecurity companies gather global data on potential threats, inclusive of current and emerging threats and threat actors.
• Application security: Cybersecurity companies can help development teams enjoy a secure software development lifecycle. This transforms DevOps (development and operations) and DevSecOps (development, security, and operations) teams and processes to ensure applications are built and deployed securely.

Also read: Is Cybersecurity Insurance Worth It?

Top Cybersecurity Companies

Cybriant

Cybriant provides an in-depth and customizable collection of managed cybersecurity services. With a team of security experts, Cybriant offers MDR, managed SIEM with live monitoring, mobile threat defense, comprehensive vulnerability management, and application security among other services.

Key Differentiators

• SIEM customization: Cybriant’s security experts can take responsibility for the configuration of users’ SIEM to abolish the need for skilled in-house security teams. They can tune users’ SIEM to generate actionable insights for threat detection and incident response.
• SIEM optimization: As a key part of any resilient cybersecurity plan, SIEM has to remain relevant. Cybriant provides the expertise to enact constant maintenance and correct implementation of your SIEM.
• 24/7 analysis and alerts: Cybriant provides continuous monitoring to improve users’ mean-time-to-detect (MTTD), mean-time-to-respond (MTTR), and to minimize data breaches. The company also offers ticketing and alerting features to satisfy standard log data review needs.
• Easy deployment of log collection infrastructure: Through the use of a SIEM, Cybriant offers users tools to simply and quickly deploy log collection infrastructure that supports regulatory requirements. This allows users to keep an activity audit trail to satisfy compliance mandates.

Con: Cybriant has a steep learning curve for beginners.

Pricing: You would have to contact Cybriant for current pricing information, as it is not publicly available. However, Cybriant offers a live demo.

Check Point Services

Check Point is the largest network cyber security vendor worldwide. Check Point’s ThreatCloud Managed Security Service combines cutting-edge threat prevention technology with adept threat analysis to continuously protect users from cyberattacks. Apart from award-winning technology, ThreatCloud’s collaborative intelligence helps users update their protections.

Key Differentiators

• Check Point anti-bot technology: Check Point’s anti-bot technology uses a unique multi-tier engine to discover infected hosts in a network. It identifies bot outbreaks through up-to-date bot intelligence from the ThreatCloud knowledgebase, and it prevents harm by halting bot communication between the botnet’s command center and infected hosts.
• Managed security service web portal: The web portal provides users with numerous informative views of their gateway, event, and alert activities in addition to ThreatCloud’s real-time security intelligence.
• ThreatCloud real-time security intelligence feeds: ThreatCloud gives real-time dynamic security intelligence to discover emerging threats patterns and outbreaks. This offers users an overall perspective of current threats.
• Periodic policy tuning: Check Point security experts occasionally modify users’ gateway protection policies to optimize their security and throughput performance. The modifications can be carried out quarterly or annually, depending on the user’s service level.

Con: ThreatCloud lacks sufficient documentation and efficient support.

Pricing: Check Point’s ThreatCloud Managed Security Service offers a free demo. Contact the vendor for specific pricing information.

McAfee Security Services

McAfee Enterprise is an industry-leading cloud security solution that allows users to secure any cloud and protect their data anywhere and anytime. McAfee Security Services help users optimize McAfee’s advanced security solutions to ensure their technologies are deployed successfully and their security posture is stronger. These advanced security solutions are built to detect, correct, and protect as well as adapt from devices to the cloud.

Key Differentiators

• Risk and compliance: McAfee Advanced Cyber Threat Services help users meet their strategic business goals and establish the most effective ways to protect their assets using information all through the information security lifecycle.
• Secure DevOps: McAfee Advanced Cyber Threat Services uncover vulnerabilities in users’ infrastructure and secure areas that present the greatest risk to their businesses. They also use threat modeling to discover software security issues in the early stages of development.
• Security Operations (SecOps): These services give users the ability to assess the maturity of their security operations regarding technologies, processes, and tools. Users can pinpoint operational challenges in their environments.
• Incident response: McAfee Security Services offer expertise to respond to and thwart complex breaches during an emergency. These experts help users mitigate the risk of cyberattacks.

Cons: Uninstalling the software is cumbersome. The constant alerts and pop-ups make McAfee software an annoyance.

Pricing: There are a handful of free security tools offered, but specific pricing information can only be obtained by contacting the vendor.

Sophos

Sophos offers high-quality IT and data protection for businesses. Today Sophos products serve more than 100,000 businesses and 100 million users. Through Sophos Intercept X with XDR, Sophos not only supports endpoint protection but also threat investigation across firewalls, servers, and more data sources.

Key Differentiators

• Deep learning technology: Sophos uses artificial intelligence to detect known and unknown malware without reliance on signatures.
• Customized solutions: Sophos Professional Services provides enterprises with experts who can leverage Sophos APIs to build custom solutions to automate tedious tasks and who can carry out custom reporting to complement the data analysis needs of an enterprise.
• Knowledge transfer: IT staff receive assistance from Sophos professionals on how to better their organizations’ security posture and minimize human error that may result in compromised security.
• XDR and XDR training: Sophos goes past the endpoint to offer extended detection and response (XDR) to provide greater visibility. Sophos also provides XDR training to equip users to detect and remediate suspicious activity in real-world scenarios.
• Configuration review and health check: Sophos reviews existing implementation of Sophos products and guides users on how to maximize their investment in Sophos security solutions. This ensures an organization is correctly protected against threats.

Con: Sophos software can be resource-intensive.

Pricing: Contact the vendor for current pricing information.

Cisco Security Services

Cisco Security Services provides a portfolio of authentic products and solutions created to work together at scale. The Cisco Secure portfolio allows users to effortlessly deploy security everywhere and effectively respond to ever-evolving threats. Cisco Secure offers solutions for data centers, cloud, networking, and collaboration.

Key Differentiators

• Secure Endpoint: Cisco Secure Endpoint is a cloud-native solution that provides users with resilient protection, detection, and threat response to reduce remediation times by up to 85%.
• Identity Service Engine (ISE): Cisco ISE simplifies the administration of highly secure network access control. It offers users increased user experience, as it depends on device access levels.
• Secure Workload: Cisco Secure Workload offers users workload protection across any cloud or application. It helps users proactively discover and remediate potential threats to mitigate the impact of the organization being compromised.
• Vulnerability management: Cisco, through Kenna Security, provides modern risk-based vulnerability management that carries out extensive threat intelligence and prioritization to lower costs and improves the efficiency and productivity of risk management teams.

Con: Setup and support may prove to be a challenge for non-technical users.

Pricing: Contact Cisco Security Services for more information.

Rapid7 Security Services

Rapid7 technology and solutions help organizations and security practitioners maneuver through the complexities of cybersecurity to ensure their security posture is future-ready. Rapid7 Security Services offers security consulting supported by security experts and industry leaders to fast-track security improvement through leading methodologies.

Key Differentiators

• Penetration Testing Services: Rapid7’s Penetration Testing Services team shows the security posture of organizations and their infrastructure by simulating real-world attacks on organizations. Organizations get to understand their vulnerabilities and what to do to cover gaps.
• Security Advisory Services: Rapid7 Advisory Services assist users to purposefully improve their security posture, as these services help to prioritize security initiatives and align them with business goals promptly.
• Incident Response Services: From incident investigation to containment and recovery, Rapid7’s Incident Response Services provide the expertise to accelerate these processes. Rapid7’s teams consult with in-house as well as outsourced teams through all stages of incident response.
• Internet of Things (IoT) Security Testing Services: Rapid7 has qualified and experienced consultants to help users identify vulnerabilities and risk in IoT deployments or new IoT products. These consultants assist organizations to implement solutions to mitigate security problems across their IoT ecosystems.

Con: Scanned devices have to be manually removed.

Pricing: Contact Rapid7 for a quote.

Choosing a Cybersecurity Company

The right cybersecurity company for you is first determined by the cybersecurity domains applicable to your use case. For example, getting to know whether you or your organization needs application security or storage security helps you avoid subscribing to solutions that are ineffective to you.

From this point, you can consider the cost and features that are aligned with your use case and budget. Consider demos and free trials wherever available, and maintain basic cybersecurity best practices for the best results.

Read next: Top Risk Management Tools & Software

The post Top Cybersecurity Companies & Service Providers 2022 appeared first on IT Business Edge.

Log Management and DevOps

Logging in today’s DevOps landscape has become more difficult than ever. DevOps needs visibility into workloads that security, development, and operations teams collaboratively manage and provide. The emergence of cloud-based services, as well as the rapid maturity of microservices-based architecture, ensure that alongside legacy systems, DevOps teams have more systems to manage.

More logs are produced, hence more log data is generated. DevOps teams are required to deal with logging structures with greater variation. As such, these teams require approaches to upholding visibility and security that can keep up with fast-paced and greatly dynamic environments.

Modern log management solutions uphold DevOps culture by going beyond being able to ingest, query, provide alerts on, and visualize data to also provide collaborative functionality. Log management allows developers to monitor and observe the behavior of applications prior to their release to production in continuous integration and continuous development (CI/CD) pipelines. This allows developers to catch preventable issues before deployment and production.

As a result, occasions where development teams would need to constantly troubleshoot or redesign their solutions while in production are reduced. This creates a polished and more seamless user experience and develops a culture of building more user-friendly products and services.

Also read: Re-Architecting Log Management for DevOps

Uses of Log Management in DevOps

Driving automation

Modern log management systems go beyond just using logs to generate insights into the performance of applications and services they support. They automatically aggregate, index, and analyze logs to provide insights and present them to CI/CD pipelines and other utilities.

These log management systems also automate code deployment to ensure repeatable steps in this process can be performed without human oversight and to improve DevOps performance.

Leveraging machine learning capabilities

Historical log data can empower DevOps teams to examine user engagements and uncover anomalies. Additionally, machine learning capabilities, like time-series anomaly detection (TSAD), can provide greater visibility for DevOps teams when logging data like application traffic. 

Offering software teams modern performance and analysis capabilities

Today’s software teams require log management solutions that provide performance and analysis capabilities as a base for observability. Such solutions offer DevOps teams a platform supporting an ever-growing, open-source market to ensure their products are part of an ecosystem that future-proofs them.

They also store important log-based metrics to eliminate the need to crunch large volumes of data from scratch to uncover aggregate trends.

Monitoring applications in real-time

In addition to usage in CI/CD, DevOps uses log management to monitor applications in real time to reduce the gap between DevOps and the end users. For instance, mappable data includes customer satisfaction scores.

Software application and infrastructure troubleshooting

The standard use case of log management in DevOps is managing the issues facing the applications and infrastructure through alerts from logs and metrics.

Also read: Effectively Using Low-Code/No-Code in the Developer Cycle

Log Management Benefits

Better operations

Increased complexity of systems and applications ensures the scope and difficulty of operations also increase. Log management equips DevOps teams with the ability to uncover trends across the infrastructure of an enterprise and to anticipate issues. Consequently, they are always equipped to prevent urgent issues that would disrupt processes and to ease operations.

Improved resource usage

As logs offer more metadata to sift through and visualize compared to metrics, you can derive greater actionable insights from them. These insights can inform users on how to better allocate their resources to prevent system overloads and bottlenecks.

Tighter security

Anomalies may be indicators of attacks. Logging allows security teams to gather, analyze, and aggregate log data to improve the effectiveness and comprehensiveness of security information and event management (SIEM) services.

User experience

User experience challenges such as slow response times and lacking a response entirely can be dissected through log management. Log management shows you requests at each level to identify which requests are failing and an understanding of why they are failing.

Also read: Top DevOps Trends to Watch in 2022

Challenges of Log Management

Handling increasingly complex systems

The complexity of today’s systems is amplified by their increasingly distributed nature. Each tool or application generates streams of logs. As much as these logs may provide valuable information, they may also be verbose and redundant.

The redundant information complicates the process of finding valuable information. Furthermore, this challenge is amplified in log management systems with underdeveloped searching and reporting features.

IT burden and automation

Log management carried out manually is tedious, inefficient, and time consuming. The use of automated log management tools eases the burden of IT professionals. However, automated log management tools may also fall short of being an ideal solution, as they work within set parameters.

Therefore, as challenges and threats to data evolve daily, preset parameters may not account for these changes. As such, a dedicated human may still be required to discern what to automate and to take up manually.

Latency

The latency between when data enters a system and when data is available in search results and visualizations can be caused by the high computational cost of indexing within log files. Furthermore, the process of indexing data by the log management system may contribute to increased latency.

Volume

Today’s data explosion is heaping pressure on organizations to successfully capture, store, organize, analyze, and derive value from large volumes of data. This adds complexity to log management, as it is expected to manage large quantities of data while providing accurate and prompt insights.

Implementing Log Management Efficiently

Prioritize automation tools

The soaring distributed nature of today’s systems coupled with their real-time requirements is challenging log management to adopt automated tools to handle various recurring tasks such as data collection. This automation reduces strain on resources in DevOps teams and promotes overall efficiency.

Leveraging the cloud

DevOps teams, and organizations as a whole, should consider cloud-based log management solutions to keep abreast with the dynamic data landscape of today. Teams can enjoy the flexibility and scalability offered by these cloud-based solutions, as they can scale up or down based on their development and operations needs.

Use a centralized system

Centralization of log management improves both data access and security of an organization. Central storage of data improves the efficiency of anomaly detection and response. It also defines access control, as it helps in controlling and managing the visibility of data. For example, it eliminates the exposure of various teams to logs outside their scope of responsibility. This approach also ensures personally identifiable information is only accessed by the correct parties.

Compartmentalize logs

As application development becomes more rapid, containerized applications are becoming more rampant due to their greater reliability and cost-effectiveness. The importance of log management to the development, support, and usage of mission-critical applications means that logs should also be compartmentalized. This provides a consistent experience across all environments and allows DevOps teams to individualize development, staging, and production environments.

Consider log management as a driver

Considering log management as an afterthought may undermine how you leverage log data and ultimately dilute the quality of the business decisions derived from the data. As such, log management should be considered a driver of observability and should be viewed as truly essential to DevOps.

Read next: Best DevOps Monitoring Tools for 2021

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