A cloud data lake solution offers a number of benefits that make it an ideal tool for managing and processing data, including protection of sensitive information, scalability of storage and resources, and automation of data-related processes. We’ll look at the top cloud data lake solutions available in the market and offer some insight into their key features, use cases and pricing.

Benefits of Data Lake Solutions

A data lake provides businesses with a robust data store perfect for pooling various data types, whether structured or unstructured. Data lakes also provide organizations with an optimal system for processing and analyzing their information.

Companies can easily set up pipelines to extract data from one storage area in the lake to another, which means they don’t have to worry about different platforms getting in the way of accessing the same content. A data lake solution can include all kinds of analytics tools, including natural language processing (NLP), artificial intelligence and machine learning (AI/ML), text mining, and predictive analytics to offer real-time insights into customer needs and business trends.

The cloud-based platform offers incredible scalability, allowing companies to grow as their data grows without interruption in services. With data lakes, it’s possible to analyze what works and doesn’t work within an organization at lightning speed.

See the Top Artificial Intelligence (AI) Software

Common Features of Data Lake Solutions

Data lake solutions have many features in common, such as data visualization, data access and sharing, scalability, and so on. Here are some common characteristics of data lake solutions.

• Data visualization enables users to explore and analyze large volumes of unstructured data by creating interactive visualizations for insights into their content.
• Scalability allows companies with both small and large databases to handle sudden spikes in demand without worrying about system failure or crashes due to a lack of processing power.
• File upload/download enables uploading and downloading files from the cloud or local servers into the data lake area.
• Machine learning helps AI systems learn about different types of information and detect patterns automatically.
• Integration facilitates compatibility across multiple software programs; this makes it easier for organizations to use whichever application they choose without having to worry about incompatibility issues between them.
• Data accessibility ensures that any authorized user can access the necessary files without waiting for lengthy downloads or parsing times.

The Best Cloud Data Lake Solutions

Here are our picks for the best data lake solutions based on our analysis of the market.

Snowflake

Snowflake is a SaaS (software-as-a-service) company that provides businesses an all-in-one single platform for data lakes, data warehousing, data engineering, data science and machine learning, data application, collaboration, and cybersecurity. The Snowflake platform breaks down barriers between databases, processing systems, and warehouses by unifying them into a single system to support an enterprise’s overall data strategy.

With Snowflake, companies can combine structured, semi-structured, and unstructured data of any format, even from across clouds and regions, as well as data generated from Internet of Things (IoT) devices, sensors, and web/log data.

Key Differentiators

• Consolidates Data: Snowflake can be used to store structured, semi-structured, and unstructured data of any format, no matter where it originates or how it was created.
• Unified Storage: Snowflake combines many different types of data management functions, including storage and retrieval, ETL workflows, security management, monitoring, and analytics.
• Analyze With Ease: The unified design lets users analyze vast amounts of diverse datasets with extreme ease and speed.
• Speed up AI Projects: Snowflake offers enterprise-grade performance without requiring extensive resources or time spent on complex configurations. Additionally, with integrated GPU and parallel computing capabilities, analyzing large datasets is faster.
• Data Query: Analysts can query data directly over the data lake with good scalability and no resource contention or concurrency issues.
• Governance and Security: All users can access data simultaneously without performance degradation, ensuring compliance with IT governance and privacy policies.

Cost

Snowflake does not list pricing details on their website. However, prospective buyers can join their weekly product demo or sign up for a 30-day free trial to see what this solution offers.

Databricks

Databricks is a cloud-based data platform that helps users prepare, manage, and analyze their data. It offers a unified platform for data science, engineering, and business users to collaborate on data projects. The application also integrates with Apache Spark and AWS Lambda, allowing data engineers to build scalable batch or streaming applications.

Databricks’s delta lake provides a robust transactional storage layer that enables fast reads and writes for ad hoc queries and other modern analytical workloads. Delta lake is an open-source storage layer that brings ACID transactions to Apache Spark and big data workloads.

Key Differentiators

• Databricks can cluster resources across multiple clusters to provide scale and fault tolerance.
• ​​The Databricks data lakehouse combines data warehouses and data lakes into a single platform that can manage all the corporate data, analytics, and AI use cases.
• The platform is built on open source.
• Databricks provides excellent performance with Apache Spark.
• The platform provides a unified source of information for all data, including real-time streams, ensuring high-quality and reliable data.

Costs

Databricks offers pay-as-you-go pricing. However, starting prices vary based on the cloud provider. A 14-day free trial is available for users that want to try it before buying.

Also read: Snowflake vs. Databricks: Big Data Platform Comparison

Cloudera data lake service

Cloudera data lake service is a cloud-based big data processing platform that helps organizations effectively manage, process, and analyze large amounts of data. The platform is designed to handle structured and unstructured data, making it ideal for a wide range of workloads such as ETL, data warehousing, machine learning, and streaming analytics.

Cloudera also provides a managed service called Cloudera Data Platform (CDP), which makes it easy to deploy and manage data lakes in the cloud. It is one of the top cloud data lake solutions because it offers numerous features and services.

Key Differentiators

• CDP can scale to petabytes of data and thousands of diverse users.
• Cloudera governance and data log features transform metadata into information assets, increasing its usability, reliability, and value throughout its life cycle.
• Data can be encrypted at rest and in motion, and users are enabled to manage encryption keys.
• Cloudera Data Lake Service defines and enforces granular, flexible, role- and attribute-based security rules as well as prevents and audits unauthorized access to classified or restricted data.
• The platform provides single sign-on (SSO) access to end users via Apache Knox’s secure access gateway.

Cost

Cloudera data lake service costs $650 per Cloudera Compute Unit (CCU) per year. Prospective buyers can contact the Cloudera sales team for quotes tailored to their needs.

Amazon web service lake formation

Amazon Web Services (AWS) Lake Formation is a fully managed service that makes it easy to set up a data lake and securely store and analyze data. With Lake Formation, users can quickly create a data lake, ingest data from various sources, and run analytics on data using the tools and services of their choice. Plus, Lake Formation provides built-in security and governance features to help organizations meet compliance requirements. Amazon Web Services also offers Elastic MapReduce, a hosted service that lets users access their cluster without having to deal with provisioning hardware or complex setup tasks.

Key Differentiators

• Lake formation cleans and prepares data for analysis using an ML transform called FindMatches.
• Lake formation enables users to import data from various database engines hosted by AWS. The supported database engines include MySQL, PostgreSQL, SQL Server, MariaDB, and Oracle database.
• Users can also use the AWS SDKs (software development kits) or load files into S3 and then use AWS Glue or another ETL tool to move them into lake formation.
• Lake Formation lets users filter data by columns and rows.
• The platform has the capabilities to rewrite various date formats for consistency and also make data analytically friendly.

Cost

AWS pricing varies based on region and number of bytes scanned by the storage API, rounded to the next megabyte, with a 10MB minimum. AWS charges for data filtering ($2.25 per TB of data scanned), transaction metadata storage ($1.00 per 100,000 S3 objects per month), request ($1.00 per million requests per month), and storage optimizer ($2.25 per TB of data processed). Companies can use the AWS pricing calculator to get an estimate or contact an AWS specialist for a personalized quote.

Azure data lake

Azure Data Lake is Microsoft’s cloud-based data storage solution that allows users to capture data of any size, type, and ingestion speed. Azure Data Lake integrates with enterprise IT investments for identity, management, and security. Users can also store any kind of data in the data lake, including structured and unstructured datasets, without transforming it into a predefined schema or structure.

Key Differentiators

• YARN (yet another resource negotiator) enables Azure Data Lake to offer elasticity and scale, so the data can be accessed when needed.
• Azure Data Lake provides encryption capabilities at rest and in transit and also has other security capabilities, including SSO, multi-factor authentication (MFA), and management of identities built-in through Azure Active Directory.
• Analyzing large amounts of data from diverse sources is no longer an issue. Azure Data Lake uses HDInsight, which includes HBase, Microsoft R Server, Apache Spark, and more.
• Azure Data Lake allows users to quickly design and execute parallel data transformation and processing programs in U-SQL, R, Python, and .Net over petabytes of data.
• Azure HDInsight can be integrated with Azure Active Directory for role-based access controls and single sign-on.

Cost

Prospective buyers can contact the Microsoft sales team for personalized quotes based on their unique needs.

Google BigLake

Google BigLake is a cloud-based storage engine that unifies data lakes and warehouses. It allows users to store and analyze data of any size, type, or format. The platform is scalable and easily integrated with other Google products and services. BigLake also features several security and governance controls to help ensure data quality and compliance.

Key Differentiators

• BigLake is built on open format and supports major open data formats, including Parquet, Avro, ORC, CSV, and JSON.
• It supports multicloud governance, allowing users to access BigLake tables as well as those created in other clouds such as Amazon S3 and Azure Data Lake Gen 2 in the data catalog.
• Using BigLake connectors, users can keep a single copy of their data and make it available in the same form across Google Cloud and open-source engines like BigQuery, Vertex AI, Spark, Presto, Trino, and Hive.

Cost

BigLake pricing is based on BigLake table queries, which include BigQuery, BigQuery Omni, and BigQuery Storage API.

Hadoop

Apache Hadoop is an open-source framework for storing and processing big data. It is designed to provide a reliable and scalable environment for applications that need to process vast amounts of data quickly. IBM, Cloudera, and Hortonworks are some of the top providers of Hadoop-based software. 

Key Differentiators

• Hadoop data lake architecture is made of several modules, including HDFS (Hadoop distributed file system), YARN, MapReduce, and Hadoop common.
• Hadoop stores various data types, including JSON objects, log files, images, and web posts.
• Hadoop enables the concurrent processing of data. This is because when data is ingested, it is segmented and distributed across various nodes in a cluster.
• Users can gather data from several sources and act as a relay station for data that is overloading another system.

Cost

Hadoop is an open-source solution, and it’s available for enterprises to download and use at no cost.

Choosing a Data Lake Provider

There are various options for storing, accessing, analyzing, and visualizing enterprise data in the cloud. However, every company’s needs are different. The solution that works best for a company will depend on what they need to do with their data, where it lives, and what business challenges they’re trying to solve.

There are many factors to consider when choosing a data lake provider. Some of the most important include:

• Security and Compliance: Ensure the provider meets security and compliance needs.
• Scalability: Businesses should choose a provider they can scale with as their data needs grow.
• Cost: Compare pricing between providers to find the most cost-effective option.
• Ease of Use: Consider how easy it is to use the provider’s platform and tools.

Read next: Top Big Data Storage Products

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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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For example, the analysis of current and past product and customer data can help organizations anticipate customer demand for new products and services and spot opportunities they might otherwise miss.

As a result, the market for Big Data tools is ever-growing. In a report last month, MarketsandMarkets predicted that the Big Data market will grow from $162.6 billion in 2021 to $273.4 billion in 2026, a compound annual growth rate (CAGR) of 11%.

A variety of purpose-built software and hardware tools for Big Data analysis are available on the market today. To make sense of all that data, the first step is acquiring a robust Big Data platform, such as Snowflake or Databricks.

Current Big Data analytics requirements have forced a major shift in Big Data warehouse and storage architecture, from the conventional block- and file-based storage architecture and relational database management systems (RDBMS) to more scalable architectures like scale-out network-attached storage (NAS), object-based storage, data lakes, and data warehouses.

Databricks and Snowflake are at the forefront of those changing data architectures. In some ways, they perform similar functions—Databricks and Snowflake both made our lists of the Top DataOps Tools and the Top Big Data Storage Products, while Snowflake also made our list of the Top Data Warehouse Tools—but there are very important differences and use cases that IT buyers need to be aware of, which we’ll focus on here.

What is Snowflake?

Snowflake for Data Lake Analytics is a cross-cloud platform that enables a modern data lake strategy. The platform improves data performance and provides secure, quick, and reliable access to data.

Snowflake’s data warehouse and data lake technology consolidates structured, semi-structured, and unstructured data onto a single platform, provides fast and scalable analytics, is simple and cost-effective, and permits safe collaboration.

Key differentiators

• Store data in Snowflake-managed smart storage with automatic micro-partitioning, encryption at rest and in transit, and efficient compression.
• Support multiple workloads on structured, semi-structured, and unstructured data with Java, Python, or Scala.
• Access data from existing cloud object storage instances without having to move data.
• Seamlessly query, process, and load data without sacrificing reliability or speed.
• Build powerful and efficient pipelines with Snowflake’s elastic processing engine for cost savings, reliable performance, and near-zero maintenance.
• Streamline pipeline development using SQL, Java, Python, or Scala with no additional services, clusters, or copies of data to manage.
• Gain insights into who is accessing what data with a built-in view, Access History.
• Automatically identify classified data with Classification, and protect it while retaining analytical value with External Tokenization and Dynamic Data Masking.

Pricing: Enjoy a 30-day free trial, including $400 worth of free usage. Contact the Snowflake sales team for product pricing details.

What is Databricks?

The Databricks Lakehouse Platform unifies your data warehousing and artificial intelligence (AI) use cases onto a single platform. The Big Data platform combines the best features of data lakes and data warehouses to eliminate traditional data silos and simplify the modern data stack.

Key differentiators

• Databricks Lakehouse Platform delivers the strong governance, reliability, and performance of data warehouses along with the flexibility, openness, and machine learning (ML) support of data lakes.
• The unified approach eliminates the traditional data silos separating analytics, data science, ML, and business intelligence (BI).
• The Big Data platform is developed by the original creators of Apache Spark, MLflow, Koalas, and Delta Lake.
• Databricks Lakehouse Platform is being developed on open standards and open source to maximize flexibility.
• The multicloud platform’s common approach to security, data management, and governance helps you function more efficiently and innovate seamlessly.
• Users can easily share data, build modern data stacks, and avoid walled gardens, with unrestricted access to more than 450 partners across the data landscape.
• Partners include Qlik, RStudio, Tableau, MongoDB, Sparkflows, HashiCorp, Rearc Data, and TickSmith.
• Databricks Lakehouse Platform provides a collaborative development environment for data teams.

Pricing: There’s a 14-day full trial in your cloud or a lightweight trial hosted by Databricks. Reach out to Databricks for pricing information.

Snowflake vs. Databricks: What Are the Differences?

Here, in our analysis, is how the Big Data platforms compare:

Choosing a Big Data Platform

Organizations need resilient and reliable Big Data management, analysis and storage tools to reliably extract meaningful insights from Big Data. In this guide, we explored two of the best tools in the data lake and data warehouse categories.

There are a number of other options for Big Data analytics platforms, and you should find the one that best meets your business needs. Explore other tools such as Apache Hadoop, Apache HBase, NetApp Scale-out NAS and others before making a purchase decision.

Further reading:

• Top Artificial Intelligence (AI) Software
• Best Big Data Tools & Software for Analytics
• Top DataOps Tools
• Top Big Data Storage Products

The post Snowflake vs. Databricks: Big Data Platform Comparison appeared first on IT Business Edge.

Though often used during the development of a new software system to aid in analysis and planning, data flow diagrams give unparalleled insight into every instance where data is potentially vulnerable.

Anatomy of a Data Flow Diagram

Data flow diagrams visually detail data inputs, data outputs, storage points, and the routes between each destination.

Components of a Data Flow Diagram

• Entities – Show the source and destination for the data. They are generally represented by a rectangle.
• Process – The tasks performed on the data is referred to as a process. Circles in a data flow diagram indicate a process.
• Data Storage – Data is generally stored in databases, which are seen in data flow diagrams inside a rectangle with the smaller sides missing.
• Data Flow – Displays the movement of data with the help of lines and arrows.

Also read: Unifying Data Management with Data Fabrics

Logical Vs. Physical Data Flow Diagrams

There are two primary types of data flow diagrams, each with a specific function and designed to inform a different target audience.

Logical data flow diagrams

Logical data flow diagrams illustrate how data flows in a system, with a focus on the business processes and workflows. With a focus on how the business operates at a high level, logical data flow diagrams are a great starting point, providing the outline needed to create more detailed physical data flow diagrams.

Benefits of logical data flow diagrams:

• Provide an overview of business information with a focus on business activities
• Less complex and faster to develop
• Less subject to change because business functions and workflows are normally stable processes
• Easier to understand for end-users and non-technical stakeholders
• Identify redundancies and bottlenecks

Physical data flow diagrams

Physical data flow diagrams provide detailed implementation information. They may reference current systems and how they operate, or may project the desired end-state of a proposed system to be implemented.

Physical data flow diagrams offer a number of benefits:

• Sequences of activities can be identified
• All steps for processing data can be described
• Show controls or validating input data
• Outline all points where data is accessed, updated, retrieved, and backed up
• Identify which processes are manual, and which are automated
• Provide detailed filenames, report names, and database field names
• Lists all software and hardware participating in the flow of data, including any security-related appliances

Also read: Top Data Quality Tools & Software

Strategies For Developing Data Flow Diagrams

Avoid feeling overwhelmed by the creation of a data flow diagram by following a few simple strategies.

• Begin with lists of all business activities, vendors, ancillary systems, and data stores that need to be included.
• Take each list and identify the data elements needed, received, or generated.
• Always include steps that initiate changes to data or require decisions be made, but avoid creating a flowchart (for example, identify that the user needs to accept or reject an incoming order or reservation, but don’t break it down by ‘if yes, then’ and ‘if no, then’).
• For complex systems, it may be helpful to start by adding data stores to the diagram and working outward to each of the processes involved – it is likely that single data inputs are used or accessed repeatedly.
• Ensure that there are no freestanding activities – only include processes that have at least one data flow in or out.
• Review labels to be sure they are concise but meaningful.
• Try to limit each data flow diagram to a maximum of 5-7 processes, creating child diagrams where appropriate or required.
• Consider numbering the processes to make the diagram easier to review and understand.
• A successful data flow diagram can be understood by anyone, without the need for prior knowledge of the included processes.

Using A Data Flow Diagram To Mitigate Security Threats

The best way to protect data from security threats is to be proactive instead of reactive.

Data flow diagrams can support cybersecurity initiatives in many ways:

• Identify when data is at rest and in transit.
• Visualize when data is shared with external vendor systems.
• Know which users and systems have access to which data, at which time.
• Enable the notification of affected users, systems, and vendors in the event of a security breach or threat.
• Understand the schedule of automated processes to know when data is being offloaded or consumed.

To best support the mitigation of security threats, data flow diagrams should include all risk assessments (corporate governance, external vendors and ancillary systems, and key business processes), complete inventory listings (hardware and software systems), and all user roles that have and require access to data at every point.

For targeted threat modeling, it may be helpful to create additional data flow diagrams to support a specific use case. One example would be a diagram that looks at authentication separate and apart from the workflows and processes that access will be granted to.

Comprehensive data flow diagrams ultimately show where the systems make data vulnerable. Threat modeling best practices generally consider data safest when at rest, so look to points in data flow diagrams where data is sent or received to ensure security and integrity are maintained.

A Living Part of System Documentation

Don’t forget that data may move through systems and processes in non-technical ways as well. Paper-based or non-technical business processes where information is gathered or stored should also be included in data flow diagrams.

Data flow diagrams should become a living part of system documentation and be thought of as a source of truth. As systems and processes are updated, it’s important that the consequences to data flow or data integrity are considered and reflected in any existing diagrams.

Read next: Best Data Governance Tools & Software

The post Identify Where Your Information Is Vulnerable Using Data Flow Diagrams appeared first on IT Business Edge.

In addition, beginning with a strategy in mind with the multicloud approach toward data management can go a long way in optimizing data management and minimizing risks.

Also read: Top 7 Data Management Trends to Watch in 2022

Multicloud Environments for Enterprise Functionality

If you find it challenging to match the different cloud platforms against the various needs of your organization, you might appreciate how difficult hosting can get.

Fortunately, developing a multicloud infrastructure can help you avoid haphazardly appending data clouds into a complex architecture that is difficult to maintain and manage. Using different clouds in such a way can be beneficial in several ways.

1. Flexibility

A common problem IT managers face is that one cloud service provider might be perfect for a portion of the organization’s functionality. In contrast, another service might be better suited for other applications. For example, a proprietary cloud would be perfect for hosting proprietary apps but might not be cost-efficient for storing public records. In a case like this, multicloud allows you to use an appropriate cloud suited to a particular area.

2. Proximity

When you are operating globally, you might have to make additional efforts to manage compliance with the local data sovereignty laws. In this case, you could host a part of your workload with regional cloud providers. It will have the additional benefit of better speeds for the end user.

3. Shadow IT

Multicloud is a straightforward approach to consolidating shadow IT architectures. Shadow IT has become common today, partly because of the ease of use offered by cloud services. However, it is a kind of data silo that causes redundancies and security issues and ultimately slows you down.

4. Failover

Multicloud ensures business continuity by offering backup that can scale with your enterprise and host data and workflows. This would mean getting back up in no time and without any data loss in the case of an outage.

In disaster recovery, you would typically restart the service. However, there might be a chance that it doesn’t work. In that case, if you have a secondary cloud, you can deploy the service there and reduce recovery time.

Also read: AWS vs Azure vs Google vs Alibaba: Compare Top Cloud Providers

Multicloud and Data Management

Multicloud presents a unique architecture, with specific requirements for data migration and storage. Having a multicloud approach will require data management strategies specifically designed for it. However, it must be noted that having a new data management strategy offers several advantages of its own, such as:

• If the demand for the workload increases beyond capacity, also known as cloud-burst, a secondary cloud can provide the additional space while the primary cloud deals with the regular traffic.
• Applications typically deploy all service instances to every available location. However, with multicloud, you can selectively deploy data based on the hardware availability.
• Multicloud abolished the trend of proprietary formats, which made users dependent on a particular cloud solution, and made it possible for users to be independent of the cloud service it uses.
• Multicloud favors analytics and artificial intelligence (AI) operations. AI and machine learning can be used to filter through data for metrics that help you improve operations and predict issues.
• Ultimately, multicloud allows a holistic model for data management by reducing architecture complexity. Though there will be serious changes in on-premises architecture, the result will be better optimized.

How Multicloud Helps with Data Management Best Practices

Data management best practices are equally applicable to multicloud as they would be to any other approach, and they must be planned and executed with due diligence.

1. Having a plan

Having multiple environments needs properly designed and documented ways to manage the data generated. Simply having the same old strategy and applying it across platforms does not work and will limit full functionality, such as collaboration. A good strategy will account for ongoing changes and be open to adoption.

2. Addressing complexity

A multicloud environment would have multiple locations on-premises and across clouds, leading to more complexity. In the end, your architecture should work seamlessly between them, which requires a good data strategy.

3. Compliance and data sovereignty

While data compliance requirements can be complicated, they get even trickier with the new complex architecture. Cloud backups solve the issue for you to an extent. Use proper resources for data mobility and consistency to ease up your workload.

Challenges for Data Management

A successful multicloud strategy addresses the following challenges that anyone operating over the cloud will inevitably face at some point.

Security concerns

Having multiple clouds means moving and managing data across different channels and, inevitably, more access points. Unfortunately, more access points make your database more vulnerable to security threats, so security must be built in every step of the way.

Data governance

Globally, regulations such as GDPR and CCPA require users and providers to share the accountability for privacy breaches. A multicloud strategy would increase the data governance requirements due to having more clouds, which in turn increases the liability for your organization.

Visibility

Having applications running on different clouds would lead to issues with the invisibility of the cloud storage landscape. It would further demand more tools and processes to function, which can sometimes be difficult to manage.

Data migration

There is still a lack of cloud-native tools to migrate data between providers. You are likely to require third-party migration tools, meaning extra licensing costs.

Also read: Cloud Security Best Practices for 2022

Adopting Best Data Management Practices

The requirement of data management practices remains the same, but their complexity varies with multicloud. Multicloud not only supports data management best practices but in some cases mandates them. Data management on multicloud can be tricky, but it has many benefits if done right.

To make full use of multicloud, and create a strategy capable of addressing the varying complexities of the new system, never try to replicate the same methodologies across the whole enterprise. This can limit your return on investment (ROI) and lead to poor performance, as it would further aggravate the possibility of having data silos and lead to a lack of visibility across cloud storage environments. Have a plan can help you reach data synergies, leading to cost optimization.

You need the right data management strategy, from planning and migrating data to running day-to-day operations. A good strategy will include consideration of data protection and security, visibility, and governance. In the end, you want to get the best features of all the cloud options that you choose and seamless data management so your IT department can focus on innovation instead of maintenance.

Read next: Unifying Data Management with Data Fabrics

The post Multicloud Strategies for Data Management appeared first on IT Business Edge.

However, data migration is a complicated and often expensive process. You will need the right approach to migrating data without error, including well thought-out strategies and appropriate tools.

Also read: Best Cloud Migration Vendors & Services

What is Data Migration?

Data migration refers to the process of transferring data from one storage system to another. It begins with data selection and preparation, during which extraction and transformation takes place. Following this step, permanent data is moved from the old storage system and loaded onto an appropriate data store. Then, the data migration ends with decommissioning the old storage system.

Data migration typically falls into one of two categories:

• Cloud Migration: Data or applications are migrated from a physical storage system to the cloud or between two cloud environments.
• Data Center Migration: Data is migrated from one on-premises data center to another for upgrading or relocation.

After decide where you’re going to migrate, next you need to determine what you need to migrate:

• Storage Migration: Data is moved from one physical storage solution to another.
• Database Migration: Structured, or database managed, data is moved using a database management system.
• Application Migration: Data is migrated from one computing environment to another to support a change in application software.
• Business Process Migration: Business applications and data related to business processes and metrics are migrated.

Why Do You Need Data Migration?

Organizations opt to upgrade their storage systems and consequentially migrate data for several reasons that ultimately help them gain a competitive advantage. Database migration helps companies overcome storage limitations and can facilitate better data management features and processing speed. On the other hand, storage migration is chiefly focused on upgrading to support new technology.

Other scenarios where you might find the need for data migration include:

• You want to upgrade to a new infrastructure to make up for size constraints.
• You want to optimize the overhead costs of running a data center.
• You need to merge new data following an acquisition.
• You need to relocate your data center.
• You want to implement a disaster recovery solution.
• You want to move an application to the cloud, for reasons ranging from ease of maintenance and access to cost

Strategies and Precursors to Data Migration

Strategizing in advance will help you save on costs and prevent downtime to ensure business continuity. It is essential to consider your limitations and understand the overall scope of your data migration project. There are two key factors that you need to consider before launching a data migration project, namely the size and time.

• Data Size: Most datasets are too big to be simply uploaded to the cloud and will need to be shipped on physical devices. This is primarily because of speed and cost constraints. You can send data below 10TB through standard drives, while larger data in the petabyte range will need specialized devices meant for data migration.
• Time Constraints: Bandwidth, network speed and limitations, and dataset size are key considerations when calculating how much time a data migration will take. If data needs to be shipped on physical devices, that time should also be taken into account.

After considering data size and time constraints, you can formulate your project budget and timeline. You also need to decide on the tools and framework for database migration. This will give you an overview of the entire process of data migration.

In addition, you will also need to decide on the migration approach, i.e., to pick between the big-bang approach and doing it in one go or the trickle approach–where you migrate in phases with both systems operating side-by-side.

Also read: 5 Cloud Migration Strategies

Key Steps to Data Migration

Data migration is one of the most critical projects your company will undertake, requiring careful efforts at every step. The reason behind the complexity is that you do not want to compromise data quality, as data-driven businesses will suffer errors in core operations otherwise.

After planning, there are roughly five more stages to data migration:

1. Data preparation involves some key actions targeted at making the data suitable for the migration. Beginning with auditing, an automated process is run to analyze data quality and inform you about inconsistencies, duplicate entries, or poor health. Next, you back up files and establish access levels.
2. Data mapping involves matching the data field between the source and the new destination.
3. Execution is where data is extracted, processed, and loaded to the destination.
4. Testing is ideally a continuous process in data migration, especially when you are migrating data in phases. Once the entire migration process is complete, you need to run another iteration of automated testing, fix the issues, and proceed to go live.
5. Auditing the data again once it is live is necessary to ensure successful completion. You should also run timely audits and monitor the system’s health.

Tools of Migration

There are numerous tools that can assist you through the migration process. And many cloud providers offer their own set of tools. Other tools, including several free and open-source applications such as Data Loader by Salesforce, are also available. Like the migration types, the migration tools can be self-scripted, on-premises, and cloud-based. Other major tools include Amazon’s AWS Data Pipeline, IBM Informix, and Microsoft Azure CosmosDB.

Also read: Successful Cloud Migration with Automated Discovery Tools

Challenges in Data Migration

Data migration is inherently complex, and there are likely going to be several challenges when carrying out this project in your organization.

• Failing to include concerned parties might disrupt your business activities and the data migration process in general. Keep them updated on a weekly basis about the progress.
• Lack of data governance or clarity about who has access to the data in the source system can create confusion and hamper data quality. A clearly defined data governance framework is essential to overcome this challenge.
• A generic and unproven migration method might do more harm than good. Always look for a reliable testimonial-backed service provider, and pick an experienced team.
• Insufficient skills and inadequate tools can both lead to unexpected delays and cost you valuable time. Give it due to research and ensure that the team assigned with data migration is sufficiently trained and has all the necessary tools.
• Planning is indispensable. It might not be sufficient by itself to guarantee successful migration, but it is necessary.

Ready to Migrate Your Data?

While data migration might not sound too daunting in theory, it is a complex process with many variables that must be figured out beforehand. Therefore, you’ll need a specialized team to execute and monitor the data migration process and treat it like a major project.

You can also take advantage of several premium and open-source applications to help you with your data migration. Like the migration types, migration tools can be self-scripted, on-premises, and cloud-based, giving you plenty of flexibility to proceed with your data migration in a way that’s best for your company.

Although it is a major undertaking, you can proceed without hesitation once you have given it due thought.

Read next: Top 7 Data Management Trends to Watch in 2022

The post Strategies for Successful Data Migration appeared first on IT Business Edge.

But his motto could also apply to data. If anything, the opportunity could be much larger. Data is becoming a competitive advantage for many companies.

Yet that data can be difficult to process. The fact is that it’s common for AI and analytics projects to fail or underperform.  

But there is good news. There are startups that are developing tools to help companies with their data journeys.

Here’s a look at eight of them to put on your radar. No Databricks in here, which has become so big that the next step is likely an IPO, but there are some billion-dollar “unicorn” valuations even in a slowing market.

Also read: Data Startups: Why the Eye-Popping Funding Rounds?

People Data Labs

People Data Labs (PDL) is focused on B2B and professional data. By processing resumes, the company has been able to provide valuable insights for recruiting, market research, sales and marketing.

“We see every company in the world building data solutions,” said PDL CEO Sean Thorne. “This is a rapidly growing market.’

The company does not focus on selling flat files of leads or contracts, which is the traditional approach. Instead, it uses a data-as-a-service model and is part of the AWS Data Exchange platform. This makes it easier to provide data to customers in an easy-to-use format.

In 2021, PDL raised $45 million in a Series B round of funding.

Airbyte

Airbyte is focused on rethinking the data integration market. The company’s technology is based on an open source platform, which has supercharged adoption and innovation. There are more than 20,000 companies on the system and the community includes about 7,000 data practitioners.

A key to Airbyte is that it can handle virtually any data pipeline, such as with database replication and long-tail and custom connectors. There is no need for in-house data engineers to maintain the systems.

Last year, the company raised more than $181 million.

Imply

The founders of Imply are the creators of Apache Druid, which is an open source database system for high-performance, real-time analytics. This experience has been critical in evolving the technology and tailoring it to the needs of enterprise customers.

The target end-user is software developers. With Imply, they can create sophisticated analytics applications.

“While adoption of Druid started with digital natives like Netflix, AirBnB and Pinterest, increasingly enterprises in the Fortune 1000 are recognizing the value of analytics applications as a way of differentiating their businesses,” said Fangjin Yang, CEO and cofounder, Imply. “And that’s what’s fueling the tremendous market opportunity for our category of real-time analytics databases.”

This year, the company raised $100 million at a $1.1 billion valuation.

Also read: Best Database Management Software 2022

MinIO

A majority of data is unstructured, which can be difficult to store and manage.

This is where MinIO comes in. Consider that its system gets over 1 million Docker pulls per day and more than half the Fortune 500 use the technology.

“The market for MinIO’s object storage product can be described simply: everywhere AWS S3 isn’t,” said Garima Kapoor, COO and cofounder, MinIO. “Even accounting for AWS’s size, this is a massive market. MinIO delivers AWS S3-like infrastructure across any cloud, virtual or bare-metal deployment scenario.”

To date, the company has raised $126 million.

Cribl

A major challenge for enterprises is dealing with diverse sources of data. But for Cribl, this has been a great opportunity. The company has built an open and interoperable platform to manage data better and get more value from it.

“What we hear from our IT and security customers is that they have an array of important tools they use across the enterprise but none of those tools talk to one another,” said Nick Heudecker, Senior Director, Market Strategy & Competitive Intelligence, Cribl. “Cribl’s solutions are open by design, seek to connect the disparate parts of the data ecosystem – such as complementing tools like Datadog, Exabeam, and Elastic — and give customers choice and control over all the event data that flows through their corporate IT systems.”

For fiscal year 2021, the company more than tripled its customer count. Ten of the 50 Fortune companies have signed on.

Cribl has raised a total of $254 million since inception.

Observable

Observable operates a SaaS platform for real-time data collaboration, visualization and analysis. The founders created the company because of their frustration of constant “tool hopping” with existing data products. This made the process error-prone, tedious and slow.

Observable is JavaScript-native, which helps to lower the learning curve. The company also has the benefit of a large community of 5 million users. This has resulted in the largest public library of data visualizations.

In all, the company has raised $46.1 million.

Reltio

Reltio is a cloud-native platform that focuses on the master data management category. There are many legacy players in the market, such as Informatica, Tibco, IBM, SAP and Oracle. As for Reltio, it sees an opportunity for disruption.

“We have various integration options, including a low-code/no-code solution, that allow for rapid deployment and time to value,” said Manish Sood, founder and CTO, Reltio. “Our system also uses machine learning to discover deeper data insights and improve data quality. Then there is built-in workflow management, which helps simplify compliance requirements and improve information stewardship productivity.”

The company counts 14 of the Fortune 100 as customers. To date, it has raised $237 million, with a valuation at over $1.7 billion.

TigerGraph

TigerGraph is a system that allows for advanced analytics and AI with connected data. The technology has diverse applications, such as for anti-money laundering, fraud detection, IoT (Internet of Things) and network analysis.

Traditional analytics systems are built on relational databases. But this can be expensive and rigid. It can also be more difficult to leverage next-generation analytics like deep learning.

This is why graph databases are becoming more popular. “Customers want to model their data from the viewpoint of the customer, supplier, or whatever entity they want to analyze and how they interact with the company across systems like CRMs, procurement, logistics and so on,” said Todd Blaschka, COO, TigerGraph.

Last year, the company raised $105 million in a Series C funding.  

A tougher market in 2022?

2022 may not give us as many eye-popping funding rounds, but if any area stays strong, it’s likely to be the startups fueling the data analytics craze.

Read next: Top Artificial Intelligence (AI) Software 2022

The post 8 Top Data Startups appeared first on IT Business Edge.

And for that data to be effectively used for decision-making, it must be cleaned, normalized, formatted, and analyzed.

For this purpose, many businesses are turning to data profiling tools, which monitor and clean data to improve its quality, allowing them to secure a competitive advantage in the burgeoning marketplace. One common use of data profiling software is to maximize potential opportunities from sales data, for example.

Also see the Top Data Quality Tools & Software

What is Data Profiling?

Data profiling involves examining source data; understanding its structure, content, and relationships; and creating information summaries that can be used to make critical business decisions.

Data profiling is combined with the extract, transfer, and load (ETL) process and is vital for business intelligence (BI), data warehousing, and data conversion and migration projects.

Types of Data Profiling

The types of data profiling include:

• Relationship discovery detects the interactions between data sources and establishes links within the data.
• Content discovery takes a closer look at data and discovers issues in specific rows and columns of datasets. It also leverages techniques like frequency counts, uniformity, and outlier detection.
• Structure discovery examines the rows and columns of datasets and determines the consistency of data. It also leverages techniques such as validation with metadata and pattern matching.

What are the Steps of Data Profiling?

Data profiling includes the following steps:

1. Gather data types, patterns, variation, uniqueness, frequency, and length.
2. Collect statistics and descriptive information.
3. Check metadata and its accuracy.
4. Tag data with labels, categories, and keywords.
5. Identify structures, relationships, and dependencies.
6. Calibrate the ETL process and data conversion and migration.
7. Assess data quality.
8. Assess the risk involved in data integration.
9. Understand data challenges early to avoid issues.
10. Determine the validity, completeness, and accuracy of data.

Also read: Top DataOps Tools

Top Data Profiling Tools

Here are five top data profiling tools and software that stood out to us in our survey of the market.

WinPure Clean & Match

WinPure Clean & Match is a data quality, cleaning, matching, and deduplication data profiling software suite for customer relationship management (CRM), spreadsheets, databases, mailing lists, and more.

Key Differentiators

• The one-click data cleaning mode processes all the clean options across numerous columns simultaneously.
• The data profiling tool immediately fixes data quality issues. WinPure Clean & Match scans every data list and provides more than 30 statistics ranging from common values and counts to the percentage of filled or empty cells.
• WinPure Clean & Match features amber and red coloring to bring to light potential data quality problems, including trailing/leading spaces, hyphens, dots, etc., all of which can be fixed from a solitary click.
• The solution features an intelligent data matching engine that is speedy and accurate.
• Standard and proprietary algorithms detect abbreviated, miskeyed, fuzzy, and phonetic variations using an in-depth domain knowledge of names that includes multicultural name variations and nicknames.
• Data matching reports are created automatically and can be emailed, printed, or exported.

Pricing: WinPure Clean & Match is available in four editions: Community, Small Business, Pro Business, and Enterprise. The Community edition of WinPure Clean & Match is free while the Enterprise edition is feature-laden and ideal for enterprise use. Those interested can take advantage of a free trial or reach out to the WinPure sales to request a demo.

DemandTools

DemandTools by Validity is a secure and versatile data profiling tool that effectively cleans and maintains your CRM data. By providing report-ready data in less time, DemandTools enhances the effectiveness of your revenue operations.

Key Differentiators

• All aspects of data can be managed in minutes, rather than months, by leveraging repeatable processes.
• Records can be automatically assigned, standardized, and deduped as they come in from integrations, end-user entry, and spreadsheets.
• Clean data can be obtained to enhance the performance of support, marketing, and sales as well as the retention and revenue they generate.
• Users can better understand how weak or strong data is and where to focus remediation efforts with the data quality assessment feature
• With data migration management, data integrity can be maintained during record deletion, exports, and imports.
• The email verification feature helps verify email addresses in your CRM to maintain seamless communication with customers.
• Other features include duplicate management, user management and standardization, mass modification, and business insights.

Pricing: DemandTools starts at $10 per CRM license per month. Before purchasing the data profiler, you can make use of a free trial.

Introhive Cleanse

Introhive Cleanse is a data profiling tool that provides an on-demand manner to maintain the accuracy of your prospect and customer data.

Key Differentiators

• With Introhive Cleanse, users can clean up CRM data simply and efficiently.
• Accounts and contacts can be enhanced and enriched with clean and compliant data in real time.
• Introhive Cleanse eliminates waiting for bouncebacks, armies of data stewards, and batch updates.
• With Introhive Cleanse, users can enjoy data quantity and quality.
• Users can identify stale contacts, merge data dupes, and maintain relevance.
• Using clean data, sales can prospect efficiently, and marketing can increase the effectiveness of campaigns.

Pricing: Those interested can book a demo at the earliest. Contact the Introhive sales team for pricing details.

RingLead Cleanse

RingLead Cleanse by Zoominfo is a data profiler that uses patented duplicate merging technology to discover and eradicate duplicates inside a CRM and marketing automation platform (MAP).

Key Differentiators

• The RingLead Deduplication merging module permits users to have full control of how duplicates merge into a solitary record.
• Any custom or standard object can be deduped while leveraging best-practice templates and an adaptable drag-and-drop configuration for merging and matching duplicates.
• Users can take advantage of advanced fuzzy matching criteria for high match rates.
• With the mass delete feature, users can locate and delete junk data files in a few simple clicks.
• Along with single table deduplication, RingLead Cleanse enables users to locate and merge duplicates at the cross-object level.
• Batch normalization templates can be customized to unify data to a single format, proper-case data, and fix data errors and typos.
• With the mass update feature, existing records can be modified without complex manual work.

Pricing: You can enjoy a free trial of the complete RingLead platform. Reach out to the RingLead sales team for pricing information or to schedule a demo.

Openprise Data Cleansing Automation

The Openprise Data Cleansing Automation platform helps clean and format data; normalize field values; dedupe accounts, contacts, and leads; match leads to accounts; and more to make all data go-to-market ready.

Key Differentiators

• Openprise bots can be deployed to monitor and clean data in real time.
• The data profiling tool normalizes data according to set specifications.
• Openprise consistently deduplicates leads, contacts, and accounts.
• The data profiling tool can deduplicate any object that has been built.
• Lead-to-account matching enables users to analyze numerous fields to establish a match across multiple languages.
• Other features include contacts and accounts segmentation, data enrichment, and data unification.

Pricing: Reach out to the Openprise sales team for product pricing details or to request a demo.

Choosing a Data Profiling Tool

A data profiler cleans, analyzes, monitors, and reviews data from existing databases as well as other sources for a variety of data-related projects. The procedure enables businesses to extract maximum value from garnered data and make effective decisions for business growth.

When making a data profiling tool purchasing decision, be sure to go through product catalogs, evaluate their features, contrast pricing plans, and analyze peer-to-peer reviews to make sure you get the tool that’s right for your needs and business.

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

The post Best Data Profiling Tools in 2022 appeared first on IT Business Edge.

In the past few years, data has exploded in size and complexity. For example, the amount of data created, captured, copied, and consumed worldwide will hit 181 zettabytes by 2025, up from only two zettabytes in 2010.

This fact has made it difficult for businesses to promptly gather, analyze, and act on data. However, DataOps (data operations) is a software framework that was created to address this very problem.

What is DataOps?

Introduced by IBM’s Lenny Liebmann in June 2014, DataOps is a collection of best practices, techniques, processes, and solutions that applies integrated, process-oriented, and agile software engineering methods to automate, enhance quality, speed, and collaboration while encouraging a culture of continuous improvement in the field of data analytics.

DataOps began as a collection of best practices but has since grown into a novel and autonomous data analytics method. It considers the interrelatedness of the data analytics team and IT operations throughout the data lifecycle, from preparation to reporting.

Also read: 6 Ways Your Business Can Benefit from DataOps

What is the Purpose of DataOps?

DataOps aims to enable data analysts and engineers to work together more effectively to achieve better data-driven decision-making. The ultimate goal of DataOps is to make data analytics more agile, efficient, and collaborative.

To do this, there are three main pillars of DataOps:

• Automation: Automating data processes allows for faster turnaround times and fewer errors.
• Quality: Improving data quality through better governance and standardized processes leads to improved decision-making.
• Collaboration: Effective team collaboration leads to a more data-driven culture and better decision-making.

DataOps Framework

The DataOps framework is composed of four main phases:

• Data preparation involves data cleansing, data transformation, and data enrichment, which is crucial because it ensures the data is ready for analysis.
• Data ingestion handles data collection and storage. Engineers must collect data from various sources before it can be processed and analyzed.
• Data processing is the process of data transformation and data modeling to transform raw data into usable information.
• Data analysis and reporting helps businesses make better decisions by analyzing data to generate insights into trends, patterns, and relationships and reporting the results.

DataOps tools operate as command centers for DataOps. These solutions manage people, processes, and technology to provide a reliable data pipeline to customers.

In addition, these tools are primarily used by analytics and data teams across different functional areas and multiple verticals to unify all data-related development and operation processes within an enterprise.

When choosing a DataOps tool or software, businesses should consider the following features:

• Collaboration between data providers and consumers can guarantee data fluidity.
• It can act as an end-to-end solution by combining different data management practices within a single platform.
• It can automate end-to-end data workflows across the data integration lifecycle.
• Dashboard and visualization tools are available to help stakeholders analyze and collaborate on data.
• It can be deployed in any cloud environment.

Also read: How to Turn Your Business Data into Stories that Sell

5 Best DataOps Tools and Software

The following are five of the best DataOps tools and software.

Census

Census is the leading platform for operational analytics with reverse ETL (extract, transform, load), offering a single, trusted location to bring your warehouse data into your daily applications.

It sits on top of your existing warehouse and connects the data from all of your go-to-market tools, allowing everyone in your company to act on good information without requiring any custom scripts or favors from IT.

Over 50 million users receive personalized marketing thanks to Census clients’ performance improvements, including a 10x sales productivity increase due to a support time reduction of up to 98%.

In addition, many modern organizations choose Census for its security, performance, and dependability.

Key Features

• Work With Your Existing Warehouse: Because Census operates on top of your current warehouse, you can retain all your data in one location without the need to migrate to another database.
• No-Code Business Models: With the simple interface, you can build data models without writing code, allowing you to focus on your business instead of worrying about data engineering.
• Works at Scale: Census is built to handle data warehouses with billions of rows and hundreds of columns.
• Build Once, Reuse Everywhere: After you create a data model, you can use it in any tool connected to your warehouse. This means that you can build models once and use them in multiple places without having to recreate them.
• No CSV Files and Python Scripts: There is no need to export data to CSV files or write Python scripts. Census has a simple interface that allows you to build data models to integrate into sales and marketing tools without writing code.
• Fast Sync With Incremental Batch Updates: Census synchronizes data in real time, so you can always have the most up-to-date data. Incremental updates mean that you never have to wait for a complete data refresh.
• Multiple Integrations: Census integrates with all of the leading sales, marketing, collaboration, and communications tools you already use. These include Salesforce, Slack, Marketo, Google Sheets, Snowflake, MySQL, and more.

Pros

• It is easy to set up and sync a data pipeline.
• Census offers responsive and helpful support.
• The solution reduces engineering time to create a sync from your data warehouse to third-party services.

Cons

• Many integrations are still in active development and are buggy to use.

Pricing

Census has three pricing tiers:

• Free: This tier only includes 10 destination fields but is ideal for testing the tool’s features.
• Growth: At $300 per month, Growth includes 40 destination fields as well as a free trial.
• Business: At $800 per month, Business includes 100 destination fields and a free demo.
• Platform: This is a custom solution for enterprises that would like more than 100 destination fields, multiple connections, and other bespoke features.

Mozart Data

Mozart Data is a simple out-of-the-box data stack that can help you consolidate, arrange, and get your data ready for analysis without requiring any technical expertise.

With only a few clicks, SQL commands, and a couple of hours, you can make your unstructured, siloed, and cluttered data of any size and complexity analysis-ready. In addition, Mozart Data provides a web-based interface for data scientists to work with data in various formats, including CSV, JSON, and SQL.

Moreover, Mozart Data is easy to set up and use. It integrates with various data sources, including Amazon SNS, Apache Kafka, MongoDB, and Cassandra. In addition, Mozart Data provides a flexible data modeling layer that allows data scientists to work with data in various ways.

Key Features

• Over 300 Connectors: Mozart Data has over 300 data connectors that make it easy to get data from various data sources into Mozart Data without hiring a data engineer. You can also add custom connectors.
• No Coding or Arcane Syntax: With Mozart Data, there is no need to learn any coding or arcane syntax. All you need to do is point and click to get your data into the platform.
• One-Click Transform Scheduling and Snapshotting: Mozart Data allows you to schedule data transformations with a single click. You can also snapshot your data to roll back to a previous version if needed.
• Sync Your Favorite Business Intelligence (BI) Tools: Mozart Data integrates with most leading BI tools, including Tableau, Looker, and Power BI.

Pros

• The solution is easy to use and requires little technical expertise.
• It offers a wide variety of data connectors, including custom connectors.
• Users can schedule data transformations with a single click.
• Mozart Data has straightforward integrations with popular vendors such as Salesforce, Stripe, Postgres, and Amplitude.
• A Google Sheets sync is available.
• Mozart Data provides good customer support.

Cons

• Non-native integrations require some custom SQL work.
• The SQL editor is a bit clunky.

Pricing

Mozart data has three pricing tiers starting at $1,000 per month plus a $1,000 setup fee. All plans come with a free 14-day trial.

Databricks Lakehouse Platform

Databricks Lakehouse Platform is a comprehensive data management platform that unifies data warehousing and artificial intelligence (AI) use cases on a single platform via a web-based interface, command-line interface, and an SDK (software development kit).

It includes five modules: Delta Lake, Data Engineering, Machine Learning, Data Science, and SQL Analytics. Further, the Data Engineering module enables data scientists, data engineers, and business analysts to collaborate on data projects in a single workspace.

The platform also automates the process of creating and maintaining pipelines and executing ETL operations directly on a data lake, allowing data engineers to focus on quality and reliability to produce valuable insights.

Key Features

• Streamlined Data Ingestion: When new files arrive, they are handled incrementally within regular or continuous jobs. You may process new files in scheduled or ongoing processes without keeping track of state information. With no requirement for listing new files in a directory, you can track them efficiently (with the option to scale to billions of files) without listing them in a directory. Databricks infers and evolves the schema from source data as it loads into the Delta Lake.
• Automated Data Transformation and Processing: Databricks provides an end-to-end solution for data preparation, including data quality checking, cleansing, and enrichment.
• Build Reliability and Quality Into Your Data Pipelines: With Databricks, you can easily monitor your data pipelines to identify issues early on and set up alerts to notify you immediately when there is a problem. In addition, the platform allows you to version-control your pipelines, so you can roll back to a previous version if necessary.
• Efficiently Orchestrate Pipelines: With the Databricks Workflow, you can easily orchestrate and schedule data pipelines. In addition, Workflow makes it easy to chain together multiple jobs to create a data pipeline.
• Seamless Collaborations: When data has been ingested and processed, data engineers may unlock its value by allowing every employee in the company to access and collaborate on data in real time. Data engineers can use this tool to view and analyze data. In addition, they can share datasets, forecasts, models, and notebooks while also ensuring a single consistent source of truth to ensure consistency and reliability across all workloads.

Pros

• Databricks Lakehouse Platform is easy to use and set up.
• It is a unified data management platform that includes data warehousing, ETL, and machine learning.
• End-to-end data preparation with data quality checking, cleansing, and enrichment is available.
• It is built on open source and open standards, which improves flexibility.
• The platform offers good customer support.

Cons

• The pricing structure is complex.

Pricing

Databricks Lakehouse Platform costs vary depending on your compute usage, cloud service provider, and geographical location. However, if you use your own cloud, you get a 14-day free trial from Databricks, and a lightweight free trial is also available through Databricks.

Datafold

As a data observability platform, Datafold helps businesses prevent data catastrophes. It has the unique capacity to detect, evaluate, and investigate data quality concerns before they impact productivity.

Datafold offers the ability to monitor data in real time to identify issues quickly and prevent them from becoming data catastrophes. It combines machine learning with AI to provide analytics with real-time insights, allowing data scientists to make top-quality predictions from large amounts of data.

Key Features

• One-Click Regression Testing for ETL: You can go from 0–100% test coverage of your data pipelines in a few hours. With automated regression testing across billions of rows, you can also see the impact of each code change.
• Data flow Visibility Across all Pipelines and BI Reports: Datafold makes it easy to see how data flows through your entire organization. By tracking data lineage, you can quickly identify issues and fix them before they cause problems downstream.
• SQL Query Conversion: With Datafold’s query conversion feature, you can take any SQL query and turn it into a data quality alert. This way, you can proactively monitor your data for issues and prevent them from becoming problems.
• Data Discovery: Datafold’s data discovery feature helps you understand your data to draw insights from it more easily. You can explore datasets, visualize data flows, and find hidden patterns with a few clicks.
• Multiple Integrations: Datafold integrates with all major data warehouses and frameworks such as Airflow, Databricks, dbt, Google Big Query, Snowflake, Amazon Redshift, and more.

Pros

• Datafold offers simple and intuitive UI and navigation with powerful features.
• The platform allows deep exploration of how tables and data assets relate.
• The visualizations are easy to understand.
• Data quality monitoring is flexible.
• Customer support is responsive.

Cons

• The integrations they support are relatively limited.
• The basic alerts functionality could benefit from more granular controls and destinations.

Pricing

Datafold offers two product tiers, Cloud and Enterprise, with pricing dependent on your data stack and integration complexity. Those interested in Datafold will need to book a call to obtain pricing information.

dbt

dbt is a transformation workflow that allows organizations to deploy analytics code in a short time frame via software engineering best practices such as modularity, portability, CI/CD (continuous integration and continuous delivery), and documentation.

dbt Core is an open-source command-line tool allowing anyone with a working knowledge of SQL to create high-quality data pipelines.

Key Features

• Simple SQL SELECT Statements: dbt uses simple SQL SELECT statements to define data models, which makes it easy for data analysts and data engineers to get started with dbt without learning a new language.
• Pre-Packaged and Custom Testing: dbt comes with pre-packaged tests for data quality, duplication, validity, and more. Additionally, users can create their own custom tests.
• In-App Scheduling, Logging, and Alerting: dbt has an inbuilt scheduler you can use to schedule data pipelines. Additionally, dbt automatically logs all data pipeline runs and generates alerts if there are any issues.
• Version Control and CI/CD: dbt integrates with Git to easily version and deploy data pipelines using CI/CD tools such as Jenkins and CircleCI.
• Multiple Adapters: It connects to and executes SQL against your database, warehouse, platform, or query engine by using a dedicated adapter for each technology. Most adapters are open source and free to use, just like dbt.

Pros

• dbt offers simple SQL syntax.
• Pre-packaged tests and alerts are available.
• The platform integrates with Git for easy deployment.

Cons

• The command-line tool can be challenging for data analysts who are not familiar with SQL.

Pricing

dbt offers three pricing plans:

• Developer: This is a free plan available for a single seat.
• Team: $50 per developer seat per month plus 50 read-only seats. This plan includes a 14-day free trial.
• Enterprise: Custom pricing based on the required features. Prospective customers can request a free demo.

Choosing DataOps Tools

Choosing a DataOps tool depends on your needs and preferences. But, as with anything else in technology, it’s essential to do your research and take advantage of free demos and trials before settling on something.

With plenty of great DataOps tools available on the market today, you’re sure to find one that fits your team’s needs and your budget.

Read next: Top Data Quality Tools & Software 2022

The post Top DataOps Tools 2022 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

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