But for purposes of this article, the term will be narrower. The focus will be on those operators that build platforms and tools to leverage data – one of the most important technologies in enterprises these days.

Yet even this category still has many companies. For example, if you do a search for data analytics on G2, you will see results for over 2,200 products.

So when coming up with a list of top data companies, it will be, well, imperfect. Regardless, there are companies that are really in a league of their own, from established names to fast-growing startups, publicly traded and privately held. Let’s take a look at 10 of them.

Also see out picks for Top Data Startups.

Databricks

In 2012, a group of computer scientists at the University of California, Berkeley, created the open source project, Apache Spark. The goal was to develop a distributed system for data over a cluster of machines.

From the start, the project saw lots of traction, as there was a huge demand for sophisticated applications like deep learning. The project’s founders would then go on to create a company called Databricks.

The platform combines a data warehouse and data lakes, which are natively in the cloud. This allows for much more powerful analytics and artificial intelligence applications. There are more than 7,000 paying customers, such as H&M Group, Regeneron and Shell. Last summer, the ARR (annual recurring revenue) hit $600 million.

About the same time, Databricks raised $1.6 billion in a Series H funding and the valuation was set at a stunning $38 billion. Some of the investors included Andreessen Horowitz, Franklin Templeton and T. Rowe Price Associates. An IPO is expected at some point, but even before the current tech stock downturn, the company seemed in no hurry to test the public markets.

We’ve included Databricks on our lists of the Top Data Lake Solutions, Top DataOps Tools and the Top Big Data Storage Products.

SAS

SAS (Statistical Analysis System), long a private company, is one of the pioneers of data analytics. The origins of the company actually go back to 1966 at North Carolina State University. Professors created a program that performed statistical functions using the IBM System/360 mainframe. But when government funding dried up, SAS would become a company.

It was certainly a good move. SAS would go on to become the gold standard for data analytics. Its platform allows for AI, machine learning, predictive analytics, risk management, data quality and fraud management.

Currently, there are 80,800 customers, which includes 88 of the Top 100 on the Fortune 500.  There are 11,764 employees and revenues hit $3.2 billion last year.

SAS is one of the world’s largest privately-held software companies. Last summer, SAS was in talks to sell to Broadcom for $15 billion to $20 billion. But the co-founders decided to stay independent and despite having remained private since the company’s 1976 founding, are planning an IPO by 2024.

It should surprise absolutely no one that SAS made our list of the top data analytics products.

Snowflake

Snowflake, which operates a cloud-based data platform, pulled off the largest IPO for a software company in late 2020. It raised a whopping $3.4 billion. The offering price was $120 and it surged to $254 on the first day of trading, bringing the market value to over $70 billion. Not bad for a company that was about eight years old.

Snowflake stock would eventually go above $350. But of course, with the plunge in tech stocks, the company’s stock price would also come under extreme pressure. It would hit a low of $110 a few weeks ago.

Despite all this, Snowflake continues to grow at a blistering pace. In the latest quarter, the company reported an 85% spike in revenues to $422.4 million and the net retention rate was an impressive 174%. The customer base, which was over 6,300, had 206 companies with capacity arrangements that led to more than $1 million in product revenue in the past 12 months.

Snowflake started as a data warehouse. But the company has since expanded on its offerings to include data lakes, cybersecurity, collaboration, and data science applications. Snowflake has also been moving into on-premises storage, such as querying S3-compatible systems without moving data.

Snowflake is actually in the early stages of the opportunity. According to its latest investor presentation, the total addressable market is about $248 billion.

Like Databricks, Snowflake made our lists of the best Data Lake, DataOps and Big Data Storage tools.

Splunk

Founded in 2003, Splunk is the pioneer in collecting and analyzing large amounts of machine-generated data. This makes it possible to create highly useful reports and dashboards.

A key to the success of Splunk is its vibrant ecosystem, which includes more than 2,400 partners. There is also a marketplace that has over 2,400 apps.

A good part of the focus for Splunk has been on cybersecurity. By using real-time log analysis, a company can detect outliers or unusual activities.

Yet the Splunk platform has shown success in many other categories. For example, the technology helps with cloud migration, application modernization, and IT modernization.

In March, Splunk announced a new CEO, Gary Steele. Prior to this, he was CEO of Proofpoint, a fast-growing cloud-based security company.

On Steele’s first earnings report, he said: “Splunk is a system of record that’s deeply embedded within customers’ businesses and provides the foundation for security and resilience so that they can innovate with speed and agility. All of this translated to a massive, untapped, unique opportunity, from which I believe we can drive long-term durable growth while progressively increasing operating margins and cash flow.”

Cloudera

While there is a secular change towards the cloud, the reality is that many large enterprises still have significant on-premises footprints. A key reason for this is compliance. There is a need to have much more control over data because of privacy requirements.

But there are other areas where data fragmentation is inevitable. This is the case for edge devices and streaming from third parties and partners.

For Cloudera – another one of our top data lake solutions – the company has built a platform that is for the hybrid data strategy. This means that customers can take full advantage of their data everywhere.

Holger Mueller at Constellation Research praises Cloudera’s reliance on the open source Apache Iceberg technology for the Cloudera Data Platform.

“Open source is key when it comes to most infrastructure-as-a-service and platform-as-a-service offerings, which is why Cloudera has decided to embrace Apache Iceberg,” Mueller said. “Cloudera could have gone down a proprietary path, but adopting Iceberg is a triple win. First and foremost, it’s a win for customers, who can store their very large analytical tables in a standards-based, open-source format, while being able to access them with a standard language. It’s also a win for Cloudera, as it provides a key feature on an accelerated timeline while supporting an open-source standard. Last, it’s a win for Apache, as it gets another vendor uptake.”

Last year, Cloudera reported revenues over $1 billion. Among its thousands of customers, they include over 400 governments, the top ten global telcos and nine of the top ten healthcare companies.

Also read: Top Artificial Intelligence (AI) Software for 2022

MongoDB

The founders of MongoDB were not from the database industry. Instead, they were pioneers of Internet ad networks. The team – which included Dwight Merriman, Eliot Horowitz and Kevin Ryan – created DoubleClick, which launched in 1996. As the company quickly grew, they had to create their own custom data stores and realized that traditional relational databases were not up to the job.  

There needed to be a new type of approach, which would scale and allow for quick innovation.  So when they left DoubleClick after selling the company to Google for $3.1 billion, they went on to develop their own database system. It was  based on an open source model and this allowed for quick distribution.

The underlying technology relied on a document model and was called NoSQL. It provided for a more flexible way for developers to code their applications. It was also optimized for enormous transactional workloads.

The MongoDB database has since been downloaded more than 265 million times. The company has also added the types of features required by enterprises, such as high performance and security.  

During the latest quarter, revenues hit $285.4 million, up 57% on a year-over-year basis. There are over 33,000 customers.

To keep up the growth, MongoDB is focused on taking market share away from the traditional players like Oracle, IBM and Microsoft. To this end, the company has built the Relational Migrator. It visually analyzes relational schemas and transforms them into NoSQL databases.

Confluent

When engineers Jay Kreps, Jun Rao and Neha Narkhede worked at LinkedIn, they had difficulties creating infrastructure that could handle data in real time. They evaluated off-the-shelf solutions but nothing was up to the job.

So the LinkedIn engineers created their own software platform. It was called Apache Kafka and it was open sourced. The software allowed for high-throughput, low latency data feeds.

From the start, Apache Kafka was popular. And the LinkedIn engineers saw an opportunity to build a company around this technology in 2014. They called it Confluent.

The open source strategy was certainly spot on. Over 70% of the Fortune 500 use Apache Kafka.

But Confluent has also been smart in building a thriving developer ecosystem. There are over 60,000 meet-up members across the globe. The result is that developers outside Confluent have continued to build connectors, new functions and patches.

In the most recent quarter, Confluent reported a 64% increase in revenues to $126 million.  There were also 791 customers with $100,000 or more in ARR (Annual Recurring revenue), up 41% on a year-over-year basis.

Datadog

Founded in 2010, Datadog started as an operator of a real-time unified data platform. But this certainly was not the last of its new applications.

The company has been an innovator – and has also been quite successful getting adoption for its technologies. The other categories Datadog has entered include infrastructure monitoring, application performance monitoring, log analysis, user experience monitoring, and security. The result is that the company is one of the top players in the fast-growing market for observability. 

Datadog’s software is not just for large enterprises. In fact, it is available for companies of any size.

Thus, it should be no surprise that Datadog has been a super-fast grower. In the latest quarter, revenues soared by 83% to $363 million. There were also about 2,250 customers with more than $100,000 in ARR, up from 1,406 a year ago.

A key success factor for Datadog has been its focus on breaking down data silos. This has meant much more visibility across organizations.  It has also allowed for better AI.

The opportunity for Datadog is still in the early stages. According to analysis from Gartner, spending on observability is expected to go from $38 billion in 2021 to $53 billion by 2025.

See the Top Observability Tools & Platforms

Fivetran

Traditional data integration tools rely on Extract, Transform and Load (ETL) tools. But this approach really does not handle modern challenges, such as the sprawl of cloud applications and storage.

What to do? Well, entrepreneurs George Fraser and Taylor Brown sought out to create a better way. In 2013, they cofounded Fivetran and got the backing of the famed Y Combinator program.

Interestingly enough, they originally built a tool for Business Intelligence (BI). But they quickly realized that the ETL market was ripe for disruption. 

In terms of the product development, the founders wanted to greatly simplify the configuration. The goal was to accelerate the time to value for analytics projects. Actually, they came up with the concept of zero configuration and maintenance. The vision for Fivetran is to make “business data as accessible as electricity.”

Last September, Fivetran announced a stunning round of $565 million in venture capital. The valuation was set at $5.6 billion and the investors included Andreessen Horowitz, General Catalyst, CEAS Investments, and Matrix Partners.

Tecton

Kevin Stumpf and Mike Del Balso met at Uber in 2016 and worked on the company’s AI platform, which was called Michelangelo ML. The technology allowed the company to scale thousands of models in production. Just some of the use cases included fraud detection, arrival predictions and real-time pricing.

This was based on the first feature store. It allowed for quickly spinning up ML features that were based on complex data structures.

However, this technology still relied on a large staff of data engineers and scientists. In other words, a feature store was mostly for the mega tech operators.

But Stumpf and Del Balso thought there was an opportunity to democratize the technology. This became the focus of their startup, Tecton, which they launched in 2019.

The platform has gone through various iterations. Currently, it is essentially a platform to manage the complete lifecycle of ML features. The system handles storing, sharing and reusing feature store capabilities. This allows for the automation of pipelines for batch, streaming and real-time data.

In July, Tecton announced a Series C funding round for $100 million. The lead investor was Kleiner Perkins. There was also participation from Snowflake and Databricks.

Read next: 5 Top VCs For Data Startups

The post 10 Top Data Companies appeared first on IT Business Edge.

While it is not an uncommon occurrence for emerging concepts to change during their formative development over time, the lack of clarity can create confusion for those in need of the technology. Here we’ll discuss how data fabrics are evolving – and how they can help distributed enterprises better manage their far-flung data operations.

See the Top 7 Data Management Trends to Watch in 2022

What is a Data Fabric?

In a 2018 talk by NetApp’s data fabric chief architect, Eiki Hrafnsson, he outlined the Data Fabric 1.0 vision as “essentially being able to move your data anywhere; whether it’s on-prem, the enterprise data center, or to the public cloud.”

In a theatrical and entertaining tech demo, NetApp engineers debuted this technology in 2015 by rapidly transferring 10GB of encrypted data between AWS and Azure cloud, all from a simple drag-and-drop interface.

This addressed a real change toward fluid data transfer between mediums, something like a storage network for the Big Data and cloud era. However, years later, this kind of performance is now generally expected, causing a shift in the development of the data fabric and what it could be used for.

According to Gartner, a data fabric is:

“ … a design concept that serves as an integrated layer (fabric) of data and connecting processes. A data fabric utilizes continuous analytics over existing, discoverable, and inferenced metadata assets to support the design, deployment, and utilization of integrated and reusable data across all environments, including hybrid and multicloud platforms.”

Comparatively, IBM defines a data fabric as:

“ … an architectural approach to simplify data access in an organization to facilitate self-service data consumption. This architecture is agnostic to data environments, processes, utility, and geography, all while integrating end-to-end data-management capabilities. A data fabric automates data discovery, governance, and consumption, enabling enterprises to use data to maximize their value chain.”

While both definitions borrow from the original concept, the idea of what a data fabric is has become more complex in order to keep up with current data trends.

Also read: Enterprise Storage Trends to Watch in 2022

Data Fabric 2.0

NetApp reassessed their idea of the data fabric in the years following its debut, redefining the concept thusly: “The NetApp Data Fabric simplifies the integration and orchestration of data for applications and analytics in clouds, across clouds, and on-premises to accelerate digital transformation.”

In other words, the scope and functionality expanded to better integrate existing enterprise applications with data sources, making the programs agnostic to the source media.

NetApp claims this fabric architecture carries numerous benefits:

• It creates a better posture to resist vendor lock-in by liberating data and offering the freedom of choice between cloud providers or on-premises, switching at any time you like.
• It empowers data management, increases mobility by knocking down silos, facilitates cloud-based backup and recovery, and may also improve data governance, the company says.
• Data fabrics enhance data discovery by granting full-stack visibility with their suite of visualization tools.

Other companies such as Talend have their own data fabric analytical tools, many of which extend the fabric to both internal and external consumers and contributors through the use of APIs.

Data Fabric Challenges

Most companies today house their data in multiple locations and in a variety of formats; therefore, data fabrics can’t always have access to all data. Moreover, the distributed nature of the data often leads to poor data quality, which can skew data analysis when aggregated.

According to a study in the Harvard Business Review, a mere 3% of companies’ data adhere to the study’s standard of data quality. The study also found that nearly half of all newly-created records contain a critical error.

According to Talend, creating a unified data environment can alleviate these quality control issues by giving IT greater control and flexibility over the end product. Their tools, the company says, build better data stewardship, more effective data cleansing, and better compliance and integrity through data lineage tracing.

Data Fabrics and Data Management

Tools like data fabrics can make the job of data quality control easier, but if they’re wielded incorrectly, then the company may find itself spending more to make up for the issues with data or analyses.

How we interact with our data is only half the bigger picture. The other half is how we create it. Data tends to be created on the fly, and to serve a limited, time-sensitive purpose. A data fabric can help IT wrangle bad or outdated data more quickly, but ideally, we should also be mitigating these issues on the front end as data is created.

If you’re curious to see a demonstration of data fabric tools to see how you might leverage them in your company, check out this hour long talk from NetApp’s chief data fabric architect.

Read next: Top Big Data Storage Tools

The post Unifying Data Management with Data Fabrics appeared first on IT Business Edge.

Big Data Requires Big Storage

Big Data is an all-encompassing term that refers to large and complex sets of unstructured, semi-structured, and structured data that cannot be processed by traditional data-processing software. These datasets are generated from numerous resources, such as large-scale e-commerce, medical records, image and video archives, and purchase transaction records.

Big Data analysis may reveal associations, trends, and patterns, especially relating to human interactions and behavior. Numerous specially-designed hardware and software tools are available today for Big Data analysis.

The extraction of meaningful insights from Big Data may aid in making critical business growth decisions, such as exploring new, underexplored market themes or the betterment of an existing product or service. Hence, much information technology (IT) investment is going towards maintaining and managing Big Data.

In fact, the Big Data industry is projected to be worth a hefty $77 billion by 2023. To make sense of Big Data, though, the first step is acquiring a Big Data storage tool.

Also read: Best Big Data Tools & Software for Analytics

Why You Need a Big Data Storage Tool

More than 150 zettabytes of data will require analysis by 2025. An organization can only harness the power of Big Data if they have a secure storage solution that can massively scale to meet the Big Data challenge. Big Data storage tools collect and manage Big Data and enable real-time data analysis.

Generally, Big Data storage architecture falls into the following categories:

• Geographically distributed server nodes such as the Apache Hadoop model
• Database frameworks such as not only SQL (NoSQL)
• Scale-out network-attached storage (NAS)
• Storage area networks (SAN)
• Solid-state drive (SSD) arrays
• Object-based storage
• Data lakes (raw data storage)
• Data warehouses (processed data storage)

Also read: Best Data Warehouse Software & Tools

Best Big Data Storage Tools

Here, in our analysis and review, are the best Big Data storage tools that are on the market today.

Apache Hadoop

Apache Hadoop is an open-source software library that enables the distributed processing of large and complex datasets across clusters of computers (called nodes) using easy programming models. The framework is designed to scale to thousands of nodes, each offering local computation and storage.

Key Differentiators

• Apache Hadoop is designed to detect and take care of failures at the application layer, thereby delivering a highly available service on top of computer clusters, each of which may be vulnerable to failures.
• Apache Hadoop includes these modules: Hadoop Common, Hadoop Distributed File System (HDFS), Hadoop Yet Another Resource Negotiator (YARN), and Hadoop MapReduce.
• Hadoop Common refers to the common utilities and libraries that support the other Hadoop modules.
• HDFS provides high-throughput access to large and complex datasets running on commodity hardware. HDFS is used to scale a single node to thousands of nodes.
• The goals of HDFS include quick recovery from hardware failures, access to streaming data, accommodation of large and complex datasets, and portability.
• Hadoop YARN is a parallel processing framework for job scheduling/monitoring and cluster resource management.
• Hadoop MapReduce is a YARN-based system for the parallel processing of large and complex datasets.
• Hadoop-related projects at Apache include ZooKeeper, Tez, Submarine, Spark, Pig, Ozone, Mahout, Hive, HBase, Chukwa, Cassandra, Avro, and Ambari.

Pricing: Apache Hadoop is available for free.

Apache HBase

Apache HBase is an open-source, distributed, versioned, NoSQL database that is modeled after Google’s Bigtable. It provides capabilities similar to Bigtable on top of Apache Hadoop and HDFS.

Key Differentiators

• The goal of Apache HBase is to host large and complex tables (billions of rows and millions of columns) atop clusters of commodity hardware.
• HBase offers both modular and linear scalability.
• HBase provides strictly uniform reads and writes.
• Shards of tables are configurable and automatic.
• Failure support between RegionServers is automatic.
• A simple-to-use Java application programming interface (API) is available for client access.
• BlockCache and Bloom Filters are available for real-time querying.
• Server-side filters facilitate query predicate pushdown.
• Apache Thrift software framework and a RESTful web service supports Protobuf, eXtensible Markup Language (XML), and binary data encoding options.
• Extensible JRuby-based (JIRB) shell support is available.

Pricing: Apache HBase is available for free.

NetApp Scale-out NAS

NetApp is a pioneer in the NAS industry. NetApp Scale-out NAS simplifies data management and helps you keep pace with growth while keeping costs down. The Big Data tool hands you seamless scalability, proven efficiency, and non-disruptive operations within a unified architecture.

Key Differentiators

• NetApp Scale-out NAS is powered by NetApp ONTAP enterprise data management software.
• Users can automatically tier cold data to private or public cloud with StorageGrid to maximize capacity on performance tiers.
• Cloud tier and performance can be combined into one data pool, thereby reducing the total cost of ownership (TCO).
• Data can be accessed at the edge and across multiple data centers and all major public clouds with integrated caching capabilities.
• Active IQ uses artificial intelligence for IT operations (AIOps) to automate the proactive optimization and care of NetApp environments.
• Users can dedupe and compress storage without performance impact.
• With built-in data security, users can safeguard sensitive customer and company information.
• Users can encrypt data-in-transit and data at the volume level as well as securely purge files.

Pricing: Reach out to sales for product pricing.

Snowflake for Data Lake Analytics

Snowflake’s cross-cloud platform provides quick, reliable, and secure access to all your data. Snowflake for Data Lake Analytics combines unstructured, semi-structured, and structured data of any format; provides rapid and reliable processing and querying; and enables secure collaboration.

Here is how Snowflake for Data Lake Analytics enables your data lake:

Key Differentiators

• Large and complex sets of data can be stored in Snowflake-managed storage with encryption at rest and in transit, automatic micro-partitioning, and efficient compression.
• You can support numerous workloads on unstructured, semi-structured, and structured data with your language of choice (Scala, Python, or Java), on a single platform.
• With Snowflake’s elastic processing engine, pipelines can be run for low maintenance, cost savings, and reliable performance.
• Pipeline development can be streamlined using your language of choice (SQL, Scala, Python, or Java) with Snowpark–no additional copies of your data, service, or clusters to manage.
• An unlimited number of concurrent queries and users can be supported with nearly unlimited, dedicated compute resources.
• With built-in Access History, you can know who is accessing what data.
• Snowflake enables collaboration among stakeholders and enriches your data lake with secure, live data sharing.
• With scalable, row-based access policies, you can enforce row and column-level security across clouds.

Pricing: A 30-day free trial includes $400 worth of free usage. Reach out to the Snowflake sales team for product pricing information.

Also read: 8 Top Data Startups

Databricks Lakehouse Platform

Databricks Lakehouse Platform combines the best of data lakes and data warehouses. The Big Data storage tool delivers the performance, strong governance, and reliability of data warehouses as well as the machine learning (ML) support, flexibility, and openness of data lakes.

Key Differentiators

• Databricks Lakehouse Platform is from the original creators of Koalas, MLflow, Delta Lake, and Apache Spark.
• You can unify your data warehousing and AI use cases on a single platform.
• The unified approach eliminates the silos that traditionally separate ML, data science, business intelligence (BI), and analytics.
• The Big Data tool is built on open-source and open standards to maximize flexibility.
• Databricks Lakehouse Platform’s common approach to data governance, security, and management helps you innovate quicker and operate more efficiently.
• Databricks Lakehouse Platform has over 450 partners across the data landscape, including MongoDB, Tableau, RStudio, and Qlik.
• The Big Data solution provides an environment for data teams to build solutions together.

Pricing: Fill out a simple form to enjoy a 14-day full trial. Contact the Databricks sales team for product pricing details.

Choosing a Big Data Storage Tool

The Big Data industry is ever-growing and powers numerous business-oriented applications. Tech giants such as Google and Facebook, for example, harness the potential of Big Data to serve targeted advertising and content to users. The first step to analyzing Big Data is securely storing it.We’ve covered some of the biggest solutions in this article, but others are also worth a look. Object storage is something every serious enterprise should be familiar with by now, and it’s also available in the cloud as a service from Amazon, Google, IBM and others. Do your own research and find a Big Data storage solution that best meets the needs of your organization.

Read next: Enterprise Storage Trends to Watch in 2022

The post Top Big Data Storage Tools 2022 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.

What is Backup Software and Why is It Used?

Backup software is a storage application that creates and stores copies of digital files, folders, software, and even a device or server as a whole. The software stores the files in a separate location from the originals, providing seamless access if the original files are lost or stolen. Typically cloud-based, it stores the copies in the cloud, making them accessible from anywhere in case a natural disaster wipes out the office.

Backup software is used to prevent data loss in case of natural disasters, hardware crashes, or ransomware attacks. Businesses get a quick way to restore their files without paying a ransom or trying to evacuate a server. Backup software can offer either manual or automatic backups, but businesses should choose an automated backup system, so they don’t lose any critical data.

How Does Backup Software Work?

Businesses install data backup software on their computers, mobile devices, and servers where it copies the data from those devices and sends it to an external storage facility using a secure internet connection. If the company experiences data loss, they can log into the backup system to initiate the restoration process and retrieve their files. 

Some businesses want to have backup software that offers both on-premises and cloud backups, so they can have their files stored in more than one location.

Also read: Make Incident Response Planning Easy with this Free Template

Key Features of Backup Software

Backup software should make it easy for businesses to recover their files during an incident. These features can facilitate recovery and keep files secure at rest or in transit.

Self-Service Backup and Recovery

Organizations need self-service options in their backup software in order to access their files faster. They shouldn’t have to go through the vendor to get access to their files, especially if the vendor doesn’t offer 24/7 support. Additionally, backup software that doesn’t run automatic backups should allow users to decide when and which files to backup. 

Flexible Storage Options

Backup software should also offer flexible storage options, allowing businesses to back up their data in multiple locations. There should be both cloud and on-premise storage available, giving businesses several ways to retrieve their data in case of an emergency.

Automatic Discovery

Automatic discovery keeps businesses from having to remember what data they’ve already backed up. Instead, the backup software runs continuously, adding new files to backups as the organization creates them.

Also read: Successful Cloud Migration with Automated Discovery Tools

Compliance

Businesses store a lot of sensitive data about their customers and employees, and their backup solutions should keep them compliant with regulatory requirements. Depending on the type of data the company stores, they may be susceptible to GDPR, PCI DSS, or SOC guidelines.

Best Backup Software

Organizations looking for backup software should consider one of the following options, chosen for their high user ratings, flexible options, and critical features.

Acronis Advanced Backup

Acronis Advanced Backup combines cloud backup with cybersecurity to keep data safe during transit and while it’s in storage. Anti-malware and antivirus protection keeps files clean, and real-time backups prevent data loss. The system protects over 20 types of workloads, including Windows, Mac, Google, and Linux, and organizations can backup text files, images, and full applications. Plus, granular recovery options allow businesses to only recover the files they need. Interested organizations will have to contact Acronis for pricing information.

Key Features

Arcserve Backup

Arcserve Backup provides magnetic tape backup and recovery to lower an organization’s total cost of ownership and provide longer retention periods. The platform is scalable, letting businesses start small and increase storage as necessary. Organizations can monitor the status of their backups in real time, get full visibility into their environments, and quickly restore data with granular recovery options. The software works well with distributed and multiplatform environments. Pricing information is not available on the Arcserve website.

Key Features

Paragon Backup & Recovery Business

Paragon Backup & Recovery Business works best for small and medium-sized businesses with easy deployment and management and automated workflows. It includes built-in security features to protect data from unauthorized access. It does only support Windows-based workloads, so organizations using Mac, Linux, or other options should look at other platforms. Businesses can choose physical or virtual storage for their backups. Pricing information is not available on the website.

Key Features

Veritas System Recovery

Veritas System Recovery backs up desktops, laptops, and servers to protect all of the information on an organization’s network. The single management console makes it easy to back up and restore data without consuming a large amount of storage capacity. The system automatically duplicates backups in the cloud and makes it easy to restore data to any hardware environment. Cloud storage on Amazon S3 or Microsoft Azure provides secure supplements to secondary storage locations. Veritas does not include pricing information on the website.

Key Features

NTI Backup Now

NTI Backup Now provides backup solutions for office PCs running on Windows operating systems. With remote backup and cloud storage, it works well for small businesses and managed services providers (MSPs). The File Backup feature supports audio, videos, photos, documents, and emails, while the Drive Backup duplicates the entire PC. The licenses are billed per device, but companies can get discounts when they purchase more than one license at a time.

Key Features

AOMEI Backupper Professional

AOMEI Backupper Professional includes file, disk, system, and partition backups for Windows PCs. The real-time sync feature automatically backs up new files to prevent data loss, and two-way sync ensures that on-premises and cloud locations contain the same files. Organizations can schedule backups, restore data to new hardware after a crash, and get notifications about backup statuses. There are two types of licenses available for business use, and each license covers one device. Organizations can get discounts for buying more than one license at a time.

Key Features

NovaBACKUP

NovaBACKUP offers PC or cloud backups for small businesses and MSPs. The single management interface makes it easy to view all devices or clients quickly, ensuring all of the data is backed up. The US-based support team makes it easy for clients to get answers to their questions quickly, usually in less than one day. The platform is secure, encrypted, and follows several compliance regulations, including HIPAA. There are several different licenses to choose from, but PC licensing is priced per device with discounts for multiple licenses available.

Key Features

EaseUS Todo Backup Enterprise

EaseUS Todo Backup Enterprise offers reliable data protection for businesses of any size. Users can schedule automatic backups and instantly restore data after an incident. With support for PCs, servers, and databases, organizations can get automated, continuous protection for new and existing assets. Plus, the central management console makes it easy to review backup statuses. Organizations can choose between Workstation, Server, Advanced Server, or Technician licenses, and pricing will depend on the type and number of licenses.

Key Features

Choosing the Best Backup Solution for Your Business

Choosing a backup solution for your business depends on the type of devices your organization uses. For example, many backup solutions don’t support macOS or iOS devices. If your business keeps critical information on those types of devices, you’ll need backup software that can support them. 

You’ll also need to determine how many devices you need to backup since so many vendors price their products per device. Take advantage of free trials when possible, and talk to support during those trials to make sure the solution will work for you before signing a contract or buying a full license.

Read next: Cyber Incident Response Planning & Guide

Some of the products that appear on this site are from companies from which IT Business Edge receives compensation.

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Flash Forward

Market research firm Mordor Intelligence projects that the enterprise flash storage industry will continue to grow in 2022 and the years beyond. The flash market is seeing its fastest growth in India and China, as the countries modernize their internet infrastructure and cloud storage capabilities. However, the largest share is still in the United States. Pushing this growth in the U.S. is the increase in enterprise mission-critical, cloud-based softwares with frequent read/write operations.

Beyond performance advantages, flash reduces power consumption costs in data centers, though the cost/gigabyte is still comparably high against HDDs. The price of SSDs continues to decline, as the technology becomes more reliable and drive capacities expand. By contrast, the HDD market share has shrunk from its $34 billion peak in 2014 and is expected to plummet to $12 billion by next year.

Multiclouds

Investments in cloud-computing infrastructure rose 32% in the last year, reaching $59 billion according to a report from industry analyst firm Gartner. By the end of 2022, that number is anticipated to exceed the $100 billion mark. But where the industry was once dominated by AWS, now enterprises are diversifying their cloud storage contracts across several vendors, creating a multi-cloud storage solution that takes advantage of cherry-picked offerings from different contracts. 

The Wallstreet Journal reported earlier this year that companies like Experian are seeking multi-cloud solutions from AWS, Microsoft, Google, and Oracle simultaneously. The same path has been followed by federal agencies, such as the CIA and the Pentagon, bringing an end to a planned $10 billion, 10-year exclusive contract with Microsoft for cloud storage. However, cloud providers are not stagnating in their offerings. Instead, some, such as Microsoft and Oracle, are pairing up, enabling their customers to seamlessly implement a multi-cloud solution that takes advantage of Azure and Oracle Cloud at the same time.

Read more: Managing Security Across MultiCloud Environments

Containerization

The Google-developed, now open-source project Kubernetes has resulted in software that manages containerized applications while orchestrating them across multiple environments. A Gartner study projects that 75% of companies will be running containerized applications by 2022, putting stresses on the storage industry to implement flexible and persistent storage solutions to accommodate Kubernetes—even across multi-clouds. 

While containerized applications are highly portable, there are challenges with maintaining consistency when, for instance, an application needs to reference a database. As the application moves, each new instance needs to be directed to its data. Companies like NetApp are developing solutions to manage containerized applications to create persistent data connectivity across multiple storage locations, removing the seams between public cloud and on-premises workloads.

Also read: Top Container Software & Orchestration Tools

Artificial Intelligence (AI)

The big players in the storage world have seen great success with implementing AI in their operations. Back in 2016, Google put its DeepMind AI to use, yielding a 40% reduction in energy expended on data center cooling. German manufacturer Siemens caught up with its own AI thermal management program last year, bringing down cooling costs that typically make up 30% of its data center’s energy consumption. 

Google later extended its AI to create weather forecasting models, projecting the thermal impacts of outside temperatures as well as fluctuations in wind and solar power asset outputs. Machine learning-based AI can process millions of data points each second, accrued from sensors placed across the data center, and can make adjustments more rapidly than a human operator can.

However, these are proprietary solutions, leaving smaller data centers on their own for building AI in-house. There’s plenty of room for growth in this field, but rising energy futures will put pressure on data centers to bring down electricity costs.

Network as a Service

Finally, pandemic conditions have accelerated change in how enterprises connect with their data storage. In its 2022 Global Networking Report, Cisco projects a near-term growth in the adoption of the Network-as-a-Service (NaaS) market, though not without some hesitation from IT professionals. Under a NaaS model, businesses of all sizes lease networking hardware such as switches and routers, providing rapid scalability and on-demand pricing. 

As network boundaries become more ambiguous and the paths between enterprise data and its consumers shift, the service-based approach gives companies the flexibility to adapt to changing landscapes without burdensome capital expenditures. This further alleviates the costs of maintaining and upgrading networking equipment, but the Cisco survey of over 1,500 IT pros finds that many are still wary of losing control over hardware.

“Understandably, organizations have a multitude of technologies and investments, many of which fall on different depreciation schedules,” the report says. “Other organizations have legacy technologies and applications that may not be a good fit for NaaS. 

“And some simply don’t want to offload the day-to-day management of their infrastructure.”

The Way Forward

These are just a handful of the trends moving in the data storage realm. Other developments worth watching include novel approaches to data center sustainability, further moves toward edge computing, improvements in hardware-level security, and the growth of computational storage. 

Read next: Best MSP Software: Managed Service Provider Tools

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At the same time, AWS has enhanced the S3 cloud storage service based on an object storage system to make it easier to deploy applications that span multiple regions. The cloud service provider has also improved upload/download performance for the S3 service by up to 60% and revamped the Amazon S3 Intelligent Tiering service to remove the minimum duration requirement for all objects stored and removed fees for objects smaller than 128 KB.

AWS has added an Amazon Elastic File System Intelligent Tiering capability that makes it easier for customers to optimize costs for storing data on different tiers of services when access patterns for data change and an AWS Transfer Managed Workflows feature that reduces the manual tasks required for preprocessing data for file transfers

Finally, an audit manager tool for AWS Backup Audit Manager and support for 64TB snapshots when employing the Amazon Elastic Block Store (EBS) service.

Providing Managed Services

When it comes to storage, AWS is best known for its S3 service. The cloud service provider has been working closely with NetApp to enable files to be stored on its cloud. The managed service now extends that effort to include a managed service through which the NetApp ONTAP file system is managed by AWS on behalf of customers. The goal is to make it easier for organizations to shift applications that depend on accessing files to the cloud using NetApp ONTAP, the open source Lustre file system, or the Windows file system, says Edward Naim, general manager, Amazon FSx.

File systems are widely employed in on-premises IT environments and in many ways make it easier to organize data than an object storage system that typically scales higher. “File systems have some advantages over object,” says Naim.  

In general, NetApp remains committed to providing access to ONTAP regardless of where IT teams store files, says Anthony Lye, executive vice president and general manager for public cloud at NetApp.

Also read: Edge AI: The Future of Artificial Intelligence and Edge Computing

Use Cases for OnTAP

In addition to on-premises IT environments that deploy NetApp storage systems, the ONTAP file system can be deployed in the cloud or at the network edge to store data as close as possible to the applications that consume that data via the NetApp application programming interface (API).

Use cases will span everything from intelligent tiering and cloud bursting from on-premises IT environments to the building of data fabrics spanning a heterogeneous IT environment, adds Lye. “Anything that writes to the NetApp API will be compatible,” he says.

Most new cloud applications are employing object storage, but the bulk of existing applications that are being lifted and shifted to the cloud require access to a file system such as NetApp ONTAP. It’s not clear yet if object storage will one day eliminate the need for file systems. However, if that day were to arrive it’s still years away. The challenge now is finding the best way to manage all of the different classes of storage services that applications now routinely employ in a way that best preserves the sanity of the IT organization.

Read next: Implementing IT Service Management Changes with CloudOps

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Virtual Data Rooms (VDRs) or “Deal Rooms” are areas where meetings can be conducted in a controlled setting. Only select individuals can gain access to them, which controls the spread of the information exchanged within. This is the new way to conduct business and is being utilized and innovated more and more frequently. It shouldn’t be neglected by any business that uses disclosure agreements or brokers deals out of public view.

Team leaders and vendors alike should make room in their infrastructure or plans for expansion for at least one VDR solution. It’s the long-distance alternative to the board room meeting or the secret lounge in the penthouse suite. It’s where the business deals of the future are done today.

What is a Virtual Data Room?

The specifics of a VDR come down to the technology behind them and their primary usage. VDRs are cloud computing solutions to private, clandestine data deals that use secured, encrypted access that can only be given by the main user — the owner — to various clients and partners. This privileged access-only parameter makes it more secure than email, which is shared with multiple recipients and with the storage server. What happens in a VDR only exists within that VDR and isn’t stored anywhere.

These services offer real-time data exchange in various forms. Some come with integrated video chatting or audio chatting, which allows clients to connect directly and effortlessly using their own devices. VDRs also take and dispense data in the form of documents that must only be opened by specific individuals. Once the VDR is closed down, the cloud service wipes it out, leaving nothing behind; reducing the risk of essential contracts being intercepted or leaked.

The VDR can’t be entered without permission and permission is only sent by the owner. Even the vendor, who distributes the software solution and hosts the VDR on their server, won’t be allowed in or know what’s going on. It’s for trusted eyes only.

Also read: Mainframes Still Matter in the Digital Business Transformation Age

Why Virtual Data Rooms are Relevant

Any major business has had at least one “email scare”, a message meant for one person sent to another, or to many, with a single misclick. Some of these are harmless memorandums or personal messages. Embarrassing, but not risky. Some of these emails have compromised major corporations in ways that they still haven’t recovered from yet.

Security is key. Leaks are happening more often than ever. Even major corporations keep losing secrets through their employees, or more likely, through interception. Email passwords can be guessed, or brute-forced with enough effort to let strangers into the intranet.

VDRs exist on an extranet. They are hosted by a third party and only exist temporarily. No information is stored that can be leaked. It all goes to the eyes that were invited to see it. This makes it essential in financial dealings, acquisitions, mergers, and other major business decisions that can’t go public without careful preparation.

VDRs are the internet equivalent of inviting clients over to a room with no windows, an airtight door, and no running electricity for cameras or wires to work in secret. They are digital vaults where information comes, goes, and are then sealed off completely.

Why Should You Consider VDRs?

VDRs offer an attractive proposition to your client. It allows them to engage in highly private deals and crucial, of-the-moment sales decisions from any place they feel safe. They can be at home, in their own office, or far away from anyone who could leak information — essentially allowing them to  conduct their meeting with their safety guaranteed. It eliminates the need to host a physical space and exchange printed, written data in the form of folders or documents. Those can be sent digitally and read on a device of the client’s choosing.

The main benefit is safety. The extra benefit is the long-range potential it offers. Proper use of VDRs to set up appointments and meetings can, for example,  turn a local business into a regional one as they can invite new clients from all over to engage with them fairly and equally.

Using a VDR shows compliance with rising standards for security and communication. They are easy, straightforward, and give a secure place for a team to gather data and information together without using up all of the free space on their own computers or servers. It can help a team within a company or help gather a team of clients from outside.

Also read: 10 Ways Companies Screw Up Their Cyber Investigations

What Businesses Should Use a VDR

Any business that deals with confidential data should use one to handle the transactions in a secure way. This includes financial institutions that need to go over sensitive client information without including any outside members in the meeting. IPOs and real estate asset management are traditionally upheld through VDRs.

Tech companies can also make use of them to share and go over code or sensitive data that is essential to operations. Only the right eyes will see it and only the right hands will manage it. The same goes for clients who are trusting their precious code to only the most qualified professionals within an organization. Code isn’t something that can be printed and brought over in a folder. It belongs on a computer and must be shared between computers.

Any business that manages data in documents, legal firms, or financial advisors, can make use of a VDR. Those are documents that need to be always handled and controlled privately, and that privacy is hard to keep when they’re on a server that other people can access. Consider a VDR the lock on the file cabinet to keep all those folders and documents safely in your control.

The Future of VDRs

As long as there are security concerns, VDRs will continue to improve to meet those concerns. Security is the main struggle that the VDR seeks to overcome. There are many ways to send large pieces of data quickly between users, but one wrong move could send that data to hundreds who shouldn’t receive it.

The future of VDRs will be born through competition to see which service offers the most security and additional features. Chatting functions will become increasingly common so important meetings won’t have to be held on more public platforms like Zoom, which often suffer from connection issues and lack security functions.

VDRs will also become the future for many companies. Anyone who considers having private meetings to go over private documents can do that with a VDR without leaving their office or even their home. Having that much control over an environment will be a very attractive prospect for many clients as well who don’t want to or can’t leave their home but must conduct business regardless.

Virtual Data Rooms are set to become a standard of modern business and they will only improve with time.

Read next: Detecting Vulnerabilities in Cloud-Native Architectures

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The io2 Block Express service will play a critical role in convincing organizations to finally move transaction processing applications that still largely run in on-premises IT environments to the cloud, says Mai-Lan Tomsen Bukovec, vice president for block and object storage at AWS.

Based on the Amazon Elastic Block Service (EBS) Block Express architecture, the io2 Block Express enables applications to take advantage of AWS storage and networking services to deliver sub-second latency and 99.999% availability to database applications that require access to block storage.

Most of those applications are running on relational databases that many organizations have resisted moving to the cloud because block storage services could not provide the level of IOPs throughput required. 

The io2 Block Express is being made available initially on R5b instances on the Amazon EC2 cloud that enables an IT team to provision a single volume with up to 256,000 IOPS, 4000 MB/s of throughput, and storage capacity of 64 TiB. AWS plans to make additional classes of ECS instances available as well. 

The io2 Block Express volumes are available in all regions where R5b is available, including US East (Ohio), US East (N. Virginia), US West (Oregon), Asia Pacific (Singapore), Asia Pacific (Tokyo), and Europe (Frankfurt), with support for more regions planned.

Also read: Successful Cloud Migration with Automated Discovery Tools

Dynamic Scaling with io2

AWS is betting many organizations do not want to acquire, provision, manage and upgrade SANs. Every time an IT organization runs out of capacity someone in an IT organization has to provision additional storage and then reconfigure the network, notes Tomsen Bukovec. In contrast, the io2 Block Express cloud service is designed to dynamically scale as required. “We already have a massive network in place,” she says.

Many organizations continue to operate their own data centers because they need to run latency-sensitive online transaction processing (OLTP) applications. OLTP applications often tend to have spikes that require IT teams to over provision infrastructure to handle, for example, seasonal increases in sales. 

The io2 Block Express cloud service removes the technical hurdles that previously prevented many organizations from closing down their data centers, says Tomsen Bukovec. Organizations that continue to run those applications in an on-premises IT environment will be doing so because of their own internal culture, she adds.

OLTP vs. On-Premises

It’s not clear to what degree other cloud service providers will make SANs available on their platform, but it’s apparent that many organizations will need to reevaluate the cost of deploying OLTP applications in the cloud versus an on-premises IT environment. The longer an application needs to run the more cost effective it tends to be to run it in on-premises applications. 

However, it’s apparent that providers of IT infrastructure optimized for OLTP applications are going to have to fend off increased competition from cloud service providers that have finally cracked the code on SAN services. 

Read next: Detecting Vulnerabilities in Cloud-Native Architectures

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As one of the largest providers of drives to both cloud storage providers and manufacturers, Seagate has been somewhat circumspect when it comes to potentially competing against IT vendors that are among its biggest customers. The long-time manufacturer of magnetic and solid-state drives (SSDs) is now putting aside that timidity to unfurl a cloud storage service that in addition to being as accessible as any other cloud storage service, has reduced costs by eliminating egress fees.

Also read: Is Serverless Computing Ready to Go Mainstream?

Lyve Cloud’s Proposition

Cloud service providers such as Amazon Web Services (AWS) make it relatively inexpensive to store data. However, when an IT organization seeks to move that data to another platform the fees that are applied by cloud service providers border on the exorbitant.

Seagate is carefully positioning its cloud service. The Lyve Cloud service is intended to be complementary to offerings provided by other cloud service providers and on-premises platforms provided by storage system vendors, said Rags Srinivasan, senior director of global marketing for Seagate.

Seagate is primarily focused on secondary data that is typically stored via a backup and recovery platform. As part of the launch of Lyve Cloud Seagate has enlisted support from Commvault, which will integrate its data protection platforms with the Seagate cloud service.

Lyve Cloud provides always-on encryption for data in motion and at rest and is already ISO27001 and SOC2 certified. The goal is to make data stored in an object-based storage system readily accessible to a wide range of other applications that don’t need to access data in milliseconds, said Srinivasan. The use cases involving Lyve Cloud  will ultimately span exabytes of data.

“The market is so huge,” said Srinivasan. “It’s not about displacement.”  

Seagate has partnered with Equinix to provide access to its cloud storage service via a network of existing data centers in the U.S.  to minimize overall latency. The plan is to eventually expand the reach of the Lyve Cloud service around the globe.

Also read: Best Cloud Migration Tools

Seagate’s Opening Gambit

It’s not likely Seagate will usurp AWS. Microsoft and Google as the dominant provider of cloud storage services any time soon. However, the mere existence of Lyve Cloud will act, at the very least, as governor on cloud storage services that tend to have a lot of costs hidden in the terms of conditions of a service agreement.

In the meantime, IT teams would be well advised to reevaluate what type of data needs to be stored in the cloud at what total cost. As more organizations start to routinely store petabytes of data in the cloud to, for example, train artificial intelligence (AI) models. In fact, lower cloud storage costs will play a critical role in eventually democratizing AI to the point  that it becomes affordable for a wider swath of organizations to employ.

Whatever the motivation for storing data in the cloud, however, the most important thing organizations should remember is it’s their data. Any cloud service provider that makes it costly to reuse that data on another platform using what amounts to an exit fee may not always be putting the best interest of the customer first.

Read next: Best Practices for Effective Cloud Control and Cost Management

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