HPE Ezmeral: Uncut

What is data fabric? Why do you need it for data analytics?

HPE-Ezmeral-Data-Fabric-Holy-Grail.pngA recent article in CIO.com caught my attention: Data fabric: The holy grail of business insights. Since the phrase holy grail is used to connote a highly sought-after objective, I wondered why data fabric deserves such high praise. A summary of the article follows—with a few of my own insights thrown in.

The author, Tom Armstrong, Senior Technical Marketing Engineer at HPE, explains that data analytics is a core business function. It’s helping organizations obtain valuable insights that improve processes, increase efficiency, and save money.

Yet several hurdles are hindering insights businesses can gain by using data and analytics. What’s the answer? Data fabric.

What is the challenge?

According to Armstrong, data is core to business operation. Organizations have a myriad of data types, tools, and languages working together to produce desired insights. Yet they must overcome several hurdles before they can effectively use data and analytics. These include:

  • Integrating data across multiple location and technologies
  • Ensuring no data is lost during integration
  • Manually copying data from and to different systems.

To solve these challenges, organizations may be tempted to use new, unfamiliar tools—or create technical silos. These are not solutions, as they cause more problems and limit productivity. Instead, Armstrong recommends solutions that aggregate all your data into a single platform, where policies can be applied to ensure data placement, movement, and protection.

“The rise of a modern data fabric as a data management architecture is a direct response to the data integration and protection challenges associated with today’s highly distributed and diverse data landscapes,” states Armstrong.  

What exactly is data fabric?

If data fabric is key to solving the data and analytics challenges for organizations, let’s first understand exactly what data fabric is.

Armstrong provides a comprehensive definition. “Data fabric is a design concept that enables reusable and augmented data integration services and data pipelines to deliver integrated data. It can combine data management, integration, and core services that are orchestrated across multiple deployments and technologies. Companies benefit with democratized data access through self-service that orchestrates data delivery across multiple use cases."

He goes on to say that data fabric is an overarching architecture that can ingest data from any store, filesystem, or database and then apply AI and ML tools to analyze and mine insights required for maximum business value. It abstracts away different geographical, physical, and logical locations with a semantic layer that uses multiple access methods and protocols. This means data science and engineering teams can continue using familiar tools to support existing productivity levels. 

What else should we know about data fabric?

Armstrong’s article explains the importance of data protection as part of data fabric. We’ve all heard recent news stories about security breaches. According to an end of the year article in Wired.com, The Worst Hacks of 2021, last year “was a year of ransomware, surveillance, data breaches, and yes, more ransomware.”

Since these types of attacks show no sign of slowing, modern data fabrics can help by building in data protection techniques such as snapshots, mirroring, and tiering. These techniques help ensure data used for insights is protected, available, and recoverable 24×7. The data fabric should automatically replicate snapshots and mirrors across clusters, thereby eliminating single points of failure. Yet data fabric also needs to provide replicas to rebuild data sets in case of a malicious attack. If the solution has platform-level data management, replicas will carry the same security policies no matter where they are located.

The article goes on to explain the importance of integration—and being sure you don’t create silos in your quest for data analytics. “Identifying insights or competitive advantages requires access to all your data – even if it spans different file systems. A modern data fabric should enable data engineers and data scientists to tap into remote data sources where the data has not been integrated into the data fabric architecture. Tapping into data means that developers or scientists have real-time access to test data or can directly access information on a specific robotic arm on the manufacturing floor when anomalies are detected.”

HPE Ezmeral: A modern data fabric

Armstrong concludes by explaining how HPE Ezmeral Data Fabric is a modern data fabric that can meet the data analytics needs of organizations today.

“HPE Ezmeral Data Fabric is a software-defined data store and file system platform that delivers trusted data to any data-driven organization by supporting multiple data types, application programming interfaces (APIs), and ingest mechanisms that augments data with AI/ML workflows. By leveraging community innovation, you can leverage the large and evolving set of tools and frameworks used today and in the future.”

This type of single data infrastructure and platform-level data management allows organizations to reduce data silos while consistently applying policies across any data source or environment. Using the HPE Ezmeral Data Fabric, analytic and data teams can now focus on their tasks instead of their infrastructure.

After reading the article, I agree with Armstrong. A modern data fabric can be the holy grail of business insights.

You can read Armstrong’s full article here:


Learn more about HPE Ezmeral Data Fabric, the industry's first data fabric to combine S3-native objects, files, streams, and databases into a single scalable data platform.

Alison Golan

Hewlett Packard Enterprise


About the Author


Alison Golan is a writer/editor for HPE's social marketing team. For 30+ years, she’s been writing about technology – from hardware and software to networking and streaming. She started her tech career as a public relations specialist, arranging media coverage with CBS, CNN, CNBC, The New York Times, The Wall Street Journal, Business Week, and Fortune. Today, she enjoys transforming technical jargon into compelling stories.