Data fabric: The holy grail of business insights

Data is core to business operations, which explains why data and analytics have become a core business function. But organizations need to overcome several hurdles before they can get to the holy grail of business insights. Such hurdles include how to integrate data across multiple locations and technologies without data loss or having to manually copy data from and to different systems before analytic, data science, or engineering teams can begin their analysis.

Data is growing both organically and inorganically (think mergers and acquisitions), which means your organization has myriad data types, tools, and languages working together to produce the desired insights. When building a data analytics infrastructure, look for vendors that won’t increase technical silos or force business and technical teams to use new, unfamiliar tools that will hamper productivity. Solutions that aggregate all your data into a single platform where policies can be applied to ensure data placement, movement, and protection are key.

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.    

What is Data Fabric?

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. 

It’s 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.  

Data Protection

Recent events in the news highlight that every organization is, or will be, under relentless attack by malicious actors. And some of these attacks will be successful. Modern data fabrics need to build in data-protection techniques such as snapshots, mirroring, and tiering to ensure that data used for insights is protected, available, and recoverable 24×7. The data fabric should automatically replicate snapshots and mirrors across clusters to eliminate single points of failure – but also provide replicas to rebuild data sets in the event 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.

Identifying insights or competitive advantage 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.

A Modern Data Fabric

HPE Ezmeral Data Fabric is a software-defined data store and file system platform with a proven track record across a wide variety of large-scale production systems. It 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. The single data infrastructure and platform-level data management allows any organization to reduce data silos while consistently applying policies across any data source or environment. This allows analytic and data teams to focus on their tasks rather than on the infrastructure.

Take advantage of HPE Discover 2021 to see how HPE Ezmeral Data Fabric simplifies the work of analytics and developer teams by integrating data from edge to cloud. And come learn how HPE Ezmeral can help with Spark when it doesn’t make sense to place it in the cloud.

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About Tom Armstrong

Tom Armstrong is a Senior Technical Marketing Engineer at HPE with deep knowledge and expertise in the areas of VMs/operating systems, application software, and data networking. He holds a B.S. degree in Information Systems.