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Data Fabric Lets You Find, Interpret, and Use Data from Anywhere
Every enterprise is under a mandate to be a data-first company by leveraging their data to compete on analytics. As a result, analytics, AI, and machine learning (ML) have become core technologies for any data-first journey. However, many of these initiatives are failing.
I’m sure you know or are experiencing the challenges responsible for these failures as well as the complexity they bring to every organization:
- Exploding data volumes across a variety of types and formats
- Rapid growth in streaming data
- Evolving data processing landscapes with new and emerging technologies to capture and process data in real-time at the edge
These challenges are creating obstacles that are hindering data science and analytic teams. The data silos creating these challenges are not going away, so technologists came up with another approach: a data fabric. This is not a new technology but one that has become more important as data becomes more distributed.
At its most basic, a data fabric provides each data consumer with a consistent, governed, and performance-optimized view of data, no matter where the user or data are located. The need for a single, unified data layer is a result of the cumulative impact of these challenges on the organization while at the same time leadership teams needing greater productivity from analytic and data science teams.
HPE Ezmeral Data Fabric is the industry’s first hybrid data fabric that simplifies the capabilities your organization needs to become a data-first organization. Learn more by watching this video.
Several features set HPE Ezmeral Data Fabric apart from the growing number of data fabric options.
- A single platform deploys across multiple heterogeneous, physical clusters, and locations located on premises, in multiple clouds, and at the edge.
- It acquires, centralizes, processes, and indexes files, objects, NoSQL databases, and real-time and batch streams into a single logical data store.
- A flexible file system allows customers to define where data is stored (hot, warm, cold) through global policies that automatically tier data without copies or complex extract, transform, and load (ETL) processes.
- A global namespace simplifies data access allowing users and apps to use the same query access point. This eliminates the need for admins to provide unique access to unique systems.
- A single security system works in tandem with the global namespace and customer’s authentication and authorization systems, reducing the need for 8-10 unique solutions to build, deploy, secure, manage, and integrate global data.
- The ability to query both metadata and data through a series of APIs to accommodate the different tools used in the enterprise today.
HPE Ezmeral Data Fabric simplifies the capabilities required for data-first analytics by:
Step 1: Ingesting the data from multiple locations, sources, and types without moving or ETL processes
Step 2: Storing it as files or objects then using global policies that dictate the type of storage to use
Step 3: Processing the data using the built-in ecosystem of tools, such as Apache Spark, to help with processing. It can also integrate data into other systems, such as Hadoop systems.
Step 4: Indexing metadata and data into an integrated NoSQL database to accommodate quick queries or complex training jobs
Yes, there are challenges to data-first analytics. That’s why you need a single platform that can be deployed wherever data is located, including legacy data lakes and warehouses. You also need a platform that allows you to scale compute and storage independently and multiple types of storage -- from high performance flash to cheap and deep storage for archiving. Add in the ability to access AI/ML tools and algorithms you use through a single global namespace and security integration, and you have a system that simplifies the processes for successful data-first initiatives.
Learn more about HPE Ezmeral Data Fabric.
Hewlett Packard Enterprise
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JoannStarke
Joann is an accomplished professional with a strong foundation in marketing and computer science. Her expertise spans the development and successful market introduction of AI, analytics, and cloud-based solutions. Currently, she serves as a subject matter expert for HPE Private Cloud AI. Joann holds a B.S. in both marketing and computer science.
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