Analytics Initiatives Require Unified Data

[JoannStarke]

Many traditional analytic solutions come with an infrastructure-centric focus that increases complexity by growing the number of silos tailored to specific data types and format. As data volumes and new data sources continue to grow, these architectures are limited in their scalability. The storage supporting them is unable to keep pace because it was never designed to service the very large data stores required for analytics, AI, and machine learning (ML). These architectures also slow data engineers’ efforts for the organization to compete on analytics.

Delayed insights can have a devastating effect on an organization’s ability to serve and nurture customers. Data-first initiatives require real-time, consistent, connected, and trusted data to support critical business operations and insights. Existing silos and new data sources slow collaboration and sharing across platforms. They also increase operational costs by requiring specialized skill sets to configure, manage, secure, and govern each data platform. Expanding data volumes and data distributed across edge, multiple clouds, and on premises can cause organizations to fail when executing their data-first initiatives.[1]

Accelerate data-first transformations  

HPE Ezmeral Data Fabric is the industry’s first integrated data and analytics platform that simplifies the capabilities organizations need for data-first initiatives. This single platform simplifies data access patterns, data acquisition, processing, and surfacing of data to your data engineers and scientists. It ingests, centralizes, indexes, and processes multiple data types and formats into a single logical store, providing data engineers and scientists with a single source of data across multiple locations and physical clusters.

In essence, it accelerates your ability to identify a key insight from a field of data.  

HPE Ezmeral Data Fabric simplifies data access for any user or tool through a global namespace and support for industry standard APIs. That means data engineers get consistent access patterns, regardless of their geographic location, organizational team, or their preferred analytics tools.   

The flexible, high performance file system allows design, development, and deployment of analytic systems with high scalability and performance. It also enables organizations to define where data is stored by matching the storage tier to performance needs, then using automated policies to tier data as it ages to a central data center followed by archival storage. This process happens automatically without copying the data or complex ETL processes. 

HPE Ezmeral Data Fabric stores files, objects, NoSQL databases, real-time and batch streams, all of which create metadata indexed into an integrated, multi-modal database. This capability allows data engineers to do quick queries on the metadata to detect anomalies for fraud detection or query the data itself for complex training jobs. The results are surfaced to users, applications, monitoring systems, or alerting dashboards through a series of standard APIs supported by the data fabric.

The built-in security management system reduces the need for 8-10 unique solutions required by existing analytic systems to build, deploy, secure, manage, and integrate each user, unique application, and analytic tool into existing authentication and authorization system(s).  Building security and data management into the data fabric saves organizations time and money by reducing the need for specialized skills and removing risk.  

With HPE Ezmeral Data Fabric, you can implement analytics across your entire enterprise to respond faster to new opportunities or business challenges. By centralizing files, objects, NoSQL databases, and streams into a single, logical data plane, data integrity increases along with the confidence to use analytics to compete with analytics.

Take the next step and watch the video below. You can also take one of our free, learn on-demand courses for data fabric, AI, and ML technologies. 

[1] The Forrester Wave^TM: Enterprise Data Fabric, Q2 2022

Hewlett Packard Enterprise

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