Is your data stuck in silos? Set it free with analytics and a modern data lakehouse.

[JoannStarke]

History tells us that using data to make decisions has been an integral part of organizations for thousands of years. Data has played a major role in human innovation, from building the pyramids to exploring our solar system. But over the generations, data has changed across three variables: variety, volume, and velocity. And it shows no signs of slowing down. 

In the age of insight, data is at the heart of every industry, and data analytics is the critical building block. It’s also a time when digital transformation initiatives are failing. The problem is the data. It’s everywhere and stuck in silos. It’s in different formats and types, and analytic use cases are as varied as the companies in the Fortune 1000. 

It’s a hybrid world

Data lakes and data warehouses are popular architectures that have been around since 2010. Data lakes were designed for data exploration, predictive modeling, and automated decision making of raw, detailed information such as text, images, audio, video, and log files. Data warehouses were designed for ad hoc queries, reporting, and dashboards to analyze aggregated data such as sales performance by product, customer, or region.

The average enterprise has multiple personas using a broad set of tools, and each one has a prescribed access method to get to the data. Most enterprise data is stuck in legacy platforms built for the pre-cloud era. As data became more and more distributed, the response of some data lake and warehouse solutions was for customers to do an expensive migration to the cloud; and yet, the cloud is not a good solution for workloads with data gravity or latency requirements. So, the bottom line is these solutions only increase complexity for data access, data ingestion, management, portability, compliance, and admin costs.

What organizations need is a different approach:

• One that provides the freedom of choice to place data on premises, in multi-cloud, or at the edge
• A solution that enables the speed and agility companies want from their data engineering and science teams
• An approach that enables power users to deploy the open source libraries and frameworks they prefer to use

Hybrid analytics needs data lakehouses   

A data lakehouse is a modern architecture that combines traditional analytic query with modern machine learning into a unified architecture that supports structured, unstructured, and aggregated data. Watch this video to learn more.

HPE Ezmeral Unified Analytics is the first unified cloud native analytics and data lakehouse solution optimized for hybrid environments.[1] It was designed for customers using machine learning and AI across traditional SQL, business intelligence, and advanced analytics with Apache Spark, Delta Lake, Hive, or Drill. It can be deployed on premises, in multiple clouds, and at the edge. It is also available in HPE GreenLake.

Certified ISV solutions from HPE GreenLake MarketplaceFrom day 1, the solution connects to existing data lakes, data warehouses, and multicloud deployments in a secure and controlled manner, allowing your business to modernize without low-value refactoring. Slowly retire existing estates or build on top.

The foundational layer to this solution is HPE Ezmeral Data Fabric, which centralizes files, objects, NoSQL databases, and streams into a single logical data layer that spans edge to cloud. Once unified, data sets are cleansed to remove duplicates. With a trusted source of data, open-source AI and ML tools can be placed on top of the data fabric making it possible to analyze the data wherever it’s located. The unified data layer simplifies management, governance, and reduces operational costs by eliminating the specialized skill required to setup, configure, and maintain different policies on individual data lake and warehouse environments. 

Organizations perform better when multiple use cases, apps, and users have access to a consistent data source. This includes data analysts and business intelligence personas using Tableau or Microsoft Excel as well as advanced data science and analytic personas writing queries on tools like notebooks.

HPE Ezmeral Unified Analytics frees up data and analytic teams to use the tools, engines, and applications they prefer, reducing the need to learn new tools or data access patterns. The self-service interface provides access to pre-integrated opinionated stacks or the ability to download certified ISV solutions from the HPE GreenLake Marketplace, making the platform even more powerful.   

The bottom line

For many organizations, it comes down to economics. They don’t want to pay for dedicated teams to maintain separate systems across different data types. They want to get back the time they spend rationalizing multiple protocols, policies, and APIs to focus on creating a digital advantage for the business.

No matter your industry, the benefits of HPE Ezmeral’s Unified Analytics data lakehouse are the same: 

• Single, modern data infrastructure that delivers a single trusted data source tuned for analytics and data science
  
  
• Freedom of choice to use the tools, engines, and applications analytic and data science teams prefer, reducing the need to learn new tools or data access patterns
  
  
• Modern data lakehouse that unifies analytic techniques, data, and decision-making processes to increase organizational agility, so you get to the end goal of leveraging your data to increase business innovation.

Learn more about HPE Ezmeral Unified Analytics.

[1] Based on HPE’s internal competitive analysis, 2021

Hewett Packard Enterprise

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