Scaling the Mountains of Siloed Data

[JoannStarke]

How a unified data environment lets you remove the barriers for successful analytics and AI

Across all industries, companies are using analytics, AI, and machine learning (ML) to unleash richer customer experiences, faster decision making, and continuous innovation. But it doesn’t take long before data science and analytic teams face mountains they must scale (or find a way to walk around them). These mountains consist of the dreaded data siloes – causing numerous headaches and slowing innovation.

A new report from 451 Research indicates that effective digital transformation initiatives need unified data environments to provide seamless access to geographically distributed data types and sources. This report explains the value these environments provide by simplifying management and processing of multiple data types and sources including traditional BI, real-time, and batch analytics. It also covers the need to increase data team productivity by replacing manual processes with automation, which frees these personas to focus on deeper levels of data exploration.    

Hybrid data lakehouse

HPE Ezmeral Unified Analytics is the industry’s first hybrid data lakehouse that enables data engineers, scientists, and analytic users to access and process multiple data types and sources from a single user experience. Just as senior leaders seek opposing points of view from their teams, the unification of data into a single experience enables corporate-wide collaboration and sharing of results to create better fraud detection, faster processing of financial transactions, or new medications to treat Parkinson’s disease, diabetes, or the latest variant of a virus.

A data lakehouse is a recent open architectural approach that combines the scalability of data lakes with the ACID-transactional reliability of data warehouses. In essence, it combines business intelligence (BI), SQL analytics, real-time data apps, data science, and machine learning into a single solution enabling deeper levels of data exploration.

Why is this happening now? Customers tell us they no longer want to pay for dedicated teams, systems, and operating expenses associated with maintaining systems for different types of data. Add in the time spent rationalizing protocols, policies, and APIs across all these systems, and it’s easy to understand why customers seek a different approach for analytics.

HPE Ezmeral Unified Analytics speaks to the needs and problems of multiple audiences:

• For business, it’s all about TCO.

Unified analytics’ single infrastructure spreads across on premises, edge, and multiple clouds, lowering operational costs. Compare this capability to alternatives that force you to move all your data to the cloud.  

• For data scientists, it enables fast time to insights.

From Day 1, pre-integrated notebooks and opinionated open-source stacks that have been certified and updated automatically by HPE are available. This feature unifies analytic techniques into a single user experience, simplifying cross organizational collaboration and data sharing.

• For data engineers, it offers freedom of choice.

Data engineers can use the tools, engines, and applications they prefer, reducing the need to learn new tools and data access patterns. The built-in app store contains opinionated stacks, and HPE GreenLake Marketplace provides single-click availability to certified ISV solutions such as Cribl, NVIDIA, and Pepperdata.

Why HPE for open source?

Open-source software is designed to process and handle the large volumes of data required to process billions of records. But when a customer downloads open-source tools from the community, they are responsible for installing, configuring, and integrating these tools into existing environments. This process take a lot of time and requires dedicated teams to complete all these tasks, not to mention updating them with the frequent releases associated with each solution.

HPE has natively integrated Apache Spark, Delta Lake, Hive, and Thrift Server, relieving customers from the installation and configuration processes. HPE also refreshes these components automatically when updates are published. Lastly, HPE provides 24x7 support elevating these components to enterprise-ready status.

If your organization is tying their financial performance to the benefits of analytics, AI, and ML, then you need to look at HPE Ezmeral Unified Analytics. Unify globally distributed data across multiple platforms and data types. Unify processes and decision making to accelerate innovation. And best of all, begin to reduce total operating costs with a solution that works with existing analytic silos as well as easily integrates with new data sources.

Learn more at www.hpe.com/ezmeral/unifiedanalytics

Hewlett Packard Enterprise

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