Listening to leaders: Modern solutions to hybrid, multi-cloud data analytics

CXOs across a variety of industries discuss challenges and best practices based on first-hand experiences; they also brainstorm future opportunities and the art of the possible.

To borrow from Albert Einstein^[1]: The more I learn, the more I realize there’s so much more to learn. One of the favorite parts of my job is talking with customers to understand their approach to technology and how they come up with innovative, sometimes ground-breaking solutions.

I recently joined HPE as the general manager for HPE Ezmeral to lead our work on building out the next generation of the enterprise data stack – a purpose-built platform that simplifies software development and catalyzes productivity. This platform helps our end users and builders develop the next generation of enterprise-ready applications. We are incorporating concepts such as big data analytics, data science, and machine learning — all the way from concept to operationalization. The goal of the HPE Ezmeral team is to sculpt the future of how enterprises adopt cutting-edge technologies.

At our upcoming HPE Discover 2022 event (June 28-30), HPE has invited four senior industry leaders to participate in a CXO forum focused on the adoption of forward-looking technologies. (Customer Conversation: How to Turn Your Distributed Business into an Analytics Factory.)

In this forum, we will discuss a variety of topics including their experiences with and plans for adoption of technologies like hybrid-cloud computing, productionizing artificial intelligence (AI) and machine learning (ML) solutions at scale, edge computing, and new hardware such as accelerators. Each of our industry leaders has experience with a range of technologies to operationalize data and advanced analytics across different use cases, usage patterns, locations, and deployment models in ways that are innovative, save time and money, and push the frontier for what is possible in a truly digital future.

Without giving much away, the rest of this article lays the groundwork for our discussion. I lay out my perspectives of the current technology landscape and highlight trends that attracted me to HPE.

HPE Ezmeral innovation panel session

In today’s data-driven world, our focus has shifted from collecting data to consuming it: how we consume it, where we consume it, and how fast we consume it. A big consideration when architecting systems today is where the data resides and how it can be accessed. For example, the cloud offers tremendous value for elastic, scale-out computations. Cloud systems are designed to make it cheap (and in many cases free) to move data into the cloud. However, taking data out of the cloud or moving it from one cloud to another becomes extremely expensive.

Also, enterprises worry about other data movement issues such as latency, security, and governance. How are enterprises thinking about these concerns? Do cost-effective, fast, and simple ways of moving data exist? Can companies tap into these technologies to unlock new value with hybrid-multi-cloud deployments?

Another trend I’m fascinated by is edge computing. As an industry, we are moving away from reporting-based analytics to more operational analytics at the edge — analytics as close to the source of the data as possible. The definition of edge has fundamentally changed. For example, what used to be a small, resource-constrained device a decade ago is now a quarter-rack server powering in-store analytics at the manager’s office in your local supermarket. As more and more industries show interest in real-time and edge analytics, what are the workloads driving this interest? What does the edge really look like? And what percentage of work in the future can be moved to the edge? Can the edge fundamentally change enterprise compute?

As the amount of data consumed increases, approaches such as ML help process insights at superhuman scale, empowering systems to observe, learn, and manage themselves. However, how prevalent is AI and ML in the enterprise world? What are the opportunities and challenges to take intelligent solutions to production? While typically more data results in better insight, how do developers manage operationalization concerns such as cost and security?

Finally, one of the topics I’m most excited about is emerging hardware. Be it a GPU or accelerator, it is changing the way technology can unlock new use cases and solve problems that were conceptual (at best) in the past.

HPE Ezmeral portfolio

HPE Ezmeral portfolio addresses many of these themes. HPE Ezmeral Data Fabric is the industry’s first edge to cloud solution that natively ingests and stores different data types, enables in-place processing, and simplifies data access. Another solution is HPE Ezmeral ML Ops, which standardizes processes and provides the tools you need to build, train, deploy, and monitor ML workflows. HPE Ezmeral Runtime Enterprise is a secure, enterprise-grade platform to build and deploy cloud-native and non-cloud-native (legacy) applications at scale across data centers, multiple clouds, and at the edge for a wide range of use cases. And lastly, HPE Ezmeral Unified Analytics solution modernizes legacy data and applications to optimize data-intensive workloads from edge-to-cloud for advanced analytics.

Concluding remarks

This CXO panel session is a great opportunity to learn from some of the leading minds who are thinking about the next frontier, which is imminent. We’ll also share how these industry leaders are using the HPE Ezmeral portfolio to create a new operating model that saves time, money, and simplifies data analytics.

I’m excited to participate in this insightful discussion about what’s possible today and what’s on the horizon for multi-cloud analytics, AI, and ML. I hope you’ll join me.

Register today for HPE Discover 2022, and then sign up for the HPE Ezmeral Innovation Panel session (session ID: IS4881). You can also learn more about our hybrid, multi-cloud story by visiting the HPE Ezmeral website.

^[1]“The more I learn, the more I realize how much I don’t know.” Albert Einstein ↑