Get AI-Ready: Modernize Your Analytics with Pure Storage and Starburst

AI success starts with a clean, reliable data foundation. Unfortunately, many teams are still using yesterday’s architectures and stacks, such as data warehouses in relational databases and Hadoop, to try to deliver modern outcomes, like AI. Although not impossible, this approach is difficult and plagued with brittle pipelines that slow access, complicate governance, and limit scale. Too often, this results in data that’s scattered and expensive to maintain. 

Pure Storage and Starburst address that foundation from two sides. Pure Storage provides high-performance object storage that is simple to operate at scale. Starburst provides federated access and governance across sources, enabling engineers to query where data resides and standardize on open formats without creating additional copies.

This article outlines a pragmatic path to modernization for data engineers. We’ll cover how to shift from legacy stacks to object storage, adopt Parquet and Apache Iceberg, upgrade processing patterns, and introduce governed, federated access that supports both analytics and AI.

Why Modernizing Your Hadoop Architecture Matters More Than Ever 

Any discussion of data architecture modification has to start with Hadoop. For many organizations, Hadoop was once the backbone of enterprise analytics. It provided distributed storage, batch processing, and, through Hive, an accessible way to query data at scale. But what worked a decade ago now shows its limits. Hadoop clusters are costly to maintain, slow to adapt, and too rigid for the demands of today’s analytics and AI.

Making Your Technology and Processes AI-Ready

Modernizing your analytics foundation, whether you’re still on Hadoop or another legacy system, means preparing for the next generation of workloads. Ultimately, modernization is not just about replacing old technology. It’s about creating a data foundation that supports faster, more governed data access to support modern workloads. 

Pure Storage supports this approach, providing modern object storage to facilitate not only the update of your technology but your business processes too. 

Starburst extends this vision by providing a federated query engine that takes the usability Hadoop pioneered and applies it to modern storage and open table formats.

With Starburst, that modernization path emphasizes optionality. Instead of rebuilding from scratch, teams can connect to existing sources, expose them through a governed query layer, and begin adopting object storage, Parquet, and Apache Iceberg at their own pace. 

Figure 1: Break down silos: A federated and governed query layer brings together databases, data lakes, and on-prem systems to fuel AI and ML securely.

The result is a pragmatic migration strategy that reduces friction while ensuring data remains accessible and trustworthy. For organizations moving toward AI, this step is no longer optional. It’s the prerequisite for progress.

Where Should You Start When Considering a Data Architecture Update?

Where should you get started? One thing you don’t want to consider is an artificial cliff edge created by your data migration. 

Modern data migration doesn’t need to be as painful as it was in the past.  

Data Migration on Your Terms

To do this, Starburst and Pure Storage collaborate to create a bridge between your old systems and new ones using data federation. The result gives organizations a clear migration path without requiring wholesale rewrites from day one.

New Data Architecture, at Your Speed

The value is immediate. With a more flexible architecture, teams can shorten the path from raw data to usable insight, reduce the operational overhead of maintaining brittle pipelines, and create a more accessible, governed approach that shifts workloads from the old system to the new. 

Using Starburst and Pure Storage together, data engineers can expose legacy sources alongside new ones through the same governed interface, so AI initiatives can begin training and validating models on production-grade data right away, instead of waiting years for infrastructure projects to complete.

Unlocking Performance Gains

Over time, your new data architecture will not only unlock new workloads but also improve older ones. 

By shifting to object storage, open formats like Parquet and Iceberg, and pairing them with Starburst’s federated data access, organizations can cut query times dramatically, scale efficiently with workload demand, and ensure analytics and AI remain responsive as data volumes grow.

The Concrete Steps You Can Take Today to Improve Your Data Architecture 

Moving toward a modern data architecture does not need to be a multi-year overhaul. There are pragmatic steps that organizations can take right now to lay the groundwork for analytics and AI. Pure Storage is the consistent foundation for all storage components of the modern data architecture. From block storage for your transactional databases to scale-out, high-performance object storage for your modern pipelines, to file and GPU-direct storage for your training clusters and Portworx^® for your modern application stacks in containers, Pure Storage delivers for modern architectures. 

Object Storage Is the Gateway to AI

A natural first move is adopting standard object storage protocols. The Pure Storage^® FlashBlade^® platform delivers the scalability and durability needed for large volumes of data while removing the rigidity of older systems. It also prepares the foundation for AI and ML operations, where rapid access to both structured and unstructured data is critical.

Modernize All of Your Data Architecture 

At the same time, it’s equally important to modernize the artifacts that live inside your architecture. Standardized file formats such as Parquet ensure data can be efficiently stored and queried across multiple tools. 

Open table formats like Apache Iceberg extend this further by enabling schema evolution, time travel, and ACID transactions, which are essential for building a governed lakehouse that can serve both analytics and AI.

Taken together, these steps give data teams immediate wins in terms of flexibility, governance, and scalability while setting a solid foundation for long-term AI readiness.

Why Simplicity Matters as You Modernize

Every modernization effort carries the risk of trading one set of complexities for another. Older Hadoop-based systems required teams to stitch together storage, compute, and orchestration with significant manual effort. Maintaining those pipelines often consumed more engineering hours than the insights they delivered.

Modern architectures reduce this burden. Object storage simplifies scaling without constant capacity planning. Open formats like Parquet and Iceberg eliminate vendor-specific lock-in while providing built-in features such as schema evolution and ACID transactions. Pairing this with a federated query engine means data can be accessed where it lives, without duplicating infrastructure.

The result is less time maintaining brittle pipelines and more time building value: shorter onboarding for new data sources, easier governance enforcement, and a platform that scales with business needs instead of resisting them.

How It Works in Practice

In production, Starburst and Pure Storage FlashBlade delivered high-throughput, low-latency analytics across federated Iceberg tables. Compared to legacy Hadoop-based systems, the combined solution reduced query times, lowered operational overhead, and improved efficiency per watt.

This validation shows what customers can expect in practice: an architecture that scales seamlessly, enforces governance, and supports demanding AI and analytics workloads without the cost and fragility of older stacks.

Figure 2: How Starburst works with Pure Storage. 

Building Your AI Data Foundation with Pure Storage and Starburst

Modernizing your data architecture is about building the foundation that tomorrow’s analytics and AI will require. That foundation depends on more than storage capacity or compute scale. It requires end-to-end data pipelines, a federated query layer for discovery, and governance that ensures the right people can access the right data at the right time.

This is where Starburst and Pure Storage align. Together, they offer a modern data stack that is flexible enough to evolve with new technologies, yet robust enough to support enterprise-scale workloads today.

For organizations looking to get AI-ready, this partnership shows what a modern data architecture can look like in practice: governed, performant, and ready to support the future of analytics and AI.

To get started with the joint Pure Storage + Starburst solution, take a look at the reference architecture,  reach out to Pure Storage sales or Starburst sales today.