Data Warehouse

Transition Your Data Warehouse Solutions

The stakes for modern businesses are high. It’s time to move beyond traditional data warehouse solutions. Get massive data capacity, unmatched performance, and operational ease-of-use.

A New Approach to Data Warehousing

Seemingly endless growth in data volumes is driving the need for a modern approach to data warehousing. Poorly utilized compute and storage lead to costly inefficiencies. Scale storage resources without compromising performance to maximise the value of data.

Shift to Modern Platforms

Deploying fast object storage on-premises increases choice and reduces risk via a cloud-native architecture. Pure accelerates time to insight with fast “time to first byte” and high throughput, while uncomplicating storage operations for easy deployments.

Scale Workloads

Enterprise workloads demand flexibility. Say goodbye to data silos. Disaggregated storage from Pure scales elastically as a single high-performance source of data. Run multiple isolated workloads and leverage on-demand compute for faster insights.

Power Your Cloud

Get cloud-scale power for hybrid and on-prem ecosystems. With the industry’s first analytical database solution that separates compute from storage for on-prem environments, Vertica and Pure offer new levels of simplicity and flexibility.

Gartner Magic Quadrant grid for Distributed File Systems and Object Storage

Pure Storage Is A Leader Again in the 2022 Gartner® Magic Quadrant™

for Distributed File Systems and Object Storage

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What is a data warehouse?

A data warehouse is a storage system optimised for storing structured data to perform the high-speed SQL queries needed to deliver timely business intelligence (BI). From processing high-speed transactions to predictive analytics, data warehouses have a decades-long history as the de facto storage standard used by enterprises to power their BI.

People also ask:

1. What are the benefits of data warehouses?

Benefits of data warehouses include:

  • Consolidation of structured data from multiple disparate sources 

  • Fast analytical queries from relational databases

  • A dedicated storage solution for cheaper queries and quicker reporting

2. Data warehouse vs. data lake vs. data hub. What’s the difference?

The differences between data warehouses, data lakes, and data hubs are as follows: 

  • Data warehouse: A single repository for integrating and storing structured data pulled from multiple unstructured data sources across your organisation.
  • Data lake: A single unrefined repository of all the structured and unstructured raw data sources within an organisation (including data warehouses). Data must still be processed to extract BI insights.
  • Data hub: A single interface that consolidates all data—both structured and unstructured—into a central accessible data layer. It differs from a data warehouse in that it can also handle operational data and it differs from a data lake by possessing the ability to serve data in multiple formats.

3. Why consolidate your data warehouses into a data hub?

Data hubs provide the data governance needed to streamline data sharing between a diverse collection of endpoints. In this way, data hubs consolidate data lakes and data warehouses into a single access layer. Data processing is abstracted away behind the data hub, giving your organisation a centralized place to extract BI insights.

Test Drive FlashBlade

No hardware, no setup, no cost—no problem. Experience a self-service instance of Pure1® to manage FlashBlade, the industry's most advanced solution delivering native scale-out file and object storage.

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Don't Take Our Word for It

We work with the best across all industries.

“We went all in, and now everything runs on Pure Storage arrays. We no longer have discussions of whether storage is the reason for any performance problems.”

Chad Plemons
Director of Digital Operations, TTX Company


Expand your ability to manage workloads dynamically and simplify operations beyond public clouds.

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Questions, Comments?

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