Gain high-performance data warehouse solutions

To support your business intelligence initiatives and accelerate decision-making, you need a flexible foundation that has been optimized to collect and analyze volumes of data from disparate sources.

IBM data warehouse solutions are available on premises, on cloud or as an integrated appliance. Infused with machine learning and AI for deeper, faster analytics, they also share a common SQL engine for streamlining queries. The IBM data warehouse is also available on the IBM Cloud Pak for Data platform to support hybrid cloud deployments.

Read: Forrester names IBM a leader in The Forrester Wave: Data Management for Analytics, Q1 2020

Why IBM for data warehousing?

Hybrid multicloud

Avoid vendor lock-in with a multicloud approach. Run on IBM Cloud Pak® for Data, a hybrid cloud data platform.

Adaptable scaling

Scale storage and compute independently with elastic pricing for data warehouses on IBM Cloud®. Pay only for the capabilities you need.

A foundation for insight

Realize the full value of your data — structured, unstructured, geospatial — by operationalizing AI across the enterprise.


Learn how IBM Db2® Warehouse on Cloud gives this healthcare information services provider the flexibility and ability to scale as needed to meet growing customer analytics demands.


Harness AI’s power

IBM and Sirius experts discuss how a modern data and AI platform unifies company data for better insights.

Get flexibility with hybrid data warehouses

This report discusses why top companies are almost twice as likely to use a hybrid data warehouse architecture.

Support data growth and complexity

The Aberdeen Group reviews how data warehouse solutions address data complexity and disparity.

Get Netezza on the cloud

Netezza is offered on IBM Cloud and AWS.

Meet resource needs more precisely

IBM Db2 Warehouse Flex One is a cloud database that better supports data volumes of less than 1 TB.

Reduce the stress of vendor lock-in

The fully managed, elastic IBM Db2 Warehouse on Cloud product is available on AWS.

Dive deeper on data warehouses

What is a data warehouse?

A data warehouse is a system that aggregates data from different sources into a single, central data store to support analytics, data mining, machine learning and AI. Also known as an enterprise data warehouse (EDW), its functionality allows businesses to run sophisticated analytics on petabytes of data that could not be handled by a traditional relational database. To further enhance your analyses, you can add visualization and business intelligence applications.

A data warehouse system is more than just storage. A data pipeline is built to enable data integration. The pipeline infrastructure includes the process known as extract, transform, load (ETL) or one known as extract, load, transform (ELT). In these processes, data from multiple sources is collected, cleansed and transformed. For the ETL process, the data is transformed before being loaded into the data warehouse using data integration software, such as IBM DataStage. In the ELT process, the data is transformed within the data warehouse itself.

Choosing a data warehouse platform

Today’s complex analytics workloads involve a diverse array of data sources and types. These range from structured transactional data residing on premises to unstructured, born-on-the-cloud data flowing in from Internet of Things (IoT) sensors and mobile devices. For the most impactful insights, your business analytics teams need all of this data integrated. Choosing the right data warehouse platform or combination of solutions can help optimize your results.

Cloud data warehouse
For analyzing data that is born in the cloud, a cloud-based data warehouse might be best. It allows you to analyze data where it resides to speed results and reduce complexity. You also gain the deployment speed, rapid scalability and budgeting flexibility of cloud solutions.

On-premises data warehouse

When data already exists on premises or when government regulations restrict moving data across state or country lines, an on-premises data warehouse might be the best choice. Again, you gain the efficiencies of analyzing data where it resides and avoid the costs of moving large amounts of data to another environments. You can also retain tight control over your data while minimizing analytics latency.

Integrated data warehouse appliance

An integrated analytics solution that combines hardware and software can offer high performance while minimizing the management burdens of operating a traditional “software-defined” data warehouse. These solutions support a variety of data sources and types as well as fast-growing data volumes. They may include the latest data science technologies, such as machine learning or AI, to support your advanced analytics initiatives.

Hybrid environments

Many businesses can benefit from a combination of platforms. The key to capitalizing on this approach is to make sure the solutions have a common underlying platform. It  could share a common SQL engine, embedded analytics capabilities, common tools and underlying data software.

You could also consider an integrated data and AI platform such as IBM Cloud Pak for Data, which modernizes how you collect, organize and analyze data. Built on the Red Hat® OpenShift® open source platform to support hybrid multicloud deployments, it includes the IBM Db2 Warehouse among its numerous data management, integration and analytics capabilities designed to fuel innovation with AI.

Database versus data warehouse versus data lake

The different data storage systems align with the types and volume of data you need to store as well as how the data will be used.

A database houses structured data and is limited in the volume of data it can accommodate. It is used primarily for fast queries and transactional processing.

A data warehouse also houses structured data but can accommodate larger volumes of both current and historical data from multiple sources. Data is organized into schemas to be used for operational data analysis.

Finally, a data lake houses massive volumes of raw data – structured, semi-structured and unstructured – opening the door to deeper analysis of data not previously accessible. The data is simply stored, not organized into schemas. It is not transformed until needed. Data lakes are commonly built on big data analytics platforms such as Apache Hadoop.

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