GigaOm Radar for Data Warehousesv4.01

1. Summary

Data warehousing is a robust and mature category of data processing that has undergone decades of transformation and refinement. Data warehouses were originally developed in the late 1980s as a way for organizations to consolidate data from disparate sources and store it in a centralized repository. They represent a paradigm shift away from transactional databases that were optimized to handle large volumes of transactions toward those that were optimized for analytics.

Data warehouses make use of several structural changes and optimizations geared specifically to improve analytics on large volumes of data, including a dimensional model/star schema, massively parallel processing (MPP), columnar storage, vector processing, and data compression. Cloud computing also had a major transformational impact on data warehouses, decoupling them from dependencies on physical storage systems, the expansion of which was often prohibitively expensive. The cloud scenario also simplified the provisioning and configuration of data warehouses, making it possible for them to be much more widely adopted. Today, data warehouse platforms all support some degree of cloud enablement, with many that are fully cloud native and some that support hybrid or multicloud deployment as well.

Data warehouses were originally developed to support reporting and analytics for management decision-making and have since grown to support a variety of workloads and data types to meet contemporary customer needs. These include predictive analytics and machine learning (ML) workloads, streaming data, internet of things (IoT) and time series data, and data from SaaS applications. Most recently, data warehouses have begun to support generative AI large language models (LLMs), making use of them in natural language querying and allowing them to be fine-tuned on data in the warehouse. Data warehouses have also evolved so that it is not necessarily a prerequisite to load the data first into the warehouse in order for it to be included in a query; many data warehouse platforms offer in-place querying of data where it resides, whether in a data lake, cloud object storage, or other source.

The result of this evolution and growth is the set of modern, flexible, powerful data warehouse solutions available today, which remain established, critical components of organizations’ data strategies.

This is our fourth year evaluating the data warehouse space in the context of our Key Criteria and Radar reports. This report builds on our previous analysis and considers how the market has evolved over the last year.

This GigaOm Radar report highlights key data warehouse vendors and equips IT decision-makers with the information needed to select the best fit for their business and use case requirements. In the corresponding GigaOm report “Key Criteria for Evaluating Data Warehouse Solutions,” we describe in more detail the capabilities and metrics that are used to evaluate vendors in this market.

All solutions included in this Radar report meet the following table stakes—capabilities widely adopted and well implemented in the sector:

• MPP
• Optimizations for analytics
• Basic cloud support
• Security and access controls
• Support for ML workloads
• BI platform integration
• Data loading, ingestion, and replication