Key Criteria for Evaluating Data Warehouse Solutionsv4.0

1. Summary

Data warehousing is a robust and mature IT category 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 reporting, analytics, and data management.

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, columnar storage, vector processing, and data compression. The adoption of cloud technology also had a major transformational impact on data warehouses, decoupling them from dependencies on physical storage systems, the expansion of which were often prohibitively expensive. The cloud also simplified the provisioning and configuration of data warehouses, making it possible for them to be much more widely adopted. Today, all data warehouse solutions support some degree of cloud enablement, with many that are fully cloud native and others that support hybrid or multicloud deployment as well.

Data warehouses were originally developed to support reporting and analytics for management decision-making. From there, they have 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)/time series data, and data from SaaS applications. Most recently, data warehouses have begun to introduce support for generative AI and 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 solutions 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 the fourth year that GigaOm has reported on the data warehouse space, and the need to centralize and facilitate analytics over the growing variety of data types and huge volumes of data that today’s organizations consume has only continued to grow. This report builds on our previous analysis and considers how the market has evolved over the last year.

This GigaOm Key Criteria report details the capabilities (table stakes, key criteria, and emerging technologies) and non-functional requirements (evaluation metrics) for selecting an effective data warehouse solution. The companion GigaOm Radar report identifies vendors and products that excel in those capabilities and metrics. Together, these reports provide an overview of the category and its underlying technology, identify leading data warehouse offerings, and help decision-makers evaluate these solutions so they can make a more informed investment decision.