Table of Contents
- Data Warehouse Primer
- Report Methodology
- Decision Criteria Analysis
- Evaluation Metrics
- Key Criteria: Impact Analysis
- Analyst’s Take
- About Andrew Brust
Data warehouses are well-established and trusted solutions that enterprises have long been using to manage and analyze large amounts of data (even as the threshold for what’s considered “large” has risen over time). Data warehouse technology has undergone significant transformation since its early days. This transformation has only accelerated with the advent of the cloud, which has brought forth new capabilities like autoscaling, elasticity, and multicluster concurrency, as well as freedom from the physical appliance hardware that dominated data warehouses in the pre-cloud era.
Longer-standing optimizations for analytics include massively parallel processing (MPP), in-memory processing, vector processing, and columnar storage. Data warehouse solutions have also accumulated an array of integration points for tools, including machine learning (ML) and business intelligence (BI), security and data governance, and data processing and management. The result is a set of modern, flexible, cloud-based and on-premises data warehouse solutions, which comprise an established, mature, critical component of today’s enterprise data analytics strategy.
Recently, tension has arisen in the industry between data warehouse and data lake providers. While warehouse solutions are based on relational database engines and lake solutions are based on working with less structured data stored in open file formats, the two have similar missions: act as an authoritative single source of truth for data analytics. Because of this shared mission, there has been some convergence between the two models, though vendor philosophies and approaches differ. Some vendors support pursuing a unification of the two as a merger of equals; some offer users the choice of either model; some predict that data lakes will accelerate the obsolescence of data warehouses; and still others are doubling down on data warehouses entirely. However, even the partisans on each side are busy adding features that make them better able to take on workloads of the other, furthering the convergence, at least in a de facto sense.
This GigaOm Key Criteria report focuses exclusively on data warehouse solutions—which continue to serve as enterprise analytics repositories of record, typically subject to a higher degree of formality and curation than data lake solutions—and details the criteria and evaluation metrics for selecting an effective data warehouse solution. The companion GigaOm Radar reports identify vendors and products that excel in those criteria 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.
How to Read this Report
This GigaOm report is one of a series of documents that helps IT organizations assess competing solutions in the context of well-defined features and criteria. For a fuller understanding, consider reviewing the following reports:
Key Criteria report: A detailed market sector analysis that assesses the impact that key product features and criteria have on top-line solution characteristics—such as scalability, performance, and TCO—that drive purchase decisions.
GigaOm Radar report: A forward-looking analysis that plots the relative value and progression of vendor solutions along multiple axes based on strategy and execution. The Radar report includes a breakdown of each vendor’s offering in the sector.
Solution Profile: An in-depth vendor analysis that builds on the framework developed in the Key Criteria and Radar reports to assess a company’s engagement within a technology sector. This analysis includes forward-looking guidance around both strategy and product.