Table of Contents
- Cloud Analytics Platform Offerings
- Benchmark Setup
- Benchmark Results
- Price Per Performance
- About Microsoft
- About William McKnight
- About Jake Dolezal
Big data analytics platforms load, store, and analyze volumes of data at high speed, providing timely insights to businesses. Data-driven organizations leverage this data, for example, for advanced analysis to market new promotions, operational analytics to drive efficiency, or for predictive analytics to evaluate credit risk and detect fraud. Customers are leveraging a mix of relational analytical databases and data warehouses to gain analytic insights.
This report focuses on relational analytical databases in the cloud, because deployments are at an all-time high and poised to expand dramatically. The cloud enables enterprises to differentiate and innovate with these database systems at a much more rapid pace than was ever possible before. The cloud is a disruptive technology, offering elastic scalability vis-à-vis on-premises deployments, enabling faster server deployment and application development, and allowing less costly storage. For these reasons and others, many companies have leveraged the cloud to maintain or gain momentum as a company.
This report outlines the results from the GigaOm Analytic Field Test to compare Amazon Redshift, Azure SQL Data Warehouse, Google Big Query, and Snowflake Data Warehouse—four relational analytical databases based on scale-out cloud data warehouses and columnar-based database architectures. Despite these similarities, there are some distinct differences in the four platforms.