High-Performance Cloud Data Warehouse Performance Testingv1.0

1. Summary

Data-driven organizations rely on analytic databases to load, store, and analyze volumes of data at high speed to derive timely insights. Data volumes within modern organizations’ information ecosystems are rapidly expanding—placing significant performance demands on legacy architectures. Today, to fully harness their data to gain competitive advantage, businesses need modern scalable architectures and high levels of performance and reliability to provide timely analytical insights. At the same time, many companies are migrating to fully managed cloud services. With these managed as-a-service deployment models, companies can leverage powerful data platforms without the technical debt and burden of finding talent to manage the resources and architecture in house. These models enable users to pay as they play and to stand up a fully functional analytical platform in the cloud with just a few clicks.

This report outlines the results from a GigaOm Analytic Field Test derived from the industry standard TPC Benchmark™ H (TPC-H)¹ to compare Actian Avalanche Cloud Data Warehouse, Amazon Redshift, Microsoft Azure Synapse, Google BigQuery, and Snowflake Data Warehouse. This test produced interesting results that reveal some of the performance characteristics of the five platforms.

Overall, the benchmark results were insightful in revealing query execution performance and some of the differentiators for Avalanche, Synapse, Snowflake, Amazon Redshift, and Google BigQuery. In our testing, Avalanche query response times on the 30TB TPC-H data set were overall 8.5 times faster than Snowflake in a test of 5 concurrent users. Furthermore, Avalanche was approximately 1.3 times faster than Redshift, 1.5 times faster than Synapse, and 7.9 times faster than BigQuery.

Examining price-performance, Avalanche ran the 5 concurrent user TPC-H queries roughly 6.4 times more cost effectively than Snowflake, as measured in cost per query per hour. Avalanche further proved 1.4 times better than Redshift, 1.3 times better than Synapse, and 12.4 times better than Google BigQuery in terms of cost per query per hour across the examined cluster classes.