AtScale’s Cloud Data Warehouse Benchmark reports provide quantified results for query performance, user concurrency, compute costs, and semantic complexity. Download them to see how AtScale improves performance across all four measures for Amazon Redshift, Azure Synapse Analytics SQL, Google BigQuery, and Snowflake.
We tested the performance and scalability boundaries of the platforms as well as the operational cost dimension. We also challenged the traditional data modeling techniques by testing an alternative to raw TPC-DS SQL.
These performance benchmarks show results for these areas:
We also show where AtScale significantly improves these results, delivering query performance improvement of up to 61x, ROI improvement of up to 12x, and a reduction in semantic complexity across the board of 76%.
Running one query in general on Google BigQuery doesn’t cost a lot of money, but when you are talking about the amount of data users we have that are running queries concurrently, it’s important to run queries as efficiently as possible. AtScale helps us reduce the amount of processing in Google BigQuery which saves us money.
AtScale powers the analysis used by the Global 2000 to make million-dollar business decisions. The company’s Intelligent Data Virtualization platform provides Cloud OLAP, Autonomous Data Engineering™ and a Universal Semantic Layer™ for fast, accurate data-driven business intelligence and machine learning analysis at scale. For more information, visit www.atscale.com.
AtScale provides the premier platform for data architecture modernization. AtScale connects you to live data using one set of semantics without having to move any data.
Leveraging AtScale’s Autonomous Data Engineering™ query performance is improved by order of magnitude. AtScale inherits native security and provides additional governance and security controls to enable self-service analytics with consistency, safety and control. AtScale’s Intelligent Data Virtualization™ and intuitive data modeling enables access to new data sources and platforms without ETL and or needing to call in data engineering.