World’s fastest database now boasts less than 50-60% TCO of Snowflake, RedShift, MySQL and PostgreSQL, according to third-party study
SingleStore, the cloud-native database built with speed and scale to power data-intensive applications, announced the results from a performance and total cost of ownership (TCO) study conducted by analyst and media firm GigaOm. The study revealed that SingleStoreDB delivers a 50% lower TCO against the combination of MySQL and Snowflake and a 60% lower TCO compared to the combination of PostgreSQL and Redshift, leading OLTP and OLAP database combinations that customers stitch together in an attempt to power SaaS, APIs and data-intensive applications. GigaOm also measured SingleStoreDB performance across three industry-standard benchmarks: TPC-C (operational), TPC-H (analytical) and TPC-DS (analytical).
“We set out to challenge a common industry assumption: that organizations need multiple databases to address transactional and analytical workloads”
Today’s modern SaaS applications are fueled by data and need to embrace a distributed architecture that enables organizations to infinitely scale their operations on a modern multi/hybrid cloud environment. They must also drive analytics on transactional data, at scale, in real time without all of the complexities and costs. Conventional architectures require two different database architectures and platforms: online transactional processing (OLTP) platforms to handle transactional workloads, and online analytical processing (OLAP) engines to perform analytics and reporting. SingleStoreDB uniquely combines transactional and analytical workloads in a single unified engine, thus eliminating performance bottlenecks and unnecessary data movement to support constantly growing, demanding workloads. These tremendous TCO savings can be attributed to the fact that SingleStoreDB can be used as a single database for modern SaaS applications that previously required stitching together multiple specialized database systems.
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“We live in the data-intensive era. It’s growing more challenging to deliver exceptional data experiences through SaaS and APIs with the increasing need for real-time data and analytics with modern applications. All of these apps are becoming data-intensive applications. Databases supporting data-intensive apps must handle a mix of operational and analytical workloads at scale while maintaining simplicity and lowering TCO. This study proves that SingleStoreDB both delivers exceptional performance for transactions and analytics, and does it at a lower TCO than most vendors,” said Suresh Sathymurthy, CMO, SingleStore.
“We set out to challenge a common industry assumption: that organizations need multiple databases to address transactional and analytical workloads,” said William McKnight, analyst, GigaOm. “We tried out the proclaimed ‘both sides database,’ SingleStore, against two common stacks with different operational and analytical databases, so we could determine relative TCO. Looking at the annual costs of the platform stacks and the time-effort costs from an enterprise perspective, the results were impressive for SingleStoreDB. The database merits serious consideration as a platform that can lower TCO while maintaining high operational and analytical performance.”
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SingleStore has been on the fast track of success with the launch of SingleStoreDB with IBM and its partnership with SAS to deliver ultra-fast insights at lower costs. With nearly 400 employees, SingleStore continues to grow rapidly due to strategic partnerships and investments from leaders like IBM, HPE and Dell. The company has been recognized with several industry awards, including San Francisco Business Times’ Fastest-Growing Private Companies in the Bay Area and the Deloitte Fast 500 awards. SingleStore won in four top rated categories from verified user review site TrustRadius in May and has been included in Gartner’s Magic Quadrant for Cloud Database Management Systems and The Forrester Wave™: Multi-model Data Platforms 2021 and Translytical Data Platforms Q4 2019 reports
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