SQream Blue Sets New Cost-Performance Standards for Mass Data Processing
GPU parallelizing technology and patented compression solution perform 3x faster and at ⅓ the cost in TPCx-BB Benchmark Analysis
The nonprofit Transaction Processing Performance Council (TPC) developed TPCx-BB as a benchmark for objectively comparing Big Data Analytics System (BDAS) solutions. With its unique GPU parallelizing solution and patented technology, SQream Blue's recent performance demonstrates its ability to offer unparalleled ROI for SaaS big data projects at large-scale enterprises.
During the comparison portion of the TPCx-BB analysis, SQream Blue shattered the existing performance benchmark, handling 30 TB of data three times faster and at ⅓ of the cost of Databricks' Spark-based Photon SQL engine.
In its State of Big Data Analytics Report published in June of 2024, SQream found that 92% of companies surveyed are actively aiming to reduce cloud spend on analytics, 71% regularly experience 'bill shock', and 41% list high costs as the primary big data challenge. The performance of SQream Blue is equivalent to reading every cataloged book in the US Library of Congress in under an hour - and then buying them all for less than
"In cloud analytics, cost performance is the only factor that matters. SQream Blue's proprietary complex engineering algorithms offer unparalleled capabilities, making it the top choice for heavy workloads when analyzing structured data," said
As part of the TPCx-BB assessment, SQream Blue tackled several data processing tasks typically encountered in real-world scenarios. Example queries included building a model to predict whether an online shopper would be interested in a given item category based on their online activities and demographic information.
SQream ran the benchmark on Amazon Web Services (AWS) with a scale factor of 30,000, which creates a dataset of around 30 TB, to test the relative capabilities of SQream at scale. All generated data was stored as Apache Parquet files on Amazon Simple Storage Service (Amazon S3), and the queries were processed without pre-loading into a database.
SQream Blue was tested against the data management solution Databricks and its most advanced query engine. During the comparison, SQream Blue's total runtime was 2462.6 seconds, with the total cost for processing the data end-to-end being
SQream Blue's performance at breakneck speeds is thanks to the platform's ability to allocate available resources to ensure dynamic workloads are handled most efficiently, and to its capacity to divide data processing between GPUs and CPUs to avoid unnecessary overhead, ensuring unprecedentedly rapid results and optimal performance.
These groundbreaking capabilities and an increased demand for high-performance analytics solutions have positioned the company as a leader in the data processing industry. SQream is currently realigning its organizational structure to better support its ambitious growth plans, with a particular focus on the US market.
This strategic focus is supported by the company's commitment to innovation. For instance, the solution's architecture contributes to its streamlined efficiency, as data does not need to be moved at any point during the preparation cycle. Rather, SQream Blue directly accesses data in open-standard formats at the customer's low-cost cloud storage to maintain privacy and ownership, preserving a single source and eliminating the need for data duplication.
Engineers, analysts, and journalists are invited to speak with SQream for an exclusive live-action demo of these results alongside a product manager. SQream Blue is currently available on AWS and Google Cloud marketplaces.
For more information about SQream Blue and detailed results from the TPCx-BB benchmarks, visit: https://sqream.com/databricks-benchmarks
About SQream
SQream empowers companies to get value from their data that was unattainable before at an exceptional cost-performance. Our data processing and analytics acceleration platform utilizes a patented, GPU-based SQL engine that accelerates the querying of extremely large and complicated datasets. By leveraging SQream's advanced supercomputing capabilities for analytics and machine learning, enterprises can stay ahead of their competitors while reducing costs and improving productivity. To learn more, follow SQream on LinkedIn, Twitter, Facebook, or visit www.sqream.com.
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SOURCE SQream
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