While organizations rush to leverage technologies like cloud and Gen AI, inordinate amounts of data are resulting in analytics costs that are resulting in bill shock, as data acceleration platform SQream found last month after their industry wide 2024 State of Big Data Analytics Report.
The report 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.
Now, SQream has announced the latest performance benchmarks for its cloud data solution, SQream Blue. The results set new industry standards in big data processing, data preparation, and ingestion, showcasing significant improvements in both speed and cost efficiency.
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 $25, highlighting how the solution can solve the cost-performance challenges associated with modern data analytics as a powerful addition to the tech stack.
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 — Matan Libis, VP Product at SQream
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 ? of the cost of Databricks’ Spark-based Photon SQL engine.
“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 Matan Libis, VP Product at SQream. “Databricks users and analytics vendors can easily add SQream to their existing data stack, offload costly intensive data and AI preparation workloads to SQream, and reduce cost while improving time to insights,” added the executive.
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 $26.94. Databricks’ total runtime was 8332.4 seconds, at a cost of $76.94, indicating a significant cost-performance advantage by SQream Blue for big data analysis.
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.
By leveraging the massive parallelism, high memory bandwidth, and seamless scalability of GPUs, organizations can overcome the limitations of traditional CPU-based systems and unlock new levels of insight and productivity — Deborah Leff, Chief Revenue Officer at SQream
Deborah Leff, Chief Revenue Officer at SQream, has already been stressing on how GPU acceleration stands as a critical enabler of agile, efficient, and cost-effective data operations, “By leveraging the massive parallelism, high memory bandwidth, and seamless scalability of GPUs, organizations can overcome the limitations of traditional CPU-based systems and unlock new levels of insight and productivity.”
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.
