SQream Secures $45 Million to Enhance Its Big Data and AI Capabilities

GPU-based big data platform SQream today declared the successful closure of its Series C funding round, bringing in $45 million from investors, including World Trade Ventures and other prominent backers like Schusterman Investments, George Kaiser Foundation (Atento), Icon Continuity Fund, Blumberg Capital, and Freddy & Helen Holdings.

With this new funding, the objective is to extend its operations within North America while enhancing AI and machine learning enterprise capabilities to increase its foothold in the big data and analytics markets.

"As artificial intelligence and machine learning rapidly evolve, we’re going to use this new funding to improve our AI and ML enterprise capabilities and stay at the cutting edge of innovation,\" said Deborah Leff, CRO of SQream, to Metaverse Post. \"We are investing in state-of-the-art technologies and research to deliver the most efficient data preparation infrastructure and AI/ML solutions to our clientele.\"

SQream’s platform utilizes the parallel processing power of GPUs, enabling businesses to handle extensive data sets with remarkable speed. The company focuses on minimizing reliance on hardware and reducing power consumption, setting it apart from traditional CPU-heavy data solutions.

"Our unique technique relies on the GPU's parallel data processing, which divides tasks into smaller segments and distributes them across multiple GPU and CPU cores, leading to quicker processing times and superior data management,\" stated Deborah Leff in her conversation with Metaverse Post. \"Our platform revitalizes legacy systems to meet contemporary analytical demands without necessitating significant investments in additional CPU or Cloud resources.\"

According to SQream, its platform shows significant benchmark enhancements, increasing data capacity while cutting ingestion times by 90%, preparation speeds by 90%, reducing footprint by 90%, and lowering costs by 80% through the application of familiar SQL commands in combination with data parallelism.

"With the rise of generative AI highlighting the necessity of integrating AI and ML into corporate strategy, we are witnessing a surge in interest towards our technology,\" commented Ami Gal, CEO of SQream, in a recent statement. \"Organizations are keenly focused on advancing their analytics capabilities, and this latest funding round is a significant milestone in our goal to equip clients with innovative analytics and processing solutions that enable them to extract valuable insights from their massive data pools and drive growth in ways previously deemed unachievable.\"

This announcement coincides with recent strategic moves by SQream, including its integration into the Samsung Cloud Platform Ecosystem.

Utilizing GPU Strength to Refine Data Frameworks

The company asserts that outdated data infrastructure is failing to meet the challenges presented by modern analytics projects, which forces businesses to limit their data analysis capabilities, risk producing incomplete complex reports, or deal with delays that hinder their project workflows.

SQream believes that incorporating GPUs into analytics tasks can usher in a transformative phase for corporate data analysis. They claim their solution can not only speed up the time-to-value but also bring down costs for handling terabyte-to-petabyte-scale datasets in AI/ML implementations and beyond.

Deborah Leff explained to Metaverse Post that SQream's proprietary GPU technology stands out because it bypasses the typical distributed data processing approach, which tends to cause cluster latency and operational bottlenecks.

Instead, SQream focuses on parallel data processing via GPUs, segmenting hefty workloads into smaller operations and distributing these tasks effectively among GPU and CPU cores.

"This synchronization enables SQreamDB to efficiently tackle complex analyses at unprecedented speeds, cutting costs by as much as 90%,\" said Deborah Leff. \"Moreover, our solution is designed to work seamlessly with diverse data infrastructures, including Hadoop, AWS S3, and Azure Blob, thanks to its adaptable plug-and-play architecture capable of integrating with existing ETL processes and data frameworks.\"

SQream serves a wide array of sectors, such as semiconductors, manufacturing, telecommunications, financial services, and healthcare. The company boasts that the SQream platform can swiftly process up to 100TB of raw data from sensors and logic controllers, transforming it into ready-to-analyze information on the same day, while also continuously supplying custom AI platforms.

SQream insists that its patented GPU technology can enhance nearly any data structure, boosting computational power and accelerating analytics speed.

"Our mission is to revolutionize the model training process by facilitating in-database model development. By harnessing the advantages of SQL powered by GPU acceleration, our offering maximizes efficiency and accelerates the training timeline,\" added Deborah Leff from SQream. \"With our platform, organizations can decrease time to insights, ensure swift ingestion and data preparation of large datasets, and enhance model accuracy. By eliminating the necessity for external tools and keeping all operations within the database, we significantly streamline machine learning processes and free up valuable resources.\"

Deborah Leff perceives that the analytics maturity curve has steepened dramatically with the advent of generative AI. This has left organizations feeling more lagged than they should be, driving them to focus intensely on overcoming hurdles rather than resigning to them.

"Generative AI has taken the spotlight with enormous anticipation and visibility, underscoring an organization’s analytical prowess. From our discussions with customers and prospects, there's an apparent urgency to resolve the challenges that are obstructing analytics and ML undertakings,\" noted Deborah Leff.

She anticipates that the recent team expansion efforts will position SQream to tap into a broader market.

"Organizations have pursued the aspiration of becoming data-driven, but as data volume rises and complexity escalates, the ultimate goal seems to keep shifting. Traditional hardware isn’t always capable of managing these evolving demands, often falling short or failing to keep pace,\" Deborah Leff stated to Metaverse Post. \"The Series C funding is aimed at bolstering our presence in the United States. We also plan to grow our delivery team to offer local software support to both customers and partner alliances.\"