SQream nabs $45M to accelerate data analytics with GPUs

SQream nabs $45M to accelerate data analytics with GPUs

Head over to our on-demand library to view sessions from VB Transform 2023. Register Here

Israel-based SQream, a startup that accelerates data and analytics workloads with GPU-driven technologies, today announced $45 million in a series C round of funding. The company said it plans to use this capital to expand its presence in North America, extend strategic partnerships and propel further advancements in AI/machine learning (ML) capabilities and big data analytics.

The investment has been led by World Trade Ventures with participation from new and existing investors, including Schusterman Investments, George Kaiser Foundation, Icon Continuity Fund, Blumberg Capital and Freddy & Helen Holdings. It takes the total capital raised by SQream to $135 million and comes at a time when data and analytics workloads are increasing at a breakneck pace, forcing companies to up their infrastructure investments to keep up.

“As generative AI shines a light on the importance of leveraging AI and ML within enterprises as well as on the value of GPUs as part of the analytics process, we have seen interest in our technology skyrocket,” Ami Gal, CEO of SQream, said in a statement. 

“Companies are very focused on driving analytics maturity right now, and this recent funding round is another step in our mission to better equip our customers with cutting-edge data analytics and processing solutions that empower them to derive meaningful insights from their vast datasets and drive growth in ways previously thought impossible,” he added.


VB Transform 2023 On-Demand

Did you miss a session from VB Transform 2023? Register to access the on-demand library for all of our featured sessions.


Register Now

The data problem

Analytics projects have grown over the years and are nowhere near slowing down, thanks to the ongoing explosion of data. According to IDC estimates, the global datasphere will touch 163 zettabytes by 2025, and 60% of it will be enterprise data. Navigating this mountain of information and putting it to good use will be nothing short of a nightmare for teams looking to drive valuable insights for business growth and competitive edge.

When the volume of records is on the scale of trillions, legacy infrastructure relying on CPUs can struggle to keep up, requiring companies to limit the amount of data that can be analyzed or risk falling behind. Most organizations try to work around this by investing in the hardware and computing resources critical for the task – which adds to their cost.

SQream, which was founded back in 2010, works to solve these problems by tapping the power of GPUs that deliver massive and parallel processing capabilities needed for hefty data and analytics workloads. The company’s patented GPU-based query optimization engine runs two key products: SQreamDB SQL database and SQream Blue cloud-native, fully-managed data preparation lakehouse.

“SQreamDB is distinctively architected to utilize the power of GPUs to accelerate data analytics. This GPU-centric approach means SQreamDB can process large volumes of data much faster than traditional, CPU-based data warehouses. Meanwhile, SQream Blue leverages the same technology and takes it to the world of data lakehouses, enabling a much more cost-effective cloud data preparation in massive workloads,” Deborah Leff, chief revenue officer at SQream, told VentureBeat.

According to the company’s website, SQream Blue lakehouse can deliver time-sensitive insights at half the cost and twice the speed of traditional cloud warehouse and query engine solutions. 

In some cases, the solutions were able to reduce data ingestion and preparation times by 90% and costs by 80%, all while using familiar SQL processes. Further, they allow companies to process extremely large datasets with a smaller carbon footprint, using less hardware and consuming less energy than conventional big data solutions that rely strictly on CPUs.

Use across sectors

While Leff did not share how its revenue has grown over the last few fiscals, she did note that SQream currently serves a large customer base covering industries such as semiconductors, manufacturing, telecom, financial services and healthcare. Some of the enterprises it works with are Samsung, LiveAction, Sinch, Orange, AIS and LG.

In one case, Leff said, an electronics manufacturer using SQream’s offering has been able to reduce the cost of data collection and loading by 90%, increasing its production yield from 50% to 90%. 

“SQreamDB replaced the (manufacturer’s) legacy Hadoop-based ecosystem with only three compute nodes accelerated by 12 GPUs, responsible for more than 280 automated daily reports, preparation of data as part of the ML pipeline and ad-hoc manual complex queries as required. On a daily basis, it handles up to 100TB of raw data generated by the manufacturing equipment sensors and logic controllers, transforming it into analytics-ready data on the same day,” she explained.

Now, SQream plans to build on this work. The company plans to use the latest fundraise to expand its team and footprint in North America, extend AI/ML capabilities and further solidify its position in the big data and analytics markets.

While the company counts mainstream data infrastructure players like Snowflake and Databricks as its biggest competitors, it must be noted that it is not alone in operating in the GPU-accelerated analytics space. Companies such as BlazingDB, Kinetica, and Heavy AI (formerly OmniSci and MapD) are also targeting the same area with their respective products.

VentureBeat’s mission is to be a digital town square for technical decision-makers to gain knowledge about transformative enterprise technology and transact. Discover our Briefings.

Source link

Five ways CISOs are using AI to protect their employees' digital devices and identities Previous post Five ways CISOs are using AI to protect their employees’ digital devices and identities
Can the quick grocery delivery model only work in emerging markets? | TechCrunch Next post Can the quick grocery delivery model only work in emerging markets? | TechCrunch