SQream, a startup hailing from Israel that specializes in enhancing data and analytics workloads through GPU-driven technologies, has just unveiled its latest achievement – securing $45 million in a series C funding round. The company intends to utilize this fresh capital injection to bolster its presence in North America, foster strategic partnerships, and drive advancements in AI and machine learning (ML) capabilities as well as big data analytics.
This substantial investment was spearheaded by World Trade Ventures and featured participation from both new and existing investors, including Schusterman Investments, George Kaiser Foundation, Icon Continuity Fund, Blumberg Capital, and Freddy & Helen Holdings. This brings SQream’s total capital raised to an impressive $135 million, a development that coincides with the rapid proliferation of data and analytics workloads, prompting organizations to augment their infrastructure investments to keep pace.
Ami Gal, CEO of SQream, acknowledged this trend, stating, “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.”
He added, “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.”
Addressing the Data Challenge
Analytics projects have burgeoned over the years and show no signs of slowing down, largely due to the ongoing explosion of data. According to IDC estimates, the global datasphere is projected to reach a staggering 163 zettabytes by 2025, with 60% of it attributed to enterprise data. This deluge of information poses a daunting challenge for teams striving to extract valuable insights for business growth and competitiveness.
When dealing with trillions of records, legacy infrastructure reliant on CPUs often struggles to keep pace, necessitating organizations to curtail the amount of data they can analyze or risk falling behind. Many organizations attempt to circumvent this issue by investing in the hardware and computing resources required, thereby increasing costs.
SQream, founded in 2010, tackles these challenges by harnessing the power of GPUs, which offer substantial parallel processing capabilities essential for demanding data and analytics workloads. The company’s proprietary GPU-based query optimization engine powers two key products: SQreamDB SQL database and SQream Blue, a cloud-native, fully-managed data preparation lakehouse.
Deborah Leff, Chief Revenue Officer at SQream, explained, “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.”
According to the company’s website, SQream Blue lakehouse can provide time-sensitive insights at half the cost and twice the speed of traditional cloud warehouses and query engine solutions. In some instances, the solutions have managed to reduce data ingestion and preparation times by 90% and costs by 80%, all while maintaining familiar SQL processes. Furthermore, they allow companies to process extremely large datasets with a smaller carbon footprint, using fewer hardware resources and less energy compared to conventional big data solutions reliant solely on CPUs.
Diverse Sector Applications
SQream’s success extends across various sectors, with a client base spanning semiconductors, manufacturing, telecommunications, financial services, and healthcare. Notable enterprises benefiting from SQream’s solutions include Samsung, LiveAction, Sinch, Orange, AIS, and LG.
In one noteworthy case, an electronics manufacturer employing SQream’s offerings managed to reduce the cost of data collection and loading by a remarkable 90%, resulting in a production yield increase from 50% to 90%. Leff elaborated, “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.”
With this latest funding round, SQream has ambitious plans for further expansion. The company intends to bolster its team and footprint in North America, enhance AI and ML capabilities, and solidify its position in the big data and analytics markets.
While SQream counts established data infrastructure players like Snowflake and Databricks among its main competitors, it operates in the GPU-accelerated analytics space, a sector also targeted by companies such as BlazingDB, Kinetica, and Heavy AI (formerly OmniSci and MapD), each with their respective products.