GPU-powered solution analyzes more data faster to unlock insights and business value for leading organizations across data-driven industries.
The exponential growth of data presents both immense opportunities and challenges for organizations. Valuable insights are often buried across massive, complex datasets too large and unwieldy for traditional analytics tools to handle. SQream offers a purpose-built solution to help companies fully harness all their data to drive unprecedented speed and scale in analytics.
I recently had an illuminating discussion with Deborah Leff, Chief Revenue Officer of SQream, during Oracle CloudWorld to understand their unique value proposition, enabling customers to rapidly gain insights from massive structured data stores. She provided compelling examples of how prominent brands across industries leverage SQream to make more informed decisions powered by deep analytics.Built for Speed: Unleashing GPUs for Analytics
SQream’s founders have backgrounds in high-performance gaming, having witnessed firsthand the immense power of parallel GPU processing. They realized similar techniques could dramatically accelerate analytics on rapidly growing datasets standard in business.
Most analytics platforms rely on legacy CPU-based architectures. But purpose-engineered for structured data workloads, SQream employs patented technology to efficiently orchestrate arrays of GPUs for blazing-fast analytic throughput.
This unlocks three major transformational benefits for customers:
Analyze more data: Organizations can work with entire datasets versus small static samples, gaining a far more complete and nuanced picture for analysis. Leff shared an example where a major electronics manufacturer lacked visibility into over 90% of sensor data from their factories. SQream now allows them to leverage all this rich data.
Increased complexity: The brute force muscle of GPUs in parallel tackles tremendously more complex queries, joins, and data transformations easily, which cripple legacy systems. This removes constraints on the types of analysis users can perform.
Faster time-to-insight: With speed as the biggest advantage, insights that previously took days or weeks to assemble now arrive in mere hours or minutes when it matters most to influence decisions.
