SQream Platform
GPU Powered Data & Analytics Acceleration
Enterprise (Private Deployment) SQL on GPU for Large & Complex Queries
Public Cloud (GCP, AWS) GPU Powered Data Lakehouse
No Code Data Solution for Small & Medium Business
By Raz Kaplan
TL;DR Data pipeline efficiency with GPU acceleration is finally available for everyone.
It is 2024, and every data-oriented role, from the junior data analyst to the CIO and CDO, knows that GPUs are great for big data analytics.
Initially designed for gaming, GPUs now drive advancements in AI and big data thanks to their superior parallel processing abilities. This makes them ideal for handling extensive datasets and executing complex algorithms swiftly, addressing the urgent demands of our data-intensive world.
GPUs enable not just quicker data processing but also facilitate near real-time analytics and the scaling of machine learning models, essential for tackling the challenges posed by rapidly growing data volumes across industries.
The boom in generative AI technologies has spiked demand for GPUs, underscoring their critical role in powering the technology that shapes our daily digital interactions and the infrastructures of tomorrow.
While the GPU infrastructure enables teams to train predictive language models such as GPT or Gemini, the first step in making it accurate lies in structured accurate data. As long as data teams keep using small samples, the hallucination rate in industrial and enterprise cases will remain high.
If we look at the challenges our customers share with us, keeping budgetary challenges aside for a moment, it all comes down to – speed and complexity.
The bottlenecks of the data pipeline, from raw data to ready-to-use insights and tables, involve complex queries like multiple JOINs and Massive SELECTs, when using the public cloud, those queries are becoming even more painful to the budget, pushing managers to make compromises that damage the quality and accuracy of the project.
Yet, the budget can not stay aside as research tells us that a high percentage of machine learning and artificial intelligence projects never make it to production because of the budget is over.
Today, we are releasing SQream Blue on Google Cloud, and AWS will be available shortly after. The GPU acceleration platform is now available for data teams of all sizes, assisting with overcoming the challenges of complexity, time, and budget:
Book a demo
Also, to keep the pipeline smooth, we’ve added a jobs orchestration UI. This allows teams to work simultaneously on multiple tasks, allocate resources, and get better visibility on the jobs.
As we announced earlier this year, as of now, SQream supercharges your Machine learning processes not only with fast, accurate and cost-efficient data preparation but also – In GPU Database model training. This unique ability reduces the time it takes to deploy machine learning models.
These pivotal advancements, SQream Blue and our enhanced machine learning process acceleration, are synergistically designed to catalyze data pipeline efficiency, ushering our customers into a new era of data empowerment.
With this formidable duo, enterprises can seamlessly navigate the complexities of big data processing, analytical insights generation, and machine learning model deployment at unprecedented speeds and cost-effectiveness.
In an age where agility and precision govern the data realm, SQream stands as your beacon, guiding you towards a future where data not only informs but transforms.
Engage with us, leverage these innovations, and redefine what’s possible in your data journey. The future of data is not just about managing or analyzing; it’s about thriving in an ecosystem where data becomes your unequivocal competitive advantage. With SQream, the future isn’t a distant dream; it’s a present reality. Venture with us, and let’s transform data into your enterprise’s most potent asset.