SQream

What do we bring to the DATA table? 

• UNMATCHED SCALE & SPEED: SQreamDB can easily query up to 100 PBs of data with the simplicity of ANSI-SQL, using the SQream Acceleration Studio or other popular BI tools. At every scale, queries will run X5 faster and business insights will become more relevant than ever.
• FLEXIBLE ARCHITECTURE: Compute and storage are completely separated, without processing performed in-memory. Multiple compute nodes run in parallel, storing and retrieving data from a single or multiple storage sources, delivering greater flexibility and allowing easy scalability.
• COST-EFFICIENT RESOURCES: Relying on fewer compute nodes, SQreamDB deployments are only 1/10 of the hardware, and as a result 1/10 of the carbon footprint. Moreover, SQream’s auto-compression of 5:1 ratio requires less storage resources.
• WIDE ECOSYSTEM: SQream integrates into existing architectures, and supports industry-standard ODBC and JDBC, as well as Python, Java and others.
• EXTENSIBLE FOR MLOPS: Data preparations for MLOps are made effortless and robust with SQreamDB. Every data scientist or engineer can connect SQream’s engine to its familiar Python environment, and using simple SQL run large-scale data preparation, creating bigger and more quality training sets.