Lakshmi Narayana G
Performance and Research Engineer @ e6data | Autonomous indexing for zero effort query acceleration (100 - 1,000X faster) on your data lakes and warehouses.
60% Lower TCO and 5-10x Superior Performance for the most demanding analytics workloads on your data lake, lakehouse Existing Data Warehouse and Data Lakehouse Engines fall short when it comes to the most demanding enterprise analytics workloads. Data engineering and Platform teams find it impossible to simultaneously achieve lower TCOs, ever-increasing usage, and query latency / throughput SLA's. e6data’s answer is to drive breakthroughs on the processing efficiency frontier. This pursuit drove us to a clean-slate, bold new approach to building MPP-style columnar processing and vectorized execution engines. Keep what's working, and get started with e6data only on your most expensive and/or challenging workloads on your existing data lakehouse: 60% Lower TCO, and 5-10x Faster Queries and Higher Concurrency 1) Works with your existing Data Lakehouse: Delta / Iceberg / Hudi / Hive 2) Secure, Private: fully within your AWS / GCP / Azure account (incl. existing VPCs) 3) No changes to BI / Reporting Tools, Application Layer, Queries 4) No changes to your ETL, data pipelines 5) Deploys in your existing Kubernetes cluster
