10x your lakehouse
Run high-concurrency SQL and AI workloads directly on your open tables. e6data is decentralized and Kubernetes-native, scaling in 1-vCPU increments, for teams facing throttling, rationing, and lock-in.
The problems this solves
Dashboards slow down under load
Customer-facing analytics misses its SLA exactly when concurrency peaks and it matters most.
The bill outruns usage
Warehouse compute grows faster than the workload, with no ceiling in sight.
Scaling means re-platforming
Every proposed fix starts with a migration and a year of risk before any result.
Same stack, one engine swapped in
Your tools, catalogs, formats, and clouds stay exactly where they are. e6data replaces the query engine underneath, with zero migration.
Point the SQL endpoint at your existing setup. Zero data movement, no query rewrites.
No custom glue code needed
Queries directly, with zero data movement.
Iceberg, Delta, and Hudi, at full performance.
Plugs into any catalog, no rule rewrites.
Connects to any BI, RAG app, or agent.
Inherits your existing controls and policies.
Query everything, scale and secure on your own stack
SQL meets AI, in your lakehouse
Query structured and unstructured data with vector search on one engine. No separate vector database, no retrieval pipeline to maintain.
Autoscaling that tracks load
Set a floor and a ceiling; executors scale with query load in 1-vCPU increments, with no latency spikes and no manual tuning.
Guardrails that stop costly queries
Set thresholds per cluster and log, alert, or cancel a runaway query in real time, before it wastes compute.
What teams see in production
The same SQL returns roughly ten times sooner on the tables you already run.
Per-vCPU billing and no idle over-provisioning cut the heaviest line on the bill.
Concurrency climbs while latency stays flat, with no coordinator bottleneck.
This use case runs on the e6data Query engine
Kubernetes-native SQL and AI analytics on your open tables: 1,000+ QPS at p95 under 2s, scaling by the vCPU with no coordinator single point of failure.
Questions your team will ask
Plain answers for the evaluator in the room.
Do I have to migrate to get the 10x?+−
No. e6data points at your existing tables through a JDBC or ODBC endpoint, with zero data movement and no query rewrites. Only the engine underneath changes.
How does it hold up at high concurrency?+−
1,000+ QPS with p95 under 2 seconds, in production. With no coordinator to bottleneck, concurrency scales out in 1-vCPU increments instead of queueing.
Will it run in my environment?+−
Any cloud, your VPC, on-prem, hybrid, air-gapped, or sovereign. It is Kubernetes-native and inherits your existing IAM and catalog policies.
Book a demo on your own workloads
Reach out to book a demo, share challenges you're facing, and tell us how this fits into what you're currently working on or thinking about.
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