Dashboards slow down under load
Customer-facing analytics misses its SLA exactly when concurrency peaks and it matters most.
USE CASE
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.
IN PRODUCTION
up to 10xfaster queries (p95)
up to 60%lower compute cost
PROBLEMS THIS SOLVES
The problems this solves
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.
BI & reporting
W/O E6DATA
AI & MLad-hoc analysisAPIsEXISTING QUERY ENGINEany governance · any catalogany table formatany cloud · on-prem · hybrid
BI & reporting
W/ E6DATA
AI & MLad-hoc analysisAPIse6dataQUERY ENGINE · SCALES BY 1 VCPUany governance · any catalogany table formatany cloud · on-prem · hybrid
Point the SQL endpoint at your existing setup. Zero data movement, no query rewrites.
RUNS WITH YOUR DATA STACK
No custom glue code needed
LAKEHOUSE
Queries directly, with zero data movement.
TABLE FORMAT
Iceberg, Delta, and Hudi, at full performance.
CATALOG
Plugs into any catalog, no rule rewrites.
APPLICATION
Connects to any BI, RAG app, or agent.
GOVERNANCE
Inherits your existing controls and policies.
Query everything, scale and secure on your own stack
proof: one engine, your open tables
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.
proof: one engine, your open tables
proof: scales in 1-vCPU increments
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.
proof: scales in 1-vCPU increments
proof: up to 60% lower compute cost
Guardrails that stop costly queries
Set thresholds per cluster and log, alert, or cancel a runaway query in real time, before it wastes compute.
proof: up to 60% lower compute cost
RESULTS
What teams see in production
10xfaster queriesThe same SQL returns roughly ten times sooner on the tables you already run.
up to 50%lower compute costPer-vCPU billing and no idle over-provisioning cut the heaviest line on the bill.
1,000+QPS at p95 < 2sConcurrency climbs while latency stays flat, with no coordinator bottleneck.
POWERED BY · QUERY ENGINE
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.
TALK TO US
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.
Problems we’re solvingThanks.
An actual person replies, usually within one working day.