Cut your heaviest workloads, on your lakehouse, no migration
The bill grows with every dashboard, and the SLAs slip exactly when concurrency peaks. e6data takes the workloads that hurt and runs them beside the platforms you already trust.
The three problems on your desk
- 01The bill
Warehouse compute dominates the platform budget, and it grows faster than usage.
- 02The SLAs
Concurrency peaks are when dashboards matter most, and when they slow down.
- 03The migration risk
Every fix that starts with re-platforming is a year of risk before the first result.
How it answers, in your order
- 01Drop in, don't rip out
e6data works alongside Databricks, Snowflake, and Trino on the same tables. No migration, no rewrites, no governance changes; the exit is re-pointing, not re-platforming.
proof: zero data movement, zero query rewrites - 02A fraction of the cost
Scaling in 1-vCPU increments kills over-provisioning: you pay for the compute a query needs and nothing around it. Full-grain data, no down-sampling.
proof: 1,000+ QPS with p95 under 2s - 03On your data, where it lives
Run in your cloud, your VPC, on-prem, or a sovereign environment, on the open tables you already govern. Compute comes to the data, so egress drops by ~99%.
proof: events queryable as open Iceberg in ~15s - 04Real-time by default
Streaming data is queryable in ~15 seconds, with sub-second ingest latency. No per-GB indexing fees, so you keep every event instead of rationing retention.
proof: sub-second ingest to open Iceberg tables
15M customer-facing queries a day at 60-75% lower TCO
A data platform team serving production dashboards straight off open tables, beside the platforms already in place.
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 solvingAn actual person replies, usually within one working day.