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

01

The bill

Warehouse compute dominates the platform budget, and it grows faster than usage.

02

The SLAs

Concurrency peaks are when dashboards matter most, and when they slow down.

03

The migration risk

Every fix that starts with re-platforming is a year of risk before the first result.

OUR COMMITMENTS

How it answers, in your order

See the benchmarks
01

Drop 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
02

A 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
03

On 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
04

Real-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
CASE STUDY · NASDAQ SAAS LEADER

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.

Read the case study
p95 1.2s
query latency at 15M queries a day

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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