DATABRICKS KEEPS
The workloads it already serves well, and the workflows your teams built around it. Your data, in the same open Iceberg tables and catalogs you govern today. Its role in your contracts and your roadmap. Nothing is ripped out.
COMPATIBILITY
Keep Databricks. Add e6data to unlock more scale.
Point e6data at the workloads that strain your Databricks bill and SLAs, and cut their TCO by up to 50%. Same SQL, same open tables, same governance. No migration, no rewrites.
IN PRODUCTION
up to 60%lower TCO on moved workloads
up to 10xfaster queries (p95)
~99%less egress, multi-region and hybrid
One platform, two engines, each doing what it does best
E6DATA TAKES
High-concurrency and customer-facing queries: 1,000+ QPS with p95 under 2 seconds. Real-time work: streaming events queryable as open Iceberg in ~15 seconds. Multi-region, multi-cloud, and hybrid reach, with ~99% less egress.
Spark and PySpark compatible · same Delta and Iceberg tables · same catalog and governance · connect via JDBC / ODBC in about 30 minutes
WHERE TEAMS START
The first workloads to move
- 01Customer-facing dashboards
Scale embedded analytics to 1,000+ QPS on the same tables Databricks reads, without over-provisioning a warehouse for peak.
- 02Real-time streaming to Iceberg
Kafka topics and CDC land as open Iceberg tables with sub-second ingest, queryable in ~15 seconds, beside your Databricks data.
- 03Multi-region, multi-cloud, hybrid
Query data where it lives, in any region, cloud, or on-prem environment, and cut egress by ~99% instead of centralizing everything first.
- 04Spark ETL without rewrites
Existing PySpark jobs run end-to-end on the e6data engine through the Spark compatibility layer, at per-vCPU economics.
Don't take the word for it
The NASDAQ SaaS case ran 15M queries a day at p95 1.2s, vs 2.3s on Databricks.
Databricks compatibility questions
Does this replace Databricks?
No. e6data runs alongside Databricks on the same open tables and takes the workloads where it wins on cost or concurrency. Databricks keeps everything else.
Do my queries need rewriting?
No. e6data runs Spark SQL and PySpark through its compatibility layer, and dashboards connect through the same JDBC and ODBC interfaces they use today.
What about governance and access control?
Your existing catalog policies keep enforcing table, column, and row-level access. e6data inherits them rather than duplicating them.
How do I know which workloads to move?
Start with the ones that dominate the bill or miss SLAs at peak. Bring one to a demo and we will run it side by side on your tables.
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.