One engine, many use cases
Every use case below runs on the same atomic engine, on your open tables, with zero data movement. Start with one that creates the most opportunity or dominates your bill.
Start here.
10x your lakehouse
High-concurrency SQL and AI on your open tables, with no migration.
Read moreScale your querying agent
Concurrency the way agents query, scaling by the vCPU with no coordinator.
Read moreReal-time event ingest for compliance
Retain everything and investigate in real time, inside your own perimeter.
Read moreReal-time ingest to object storage
Kafka, CDC, and HTTP straight to open Iceberg on your object storage.
Read moreSeven workloads, one engine.
Customer-facing analytics. Dashboards inside your product, where a slow query is a customer problem. e6data holds the SLA at production scale and cuts the cost of serving it.
PROOF: 15M CUSTOMER-FACING QUERIES A DAY AT P95 1.2S, 60-75% LOWER TCO · Learn more →
High-concurrency dashboards. Monday morning, everyone opens the same dashboard. With no coordinator to bottleneck, concurrency scales out instead of queueing up.
PROOF: 1,000+ QPS WITH P95 UNDER 2 SECONDS, ON THE RECORD · Learn more →
Ad-hoc and interactive analytics. Exploratory questions on full-grain data, without rationing which ones are worth the compute. Analysts keep their SQL and their tools.
PROOF: UP TO 10X FASTER QUERIES (P95) · Learn more →
Real-time streaming analytics. Kafka topics, HTTP sources, and CDC land as open Iceberg tables with sub-second ingest latency. No Flink pipeline to babysit, no separate ETL hop.
PROOF: STREAMING DATA QUERYABLE IN ~15 SECONDS · Learn more →
Security log analytics. Per-GB pricing forces teams to discard the events they later need. With no per-GB indexing fees, you retain everything on open tables and investigate in real time, inside your own perimeter.
PROOF: SUB-SECOND INGEST, QUERYABLE IN ~15S, NO PER-GB INDEXING FEES · Learn more →
Spark ETL, without the Spark bill. Existing PySpark jobs run end-to-end on the e6data engine through the Spark compatibility layer. Same scripts, per-vCPU economics.
PROOF: EXISTING PYSPARK ETL RUNS WITHOUT REWRITES · Learn more →
AI agents on live data. Agents query in bursts and in parallel, a concurrency pattern step-jump engines price badly. Built from the ground up for the concurrency agents demand.
PROOF: 1,000+ QPS WITH P95 UNDER 2S, SCALING BY 1 VCPU · Learn more →
See your numbers on your 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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