SOLUTIONS · USE CASES

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.

customer-facing analytics high-concurrency dashboards ad-hoc analytics real-time streaming security log analytics spark etl ai agents

FEATURED · TOP-OF-FUNNEL USE CASES

up to 10x

10x your lakehouse

High-concurrency SQL and AI on your open tables, with no migration.

QUERY ENGINEView use case →

1,000+

Scale your querying agent

Concurrency the way agents query, scaling by the vCPU with no coordinator.

QUERY ENGINEView use case →

$0

Real-time event ingest for compliance

Retain everything and investigate in real time, inside your own perimeter.

INGEST & INDEXView use case →

~15s

Real-time ingest to object storage

Kafka, CDC, and HTTP straight to open Iceberg on your object storage.

INGEST & INDEXView use case →

0115Mqueries a day

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

Read the case study →

021,000+QPS at p95 < 2s

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

Query Engine →

0310xfaster, up to (p95)

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)

Query Engine →

04~15sevent to queryable

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

Ingest engine →

05100%of events retained

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

Security & Reliability →

060rewrites required

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

Enrich & ETL →

071 vCPUper increment

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

AI solutions →

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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