Real-time ingest to object storage
Stream Kafka, CDC, and HTTP sources straight into open Iceberg tables on your object storage, queryable in ~15 seconds. No Flink pipeline to babysit, no separate ETL hop.
The problems this solves
Streaming to object storage is slow to query
Data lands, but only becomes useful hours later through a separate batch step.
Pipelines sprawl
Separate ingest, transform and index systems multiply cost and failure points.
Indexing fees and format lock-in
Per-GB charges and proprietary stores tax every gigabyte you keep.
Streams in, open tables out
One engine ingests, transforms, and indexes as data lands, writing governed Iceberg to your own object storage. It replaces Flink-style pipelines and separate ETL hops.
No custom glue code needed
Kafka, CDC, and HTTP, with no shuffle.
Writes open Iceberg you fully own.
Your object storage, in your account.
Registers in any catalog you run.
Read by any engine, not just ours.
Straight to open tables, no pipeline to run
Straight to open tables
Streams land as governed Iceberg you own, readable by any engine, not a proprietary index locked to one vendor.
No pipeline to babysit
Ingest, transform, and table maintenance run in one engine, replacing Flink-style pipelines and a separate ETL hop.
Your storage, your bill
Data sits in your own object storage with no per-GB indexing fees, so keeping more history costs storage and nothing else.
One engine from stream to query
Events are durably captured within a second of arriving on object storage.
From landing to queryable on open Iceberg in about fifteen seconds.
No per-gigabyte indexing fees, so retention is a value decision, not a cost one.
This use case runs on e6data Ingest engine
A streaming ingest engine that lands Kafka, CDC, and HTTP sources in open Iceberg tables with sub-second latency, queryable in ~15 seconds, with no per-GB indexing fees.
Questions your team will ask
Plain answers for the evaluator in the room.
Does this replace my streaming pipelines?+−
It replaces Flink-style pipelines and a separate ETL hop for getting streams into the lakehouse. Ingest, transform, and table maintenance run in one engine that writes open Iceberg.
Who can query the tables it writes?+−
Anything that reads Iceberg: the e6data Query engine, Spark, Trino, Snowflake, or Databricks. The output is your table, in your catalog, under your governance.
Will it run in my environment?+−
Any cloud, your VPC, on-prem, hybrid, air-gapped, or sovereign. Streams never leave your perimeter, and data lands in your own object storage.
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.
Prefer to self-serve? Problems we're solving →