What is Streambased I.S.K.
The freshest view of your Kafka data without overheads
Streambased I.S.K. (Iceberg Service for Kafka) projects Kafka topics directly as Apache Iceberg™ tables – instantly and without duplication.
This zero-copy architecture makes every topic immediately queryable in Iceberg, giving you the freshest view of your data, while removing the operational overhead that normally comes with pipelines and maintenance.
What You Get with Streambased I.S.K.
The Freshest View
Data in Kafka is queryable in Iceberg the moment it lands. Dashboards, investigations and ML models always stay in step with the stream.
No Ops Overhead
No compaction jobs, no snapshot cleanup, no repartition rewrites. Data stays in Kafka, and Iceberg is just a logical view.
Unified Governance
Kafka’s access rules, ACLs and retention windows carry over directly. The same policies apply whether you query a second ago or a year back.
Works With Your Stack
Runs on any Kafka distribution, plugs into any Iceberg engine (Trino, Spark, DuckDB, Snowflake, Databricks) and any catalog (Hive, Glue, Nessie).
Single Source of Truth
No duplication or drift. Kafka remains the system of record; Iceberg reflects it consistently for every client.
Instant Schema Evolution
When a schema changes in Kafka, it’s instantly visible in Iceberg. No remapping, no rebuilds, no downtime.
Getting Started in Minutes, not Months
1
Configure
Define the topics and Iceberg catalog once - I.S.K. handles the rest.
2
Deploy
Point your Iceberg-compatible tools at the catalog or filesystem endpoints, and your topics are immediately queryable in Iceberg.
3
Connect
Use any Iceberg-compatible analytics engine to query Kafka data as tables.
Check The Docs
Purpose-Built for Real-Time Analytics
Dashboards and Reporting
Keep dashboards and reports aligned with live data. Every Kafka topic is instantly available in Iceberg, so teams can query fresh events without waiting for pipelines to finish.
Machine Learning and AI
Models are never stale. Streambased projects Kafka topics as Iceberg tables, so feature stores and predictions are updated with the most recent data while retaining the full history.
Audit and Compliance
Retention windows and access controls carry over from Kafka. Auditors see the same policies applied consistently, from the most recent trade to years of archived activity.
Data Science Exploration
Queries combine today’s activity with years of history in one place. Analysts and ML engineers can join and analyse streams with accuracy and freshness guaranteed.
Let’s find the right solution for your data
We’re here to help you unlock the full potential of your streaming data. Tell us about your challenges or ideas — and let’s explore how Streambased can support your business.
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