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
Configure the Kafka topics and Iceberg catalog once in Streambased - ISK handles the rest
2
Deploy
Point your Iceberg-compatible tools at ISK's catalog and data endpoints.
3
Connect
Your topics are immediately queryable. Use any Iceberg-compatible analytics engine to read your 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.
What is Streambased K.S.I.
Cost Effective Kafka With Iceberg Tiered Storage
Streambased K.S.I. (Kafka Service for Iceberg) unifies long term storage in Apache Iceberg with recent data in Kafka to create a complete view for Kafka clients.
What You Get with Streambased K.S.I
Cheap Infinite Retention
Store Kafka data forever in cost effective object storage. No more cost/retention compromises mean a richer/more resilient source for your Kafka applications.
No Connector Overhead
Remove deployment, maintenance and evolution overhead that surrounds traditional ETL based approaches to long term Kafka data storage.
Unified Governance
Apply the entire Kafka ecosystem (ACLs, quotas, schemas etc.) to your long term data and see instant updates on changes.
Works With Your Stack
Seamlessly integrates with any Kafka or Kafka compatible distribution (Confluent, Amazon MSK, Aiven etc.) and any Iceberg catalog (Polaris, Unity, LakeKeeper, Nessie).
Single Source of Truth
No duplication or drift. Events are stored only once as either Kafka or Iceberg ensuring no opportunity for inconsistencies can arise.
No Iceberg Maintenance
Combined with Streambased I.S.K. [Ink], K.S.I.’s view based approach ensures that Iceberg is managed in a way that removes traditional maintenance requirements such as compaction and snapshot expiration.
Getting Started in Minutes, not Months
1
Configure
KSI requires only Kafka connection and Iceberg catalog configurations.
2
Deploy
K.S.I. is container based, stateless and horizontally scalable. Deploy into your Kubernetes or Docker and be up and running instantly.
3
Connect
Point your Kafka clients at the K.S.I. endpoints and experience the full dataset Kafka experience.
Check The Docs
Purpose built for Streaming
Cost Reduction
Offload expensive Kafka storage to cheap and universal object storage. With K.S.I. Kafka provides a very small “hottest” storage with the majority of data stored in Iceberg.
State Hydration
Replay stream processing state from a complete dataset employing the complete Kappa architecture pattern.
Lagging Consumers
No longer can a lagging consumer drop off the end of retention and cause data loss. With K.S.I. consumers can access data all the way back to the beginning of time.
Kafka as a System of Record
Kafka, as the typical entry point into an organisation’s data estate, is the ideal system of record for data. With K.S.I. this capability can finally be realized providing the single source of truth for ingested data.
What is Hyperstream
High-performance analytical queries across Kafka and Iceberg
Hyperstream indexes Kafka and Iceberg for ultra-fast SQL.
Up to 30x performance improvement vs today’s SQL on Kafka products.
What you get with Hyperstream
Faster query performance
Hyperstream lets you filter data by message fields like user, timestamp or status without scanning entire datasets, dramatically reducing query time.
Efficient non-sequential access
Instead of being limited to Kafka’s offset-based access pattern, you can query data flexibly based on business-relevant attributes.
Reduced compute costs
Avoiding full-table scans saves more than just time. Hyperstream lowers the amount of processing required for queries, saving infrastructure and reducing peak loads.
Improved scalability
As data grows, traditional query performance drops. Hyperstream queries remain performant, making it easier to handle large-scale streaming and analytical workloads.
Better support for analytical use cases
Indexes power filtering and aggregations present in 99% of analytical use cases. Hyperstream brings these directly to the data.
Seamless integration
Hyperstream is SQL based, ensuring compatibility with all Iceberg engines.
Getting started in minutes, not months
1
Configure
Configure an Iceberg engine and Streambased I.S.K. data source – Hyperstream does the rest.
2
Deploy
Manage index lifecycles with Streambased UI/REST tools.
3
Connect
Add Hyperstream’s powerful SQL tools to your application code and see immediate speed up.
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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