Qlik Open Lakehouse includes Amazon Athena support and multi-engine access, plus deployment in the customer’s virtual private cloud (VPC) with automatic Iceberg optimization and built-in data quality and lineage
Qlik has announced the general availability of Qlik Open Lakehouse, a fully managed Apache Iceberg service in Qlik Talend Cloud. It offers real-time data pipelines, automated optimization, and multi-engine access without lock-in, giving organizations a faster and more cost-effective foundation for AI and analytics.
Qlik Open Lakehouse runs in the customer’s own cloud with bring-your-own-compute. It combines change data capture (CDC) ingestion, automatic Iceberg optimization, and multi-engine access, letting teams use familiar tools like Amazon Athena, Snowflake, Spark, Trino, and Amazon SageMaker. Early users saw faster queries and lower infrastructure costs when moving workloads from proprietary warehouses to open Iceberg tables.
“AI stalls when data is slow, fragmented, and expensive,” said Mike Capone, CEO of Qlik. “Qlik Open Lakehouse fixes that by giving teams a real-time, Iceberg-based foundation they can run in their cloud at enterprise scale and query with the engines they already use. It brings performance, cost control, and governance into one motion so decisions happen faster and models improve every day.”
What’s new
- General availability of Qlik Open Lakehouse in Qlik Talend Cloud, deployed in the customer’s VPC with bring-your-own-compute for full security, performance, and cost control
- Multi-engine access on day one, including Amazon Athena support so teams can query Iceberg tables serverlessly alongside Qlik analytics and other engines
- SageMaker-ready data stored in governed Iceberg tables on Amazon Simple Storage Service (Amazon S3), making it easier for ML teams to access, prepare, and train models without building additional data copies
- Automatic Iceberg optimization for compaction, partitioning, and metadata maintenance to improve query performance and reduce storage footprint
- Low-latency pipelines from hundreds of sources using CDC, with built-in data quality, lineage, cataloging, and FinOps observability
- Qlik Analytics and AI on top with the Qlik engine and workflow automation so insights can trigger actions in business systems
“The general availability of Qlik Open Lakehouse translates Qlik’s long-term strategy into a tangible reality for companies adopting open table formats,” said Mike Leone, Principal Analyst, Enterprise Strategy Group. “Its ability to handle large amounts of data quickly, optimize it in real time, and work with different tools in the cloud solves common problems with data being out of date, slow, or expensive. Because it also has Qlik’s robust integration and data governance, it provides a strong platform for AI and analytics that teams can adopt without having to completely rebuild their systems or switch to new tools.”
Why it matters
AI value is bottlenecked by data. Qlik Open Lakehouse closes that gap by giving enterprises data and analytics foundations for AI: trusted, explainable, and up-to-date data in an open format that any engine can query. The result is faster decision-making, lower total cost, and freedom of choice across analytics and ML. In preview, customers saw up to 5x faster query performance and up to 50 percent lower infrastructure cost as they removed unnecessary copies and tuned Iceberg tables at scale.
How it works
- Open by design: Data lives in Apache Iceberg on customer object storage. The same tables are queryable from Qlik, Amazon Athena, Snowflake, Spark, Trino, and ML services like Amazon SageMaker.
- Real time by default: CDC keeps tables current. Automatic optimization maintains performance as data grows.
- Governed and trusted: Integrated data quality rules, lineage, cataloging, and access controls provide the assurance AI and regulated workloads require.
- Built for action: The Qlik engine and automation connect insight to workflow, so teams do not stop at dashboards.
Availability
Qlik Open Lakehouse is available today for Qlik Talend Cloud customers, including Amazon Athena support. SageMaker integration for model training and inference on Iceberg data is supported via standard AWS patterns. Additional ecosystem updates are targeted for Q4 2025.
