Qlik®, a global leader in data integration, data quality, analytics, and artificial intelligence (AI), recently announced the general availability of Qlik Open Lakehouse, a fully managed Apache Iceberg service in Qlik Talend Cloud® that delivers real-time pipelines, automated Iceberg optimization, and true multi-engine access without lock-in. The result is an AI-ready data foundation that cuts time and cost between data and action.
Deployed in the customer’s own cloud account with bring-your-own-compute, Qlik Open Lakehouse combines change data capture (CDC) ingestion with automatic Iceberg optimization and multi-engine access so teams can use the tools they already rely on, including Amazon Athena, Snowflake, Spark, Trino, and Amazon SageMaker for machine learning (ML). In preview, customers reported faster queries and meaningfully lower infrastructure costs as they shifted workloads from proprietary warehouses to open, optimized 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.”
With the general availability of Qlik Open Lakehouse in Qlik Talend Cloud, customers can now deploy directly within their VPC and leverage bring-your-own-compute for greater security, performance, and cost optimization. From day one, it supports multi-engine access, including Amazon Athena, enabling teams to query Iceberg tables serverlessly alongside Qlik analytics and other engines. Data stored in governed Iceberg tables on Amazon S3 is SageMaker-ready, making it easier for ML teams to access, prepare, and train models without the need to create additional copies.
The platform automatically optimizes Iceberg tables for compaction, partitioning, and metadata maintenance, improving query performance while reducing storage footprint. It also offers low-latency pipelines from hundreds of sources using CDC, combined with built-in data quality, lineage, cataloging, and FinOps observability. On top of this, Qlik Analytics and AI capabilities integrate with the Qlik engine and workflow automation, ensuring that insights can trigger actions in business systems.
According to Mike Leone, Principal Analyst at Enterprise Strategy Group, this launch brings Qlik’s long-term strategy into reality by delivering a platform that can handle large data volumes quickly, optimize them in real time, and work seamlessly with various cloud tools, solving the common challenges of outdated, slow, or costly data. Because of its strong integration and governance, Qlik Open Lakehouse offers enterprises a solid foundation for AI and analytics without requiring them to rebuild systems or adopt entirely new tools.
Ultimately, the solution addresses the bottleneck between AI value and data availability by providing trusted, explainable, and up-to-date data in an open format that any engine can query. This results in faster decision-making, lower infrastructure costs, and greater flexibility across analytics and machine learning. In preview, customers reported up to five times faster query performance and as much as 50 percent lower infrastructure costs by removing unnecessary copies and optimizing Iceberg tables at scale.
By design, Qlik Open Lakehouse is open, real-time, governed, and action-oriented: data resides in Apache Iceberg on customer object storage and is queryable by engines such as Qlik, Amazon Athena, Snowflake, Spark, Trino, and services like Amazon SageMaker; CDC ensures tables stay current with automatic optimizations maintaining performance as data grows; integrated data quality, lineage, cataloging, and access controls provide the trust required for AI and regulated workloads; and Qlik’s engine and automation connect insights directly to workflows so teams can move beyond dashboards into action.
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