Qlik announces open lakehouse on talent cloud

Qlik has announced the general availability of Qlik Open Lakehouse, a fully managed Apache Iceberg service within Qlik Talend Cloud. Designed to support real-time data pipelines, automated Iceberg table optimisation, and access via multiple query engines, the solution enables organisations to manage data in an open format within their own cloud environments.

It aims to reduce both the time and cost involved in transforming data into actionable insights, particularly for analytics and artificial intelligence (AI) use cases.

Deployed within the customer’s own virtual private cloud (VPC) using a bring-your-own-compute model, Qlik Open Lakehouse combines change data capture (CDC) ingestion with automated optimisation of Iceberg tables. This allows organisations to maintain performance as data volumes increase while avoiding reliance on proprietary data storage.

The platform supports integration with widely used query and machine learning (ML) engines, including Amazon Athena, Snowflake, Apache Spark, Trino, and Amazon SageMaker, enabling teams to continue using existing tools without requiring significant changes to their workflows or infrastructure.

During the preview phase, customers reported improvements in query performance and reductions in infrastructure costs after migrating workloads from proprietary warehouses to open-format, optimised Iceberg tables. The service supports data storage on Amazon S3 in a governed Iceberg format, making it easier for ML teams to access, prepare, and train models directly on live data without creating redundant copies. Automatic table optimisation features handle compaction, partitioning, and metadata maintenance, which helps improve overall system efficiency while controlling storage growth.

Qlik Open Lakehouse also integrates with Qlik’s analytics and automation capabilities, enabling insights to trigger actions within business systems. The platform offers native support for real-time data updates via CDC from a wide range of data sources. Additionally, it incorporates data quality rules, lineage tracking, access controls, and cataloguing tools, supporting the requirements of both enterprise-scale operations and regulated workloads.

Mike Capone, CEO of Qlik, stated that AI initiatives often stall due to issues with data speed, fragmentation, and cost. He noted that Qlik Open Lakehouse seeks to resolve these challenges by offering a real-time, Iceberg-based foundation that can operate within the customer’s cloud infrastructure and work with familiar data engines. According to Capone, the solution brings together performance, governance, and cost control to accelerate decision-making and improve model outcomes.

Mike Leone, Principal Analyst at Enterprise Strategy Group, commented that this release marks a tangible step in Qlik’s broader strategy. He highlighted the platform’s capacity to handle large volumes of data efficiently, its compatibility with diverse cloud tools, and its ability to provide governance and integration without requiring organisations to adopt entirely new systems. According to Leone, the combination of performance optimisation, open format support, and data governance makes Qlik Open Lakehouse a practical solution for teams adopting AI and analytics at scale.

The platform is designed with an open architecture, storing data in Apache Iceberg format on the customer’s object storage. The same data tables can be queried by Qlik, Amazon Athena, Snowflake, Spark, Trino, and services such as SageMaker, enabling flexibility in analytics and machine learning workflows.

Real-time data updates are ensured through CDC, while automatic optimisation features maintain system performance over time. Governance capabilities, including data quality checks, lineage, access control, and cataloguing, support trust and transparency, important factors for regulated environments and AI applications. Qlik’s analytics engine and automation layer enable organisations to act on insights rather than stopping at visualisation.

Qlik Open Lakehouse is now available to Qlik Talend Cloud customers, with support for Amazon Athena included. Integration with Amazon SageMaker for model training and inference on Iceberg-formatted data is supported through standard AWS deployment patterns. Additional ecosystem updates are expected in the fourth quarter of 2025.