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Qlik has significantly advanced its Open Lakehouse platform by introducing streaming data ingestion and real-time transformation capabilities, alongside expanded ecosystem integrations. These enhancements are designed to provide enterprises with a governed, open data foundation that supports operational AI and analytics with fresher data, moving beyond traditional batch processing delays.
Qlik Open Lakehouse now supports streaming ingestion from Apache Kafka, Amazon Kinesis, and Amazon S3.
New real-time transformation features allow data cleansing and filtering as data lands.
Comprehensive data quality, lineage, and Qlik Trust Score are applied automatically to governed Apache Iceberg tables.
The platform expanded zero-copy mirroring to Databricks and Amazon Redshift, with support for Snowflake Open Catalog.
All ingestion and transformation processes are offloaded from data warehouse compute, optimizing costs.
The goal is to shorten the time from data generation to actionable insight for AI and analytics.
The latest updates target the need for timely data in operational AI initiatives. By enabling streaming ingestion and on-the-fly transformations, Qlik Open Lakehouse ensures that high-volume event data lands in governed Apache Iceberg tables ready for immediate use. "The next phase of AI is operational," said Drew Clarke, EVP Product and Technology at Qlik. "It runs on fresh, governed data, not nightly batches. It shortens time to action and turns AI from pilots into performance."
The new capabilities allow teams to ingest streaming events without consuming expensive data warehouse compute. As data arrives, transformations such as cleansing, filtering, and normalization are applied. The platform also automatically handles Iceberg table optimization for sustained performance. In a major ecosystem play, Qlik added zero-copy mirroring to Databricks and Amazon Redshift, alongside existing Snowflake support, allowing the same Iceberg datasets to be queried across platforms without data duplication. Enhanced Apache Spark compatibility and support for Snowflake Open Catalog further simplify data architecture.
This release addresses a common bottleneck where real-time work stalls due to data latency or quality issues. Qlik Open Lakehouse now unifies ingestion, transformation, optimization, and governance into a cohesive pipeline, making data usable immediately upon arrival. Data is written once to Apache Iceberg in the customer's own cloud account and becomes queryable by a wide range of engines, including Snowflake, Amazon Athena, and Apache Spark, with built-in governance. Peter-Christian Quint of cimt noted, "The result is a step-change in efficiency that boosts performance and delivers real cost savings — and that’s exactly where Qlik sets itself apart."
The introduction of streaming and real-time capabilities marks Qlik's commitment to powering the next wave of data-driven operations. By providing a managed, open lakehouse that centralizes governance and accelerates data flow from source to analytics and AI models, Qlik is reducing architectural complexity and time-to-insight. These enhancements position Open Lakehouse as a critical component for enterprises building a responsive, AI-ready data foundation.
About Qlik
Qlik converts complex data landscapes into actionable insights, driving strategic business outcomes. Serving over 40,000 global customers, our portfolio provides advanced, enterprise-grade AI/ML, data integration, and analytics. Our AI/ML tools, both practical and scalable, lead to better decisions, faster. We excel in data integration and governance, offering comprehensive solutions that work with diverse data sources. Intuitive analytics from Qlik uncover hidden patterns, empowering teams to address complex challenges and seize new opportunities. As strategic partners, our platform-agnostic technology and expertise make our customers more competitive.
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