Qlik has introduced new capabilities to its Qlik Open Lakehouse platform, enhancing its ability to handle streaming data with real-time ingestion, transformations, data quality, and expanded integrations. The updates enable organizations to ingest high-volume streaming events from sources like Apache Kafka, Amazon Kinesis, and Amazon S3 directly into governed Apache Iceberg tables within their own cloud environments. Transformations such as cleansing, filtering, normalization, and flattening now occur on-the-fly as data arrives, ensuring immediate usability. These advancements are designed to eliminate reliance on data warehouse compute for ingestion and transformation tasks. Instead, processing is offloaded to cost-effective, scalable compute resources. Automatic optimization for Iceberg table compaction and metadata maintenance ensures sustained performance at scale. Comprehensive governance features—including data lineage, cataloging, and Qlik Trust Score—are applied automatically to both original and mirrored datasets. The platform now supports integration with Snowflake Open Catalog, expands compatibility with Apache Spark, and introduces zero-copy mirroring to Databricks and Amazon Redshift—allowing teams to access the same governed Iceberg data across multiple engines without duplicating storage. This simplifies hybrid lakehouse and warehouse architectures while maintaining data consistency and reducing costs. Drew Clarke, EVP of Product and Technology at Qlik, emphasized the shift toward operational AI, stating that real-time, governed data is essential for AI to move beyond pilots and into production. With these updates, teams gain immediate access to trusted, up-to-date data across analytics, applications, and machine learning workflows. Peter-Christian Quint, Managing Partner at cimt, highlighted that the true value of an open lakehouse emerges when simplicity and governance align. He noted that Qlik enables organizations to write data once into governed Iceberg tables and distribute it seamlessly across engines like Snowflake, Amazon Athena, Amazon SageMaker Studio, Apache Spark, Trino, and Presto—delivering near real-time insights with built-in quality and trust. The new streaming ingestion and transformation features are scheduled for general availability in Q1 2026 for Qlik Talend Cloud customers. Support for Snowflake Open Catalog, enhanced Spark compatibility, and zero-copy mirroring to Databricks and Amazon Redshift will roll out in phases starting in Q1 2026, with regional availability to be announced. Customers and prospects can experience the new capabilities at AWS re:Invent in Qlik’s booth #1727 or request a personalized demo. For more information, contact a Qlik representative or start a free trial.