New Delhi/San Francisco, June 11, 2025 — In a move set to boost enterprise data agility and AI readiness, Qlik® has announced an expanded set of capabilities for the Databricks Data Intelligence Platform, designed to help organizations build trusted data pipelines and optimize performance across Delta and Iceberg formats. The new features reinforce Qlik’s mission to simplify data management and analytics across complex, hybrid data landscapes.
These innovations—ranging from real-time data ingestion via Change Data Capture (CDC) to automated Apache Iceberg™ optimization—are expected to significantly enhance interoperability and performance for AI workloads, particularly within Databricks Mosaic AI environments.
“Databricks customers continue to push the boundaries of what’s possible with open data formats and AI,” said Ariel Amster, Director, Strategic Technology Partners at Databricks. “By delivering real-time CDC into UniForm tables and its native integration with Mosaic AI, Qlik is helping our joint customers simplify and accelerate innovation on the Databricks platform.”
Key Highlights of the Qlik-Databricks Integration:
Real-Time Streaming via CDC: Qlik Replicate® now streams continuous change data from enterprise sources into Unity Catalog’s UniForm (Iceberg) tables. This enables low-latency, high-throughput data ingestion that meets strict business SLAs across both Delta and Iceberg formats.
Adaptive Iceberg Optimization: Through Qlik Open Lakehouse and Qlik Talend Cloud®, newly ingested Iceberg tables are automatically optimized via intelligent compactions and partition pruning, improving query speeds and reducing storage overhead. These tables can be queried natively via Databricks Photon or any Iceberg-compatible engine.
AI-Ready, Trusted Data Products: Qlik now enables governed data product creation with embedded quality checks for assets like Delta Live Tables, ensuring enterprises can trust the data that powers AI, analytics, and operational workflows.
Developer-Centric Enhancements: Upcoming roadmap updates include schema inference, Databricks notebook import, and native Spark debugging, streamlining self-service data pipeline development and improving integration into Databricks-native environments.
“From ingestion to insight, Databricks customers are demanding more speed, flexibility, and trust across their data estate,” said David Zember, SVP of Worldwide Channels and Alliances at Qlik. “These new capabilities allow teams to do more with their Databricks investment—especially around governance, interoperability, and AI readiness.”
The Bigger Picture
As enterprises accelerate their shift toward open lakehouse architectures and AI-driven insights, seamless interoperability between data platforms is critical. With this expansion, Qlik is not just enabling high-performance streaming and governance—it is also reinforcing a foundation where AI models can be trained on high-quality, trusted data from day one.
The strengthened partnership between Qlik and Databricks is likely to resonate with enterprises across sectors—from financial services and manufacturing to healthcare and telecom—who are looking to modernize data pipelines without compromising on control, performance, or scalability.
