Qlik Adds Data Engineering Capabilities
Qlik today launched agentic data engineering capabilities across Qlik Cloud to help data teams use purpose-built artificial intelligence agents and declarative workflows to find trusted data, define business meaning, evaluate quality, shape data products, and create pipelines.
Qlik's agentic data engineering capabilities bring AI assistance into more of the data engineering workflow so teams can move from intent to trusted data products faster while keeping humans in control of the decisions that matter. Those data products can then be reused across analytics, automation, and AI use cases.
"Organizations are using many AI tools, it isn't just one assistant or one model or a single data platform," said Drew Clarke, executive vice president of product and technology at Qlik, in a statement. "Our approach is to bring governed Qlik context into the tools data teams already use, so they can accelerate engineering work with agents while preserving choice, transparency, and control."
New capabilities include the following:
- Data Quality Agents: Helps users retrieve trust scores and data quality metrics, create or edit data quality rules, define service-level objectives, run calculations, and detect or report anomalies through natural language or MCP-enabled workflows.
- Data Products: Helps teams create, manage, and govern trusted data products, making curated, AI-ready datasets easier to specify, maintain, and consume across analytics and AI use cases with agentic AI.
- Catalog Glossary: Help users discover data assets, standardize terminology, and connect business definitions to governed metadata.
- Declarative Pipelines with Coding: Lets data engineers work with approved third-party coding agents and development environments to generate and modify pipelines using governed pipeline context.
- Expanded MCP-enabled data tools: Gives authorized AI clients access to Qlik capabilities and context, helping teams use their preferred AI assistants while maintaining enterprise controls.