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Organizations repeatedly face the same data integration challenge: many data sources, inconsistent schemas, and extensive manual effort to transform data into analytics-ready formats. This session explores an alternative approach using open-source tooling and AI to reduce that effort significantly.
In this hands-on session, you will build intelligent data pipelines that automatically transform inconsistent data uploads into unified, analytics-ready tables. The session demonstrates how Apache NiFi can be used for orchestration, how LLM-powered schema inference can automate schema mapping and validation, and how Apache Iceberg’s schema evolution capabilities allow pipelines to adapt to changing data structures while preserving backward compatibility. The focus is on practical implementation and real-world architecture patterns suitable for enterprise-scale environments.
What You will Learn
How to build self-adapting data pipelines that infer semantic meaning across different schemas
Practical techniques for integrating LLMs into data pipelines for automated schema mapping and validation
How Apache Iceberg schema evolution supports compatibility while adapting to new data structures
Who Should Attend
Data Engineers
Platform Engineers
Analytics Engineers
Software Architects working on data platforms
