Integrating artificial intelligence into data pipelines enhances enterprise data management, streamlining workflows and reducing manual coding. An evolved metadata-driven ETL framework incorporates Azure Databricks and a feedback loop for continuous analytics. Organizations face challenges such as processing large datasets, delivering real-time analytics, and maintaining scalability. The original framework utilized a metadata schema for dynamic ETL job configuration but required enhancements to orchestrate machine learning tasks alongside data integration. The new architecture offers robust capabilities to meet modern data demands.
The integration of artificial intelligence into data pipelines has become a game-changer, enabling enterprises to streamline data workflows and minimize manual coding.
The demands on data architectures have grown more complex due to the reliance on AI and machine learning to unlock insights from vast datasets.
Today's enterprises face pressure to process large datasets, deliver real-time analytics, and adapt to shifting business needs while maintaining scalability and governance.
The evolved framework merges Azure Databricks with a metadata-driven approach to MLOps, transforming data architecture into a robust system that meets modern demands.
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