Set up data pipelines to extract data from sources such as databases, APIs, logs, and more. Ensure data quality and integrity during the ingestion process.

Integrate data from various systems to create a cohesive dataset. Implement data federation and virtualization techniques.

Use tools like Data Factory, Azure Synapse or Fabric to schedule and monitor data pipeline executions. Ensure the reliability and fault tolerance of data pipelines.

Implement ETL (Extract, Transform, Load) processes to clean, aggregate, and reshape data. Use distributed computing frameworks like Apache Spark for large-scale data processing.

Design and implement data storage solutions, such as databases, data lakes, and warehouses. Optimize storage structures for performance and scalability.