ETL Pipelines with Databricks and Apache Spark focus on building scalable data workflows for extracting, transforming, and loading large datasets. Apache Spark is a distributed computing engine that processes data in parallel across clusters. Databricks provides a managed platform for running Spark-based pipelines efficiently. This training explains how data is ingested from multiple sources and transformed using Spark operations. It then shows how data is loaded into data warehouses or storage systems. It also covers core concepts such as Spark DataFrames, RDDs, cluster management, and job scheduling. You will learn how to design and optimize ETL pipelines for performance, scalability, and reliability in big data environments. The course also highlights real-world use cases in analytics, data engineering, and cloud data platforms.
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