Data Ingestion and Transformation with Apache Spark focus on collecting, processing, and converting large datasets for analytics and data engineering workflows. Apache Spark provides distributed computing capabilities that enable fast and scalable data processing across clusters. This training explains how to ingest data from sources such as databases, files, APIs, and streaming platforms into Spark environments. It also covers transformation techniques like filtering, aggregation, joins, and schema handling using Spark DataFrames and Spark SQL. You will learn how to design scalable ETL pipelines that process structured and unstructured data efficiently. The course also highlights best practices for optimizing performance, fault tolerance, and data workflow management in big data systems.
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