This podcast explores how to build scalable graph data pipelines by integrating Apache Spark with Neo4j for large-scale data processing and analytics. It discusses data ingestion, transformation, distributed computation, and best practices for moving data efficiently into graph structures. Perfect for data engineers, big data architects, software developers, and professionals working with large-scale connected data systems.