Introduction
Performance optimization in Azure Databricks uses Apache Spark and Delta Live Tables (DLT) to handle large-scale data processing efficiently. Spark's in-memory computing capabilities, combined with strategies like caching frequently accessed data, optimizing shuffle operations, and using optimized data formats like Parquet, enhance performance. Delta Live Tables further improve efficiency by automating the creation of real-time data pipelines with built-in quality and reliability features. You learn about Using Delta Lake. DLT ensures data consistency with ACID transactions and schema enforcement. Effective partitioning, Z-Ordering, and auto-optimization features in DLT contribute to reducing latency and improving the overall performance of data workflows in Azure Databricks.