Explore how Apache Spark 3.0 and Delta Lake enhance data lake reliability in this 58-minute webinar from Databricks. Learn about Apache Spark's role in big data processing, the evolution of data lake architectures, and Delta Lake's capabilities for ensuring reliable data. Discover how unified batch and streaming simplifies architectures. Dive into Spark 3.0's new features, including the Adaptive Query Execution framework for improved query performance, Dynamic Partition Pruning for faster processing in star schema designs, and accelerator-aware scheduling for GPU optimization. Examine new Pandas UDF types and function APIs, as well as enhanced monitoring capabilities. Gain insights into Delta Lake 0.7.0, the Spark Catalyst Optimizer, and the Lakehouse paradigm. Understand how ACID transactions, schema enforcement, and time travel contribute to data reliability. Learn about data quality frameworks, improved SQL capabilities, and the integration of DataSourceV2 and Catalog API.