A Delta Lake is an open-source storage layer that enhances data lakes by adding reliability and performance improvements. It integrates with Apache Spark and provides ACID (Atomicity, Consistency, Isolation, Durability) transactions, which ensure data integrity and consistency[1][2].

Delta Lake addresses common challenges in data lakes, such as data inconsistency, schema evolution, and performance issues. It supports schema enforcement and evolution, allowing for gradual updates without breaking existing data pipelines[2]. Additionally, Delta Lake offers time travel capabilities, enabling users to query previous versions of data, which is useful for auditing and historical analysis[2].

The architecture of Delta Lake typically follows a medallion approach, organizing data into three layers: Bronze (raw data), Silver (cleaned and transformed data), and Gold (aggregated business-level data)[2]. This structure helps manage data efficiently and supports both batch and streaming data processing.

Overall, Delta Lake enhances the functionality of data lakes, making them more robust and suitable for large-scale data analytics and machine learning workloads[1][2].