Delta Lake is built on top of a distributed file system, such as HDFS or cloud-based data lakes like Amazon S3. The architecture of Delta Lake consists of several key components:

1. Delta Lake Table: A Delta Lake table is a collection of data stored in a distributed file system, such as HDFS or S3. The data is organized into partitions, which can be processed in parallel to achieve scalability.
2. Transactions Log: Delta Lake maintains a transaction log that records all changes to the data in the table. This log provides a complete history of the data and allows Delta Lake to recover from failures and support ACID transactions.
3. Metadata Store: Delta Lake uses a metadata store, such as Apache Hive Metastore, to store information about the structure and organization of the data in the table. The metadata store also tracks the state of each partition, including the number of records, column data types, and other statistics.
4. Delta Engine: The Delta engine is the heart of Delta Lake and provides the underlying functionality for reading and writing data, managing transactions, and executing operations such as updates, deletes, and merges.
5. Spark Integration: Delta Lake integrates with Apache Spark and provides a Spark DataFrame API that allows developers to read and write data using Spark SQL. The Delta engine uses Spark to execute operations in parallel, taking advantage of the distributed computing capabilities of the Spark platform.

Overall, the architecture of Delta Lake provides a unified and scalable solution for data storage, management, and processing in data lakes, making it a popular choice for large-scale big data analytics and machine learning workloads.