Delta Lake is an open-source storage layer that sits on top of existing data lake storage, such as Apache Hadoop Distributed File System (HDFS) or Amazon S3. It allows users to perform ACID (Atomicity, Consistency, Isolation, and Durability) transactions on large-scale data lakes and adds a layer of reliability and performance optimization to data lakes.

Delta Lake provides a number of features including:

Data versioning: Allows users to roll back to previous versions of the data in case of errors or mistakes.

Schema enforcement Enforces a schema on the data in the lake, ensuring that data is consistent and of high quality.

Time travel: Allows users to query historical versions of the data.

Data auditing: Provides a full history of changes to the data lake, making it easier to understand and troubleshoot issues.

Delta Lake is supported by several big data processing and analytics frameworks such as Apache Spark, Apache Hive, and Apache Presto and can be used with popular data lakes such as AWS S3, Microsoft ADLS and IBM ADLS.

After knowing delta lake many might be having a question, “what is the difference between delta lake and and data warehouse?”
The features mentioned (ACID transactions, data versioning, and schema enforcement) are also present in a traditional Relational Database Management System (RDBMS), which is the basis for most data warehouses.
The key difference between Delta Lake and a traditional data warehouse lies in their design and architecture.
Delta Lake is designed for use with big data technologies such as Apache Spark and Apache Hive, and provides a way to manage large amounts of semi-structured or structured data stored in data lakes. Delta Lake provides the reliability features you mentioned in a distributed file system (such as Hadoop HDFS), while still allowing for fast and flexible data processing using big data technologies.

On the other hand, traditional data warehouses are typically centralized systems based on a relational database management system (RDBMS) and optimized for Online Analytical Processing (OLAP) queries. They are designed to store and manage structured data from multiple sources and provide fast query performance for complex business intelligence and reporting applications.

So in summary, while both Delta Lake and data warehouses aim to provide reliability and consistency for data storage, Delta Lake is designed for big data processing in a data lake environment, while data warehouses are designed for centralized management of structured data for reporting and analytics.