Understanding the Basics of Delta Lake Architecture

3 min readApr 8


Source: Databricks

Delta Lake is a powerful open-source data storage layer that brings a new level of reliability and performance to big data processing. It is designed to work with cloud data lakes and provides support for batch and streaming data workloads, as well as schema enforcement and versioning, among other features. To read about Introduction to Delta Lake, check my previous article: Revolutionize Your Big Data Management with Delta Table

Understanding the basics of Delta Lake architecture is key to unlocking its full potential. In this article, we will take a closer look at the components that make up Delta Lake and how they work together to deliver high-quality data processing capabilities.

Delta Lake Components

The Delta Lake architecture is made up of three main components: the Delta table, the Delta log, and the storage layer. Let’s explore each component in more detail.

Delta Table
At the core of Delta Lake is the Delta table, which is a transactional table that is optimized for large-scale data processing. It stores data in a columnar format that enables efficient querying and processing of large datasets. The Delta table also supports schema enforcement and evolution, which ensures that data is consistent across different versions of the table.

Delta Log
The Delta log is a transaction log that records every operation performed on the Delta table. It acts as a source of truth for the Delta table, and is responsible for maintaining the consistency and durability of the data stored in the table. The Delta log also provides support for versioning and rollbacks, which allows users to restore previous versions of the Delta table if needed.

Storage Layer
The storage layer is where the actual data is stored in Delta Lake. It can be any type of storage layer that supports object storage, such as Amazon S3 or Azure Data Lake Storage. The storage layer provides durability and scalability for the data stored in Delta Lake, and allows users to store and process large datasets without having to worry about managing the underlying infrastructure.

Delta Lake Architecture in Action

Now that we have a basic understanding of the Delta Lake architecture, let’s take a look at how it works in action. Here is a simple example of how data is processed using Delta Lake:

  1. Data is ingested into the Delta table, where it is stored in a columnar format for efficient processing.
  2. Any changes made to the Delta table are recorded in the Delta log, which ensures the consistency and durability of the data.
  3. Queries and transformations are performed on the Delta table using SQL or other programming languages.
  4. The processed data is written back to the Delta table, which updates the Delta log and ensures that the data remains consistent across different versions of the table.

Benefits of Delta Lake Architecture

There are several benefits to using Delta Lake architecture for big data processing. First and foremost, it provides reliability and consistency for large-scale data processing workloads. It also supports schema enforcement and evolution, which helps to ensure that data is accurate and consistent across different versions of the table. Additionally, Delta Lake is designed to work with cloud data lakes, which provides scalability and cost-effectiveness for big data processing.

In conclusion, understanding the basics of Delta Lake architecture is crucial for anyone looking to take advantage of this powerful data storage layer. By leveraging the Delta table, Delta log, and storage layer, users can process large datasets with confidence and accuracy, and take advantage of the many benefits that Delta Lake has to offer.

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Data Engineer, Big Data Enthusiast and Automation using Python