Getting Started with Delta Lake and Delta Tables in PySpark

Introduction

Managing large amounts of data can get complicated fast. Data might arrive late, get overwritten, or even become corrupted. If you’re working with big data using Spark, you might wish your data lake could act more like a traditional database—with reliability, simple updates, and easy rollback. This is where Delta Lake comes in.

Delta Lake is an open-source storage layer that brings reliability, performance, and powerful features to your data lakes, making your life much easier. In this post, we’ll explore what Delta Lake and Delta Tables are, why they matter, their key features, how they are structured behind the scenes, and in subsequent posts we will also learn about how to use them in PySpark with simple examples.

What is Delta Lake?

Think of Delta Lake as an upgrade for your data lake. While you might be storing files in formats like Parquet, Delta Lake adds an extra layer on top of it. This layer introduces features typically found in databases, such as transaction support and data versioning, but keeps all the flexibility and scalability of a data lake.

With Delta Lake, your data is safer, easier to manage, and much faster to query. It’s like giving your data lake superpowers.

Key Features of Delta Lake

• ACID Transactions: Delta Lake ensures that your data operations are Atomic, Consistent, Isolated, and Durable (ACID). This means your data remains reliable, even if there are system failures or multiple users working at once.
• Scalable Metadata Handling: No matter how much your data grows, Delta Lake can manage information about your files quickly. Your queries remain speedy, even with billions of files.
• Schema Enforcement and Evolution:
  • Schema enforcement: Keeps bad data out by making sure everything written matches your table’s structure.
  • Schema evolution: Lets you change the table’s structure (like adding columns) without causing problems.
• Time Travel: Every time you change your data, Delta Lake keeps track. You can "go back in time" and view or restore previous versions of your data, which is great for debugging or audits.
• Unified Batch and Streaming: You can use Delta Tables for both streaming and batch jobs, removing the need to manage two separate systems.
• Upserts and Deletes (MERGE): Delta Lake allows you to update, insert, or delete data easily using commands similar to SQL databases. This is hard to do in traditional data lakes.
• Data Reliability and Quality: By recording every change and enforcing data quality rules, Delta Lake helps ensure your analytics are always based on trustworthy data.
• Performance Optimization: With features like data skipping (ignoring irrelevant data) and file compaction (keeping files tidy), your queries run much faster and more efficiently.

What are Delta Tables?

A Delta Table is simply a table stored in the Delta Lake format. It looks like a regular Spark table, but it has extra powers like transaction support and data versioning.

Delta Tables live in directories with special Parquet files and Delta Lake metadata. This metadata keeps track of all changes, so you can always see how your data looked in the past.

Elements Inside a Delta Table(The Files and Folders):

When you create a Delta Table, it’s stored as a folder (directory) in your storage system. This folder contains different files and subfolders, each with a specific role in making Delta Lake powerful and reliable.

1. Parquet Files

• Purpose: Store the actual data of your table in a columnar format.
• How they look: Filenames are long and random, ending with .parquet (e.g., part-00000-xxxx.snappy.parquet).

2. The _delta_log Directory

This folder is the "brain" of the Delta Table. It contains the log of every change made to the table.

Inside _delta_log:

• JSON Transaction Log Files:
  • Example: 00000000000000000000.json
  • Purpose: Each file records all actions (like adds/removes, schema changes) that happened in a transaction. Every write, update, or delete in the table adds a new log file.
• Checkpoint Parquet Files:
  • Example: 00000000000000000010.checkpoint.parquet
  • Purpose: Summarize the table’s state at a point in time for faster recovery. Instead of reading all the JSON logs from the start, Spark can load the latest checkpoint and just the most recent JSON files.
• CRC Files (Optional):
  • Example: 00000000000000000010.checkpoint.parquet.crc
  • Purpose: Store checksum values to detect corruption in checkpoint files.

3. Change Data Capture (CDC) Files (Optional)

If you enable CDC, Delta Lake stores special files to track every row-level change between table versions.

• How they look: Located in a subfolder like _change_data/version=.../*.parquet
• Details: Each CDC file contains all the inserts, updates, or deletes that happened in that version.

Sample Structure of a Delta Table Directory

Conclusion

Delta Lake brings the best of both worlds—combining the flexibility of a data lake with the reliability and features of a data warehouse. By using Delta Tables in PySpark, you make your data workflows simpler, faster, and safer.

And remember: a Delta Table isn’t just a bunch of files—it’s a well-organized structure that tracks every change, enables time travel, and guarantees data quality. Whether you’re just starting with big data or looking to upgrade your existing pipelines, Delta Lake is a technology worth exploring.

Have Doubts or Questions? Drop it in comment below!