Update Delta Lake Tables with PySpark | Delta Lake Tutorial Part 3 (DeltaTable API)
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Delta Lake Tutorial with PySpark — Updating Delta Tables using DeltaTable API
In this third part of the Delta Lake tutorial series, learn how to update records in a Delta table using PySpark’s DeltaTable API. We demonstrate how to perform conditional updates while preserving data integrity, using the power of ACID transactions built into the Delta Lake format.
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🔍 What You’ll Learn:
1. ⚙️ How to set up a Spark session with Delta Lake support
2. 📦 Load and interact with an existing Delta table
3. 🔁 Perform conditional UPDATE operations using the DeltaTable API
4. 🔎 Validate updates using PySpark
5. 🧠 Understand the role of ACID compliance and schema enforcement in Delta Lake
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🎯 Best For:
• Data Engineers working with big data pipelines
• Beginners learning Delta Lake with PySpark
• Anyone implementing Lakehouse architecture
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📁 Technologies Used:
• PySpark
• Delta Lake
• DeltaTable API
• Jupyter Notebook
Timestamps:
00:00 Intro
00:21 Read data using Delta Table API
00:46 toDF() method to represent delta table as dataframe
00:55 Update Delta Table - 1
01:44 Updating Delta table using SQL
02:13 Updating multiple columns at once -2
03:01 Updating based on pattern matching-3