Databricks Platform Features - Deep Dive into Delta Lake using PySpark | Data Engineering
Delta Lake is a key feature of the Databricks Platform, extensively used to build Enterprise Data Lakehouses on cloud platforms like AWS, Azure, and GCP. In this video, you’ll learn how to use Delta Lake with Spark DataFrames and Python to perform operations like data updates, deletes, merges, and snapshot recovery.
What You’ll Learn:
• Creating DataFrames for Delta Lake
• Writing and reading data in Delta format
• Performing updates, deletes, and merges
• Implementing point-in-time recovery with Delta logs
• Cleaning up Delta files using Vacuum and compaction
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0:00:00 — Introduction to Delta Lake using Data Frames on Databricks Platform
0:02:59 — Creating Data Frames for Delta Lake on Databricks Platform
0:09:16 — Writing Data Frame using Delta Format on Databricks Platform
0:13:56 — Updating Existing Data using Delta Format on Databricks Platform
0:19:55 — Delete Existing Data using Delta Format on Databricks Platform
0:24:45 — Merge or Upsert Data using Delta Format on Databricks Platform
0:34:34 — Deleting using Merge in Delta Lake on Databricks Platform
0:42:26 — Point in Snapshot Recovery using Delta Logs on Databricks Platform
0:50:46 — Deleting unnecessary Delta Files using Vacuum on Databricks Platform
1:00:09 — Compaction of Delta Lake Files on Databricks Platform
Helpful Resources:
Are you interested in learning Delta Lake using Spark SQL? Watch this video:
👉 • Databricks Platform Features - Deep ...
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