Delta Lake Tutorial #3 — Batch and Streaming Reads & Writes with PySpark (Databricks 2026)

In this video, we cover how to read from and write to Delta tables using both batch and
streaming modes in PySpark and Databricks — including the production patterns that make
streaming reads reliable when your source table is also receiving updates and deletes.

This is video #3 in the Delta Lake Complete Guide 2026 series.

What we cover in this video:
▸ Batch reads and writes on Delta tables
▸ Streaming reads from Delta tables using Structured Streaming
▸ Streaming writes to Delta tables
▸ The ignoreChanges and ignoreDeletes options — why they matter in production
▸ Checkpointing and fault tolerance in Delta streaming jobs
▸ Running batch and streaming jobs on the same Delta table simultaneously

By the end of this video, you will be able to build both batch and streaming pipelines
on Delta Lake with confidence — including the safety patterns most tutorials skip.

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🔗 LINKS
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▸ Delta Lake Complete Guide 2026 playlist: [playlist link]
▸ Video #1 — What is Delta Lake: [video link]
▸ Video #2 — Creating and managing Delta tables: [video link]
▸ Full Databricks on AWS course (coming July 2026): [course link]
▸ More advanced courses: www.scholarnest.com

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📌 ABOUT THIS SERIES
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The Delta Lake Complete Guide 2026 is a free, structured series for data engineers
who want to go beyond the basics. Every video is recorded as part of a full
Databricks on AWS course, so the depth reflects real production engineering —
not simplified demos.

New videos every week. Subscribe and hit the bell so you don't miss any part of the series.

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👤 ABOUT THE INSTRUCTOR
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I am a data engineer and architect with 25 years of industry experience. I create
self-paced courses on Apache Spark, Databricks, Kafka, and Generative AI for
data engineers.

📺 YouTube: Learning Journal (you're already here!)
🌐 Website: www.scholarnest.com

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