Databricks Data Engineering Mastery

$149

8-module course for data engineers: PySpark, Delta Lake, medallion architecture, pipeline orchestration, Unity Catalog, testing, CI/CD, and production operations. 40 hours of hands-on content.

📁 18 files🏷 v1.0.0

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📁 File Structure 18 files

databricks-data-engineering-mastery/ ├── README.md ├── free-preview/ │ ├── cicd-data-pipelines-free.md │ ├── delta-lake-deep-dive-free.md │ ├── medallion-architecture-free.md │ ├── pipeline-orchestration-free.md │ ├── production-operations-free.md │ ├── pyspark-foundations-free.md │ ├── testing-data-quality-free.md │ └── unity-catalog-governance-free.md └── modules/ ├── cicd-data-pipelines.md ├── delta-lake-deep-dive.md ├── medallion-architecture.md ├── pipeline-orchestration.md ├── production-operations.md ├── pyspark-foundations.md ├── testing-data-quality.md └── unity-catalog-governance.md

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Databricks Data Engineering Mastery

Thank you for purchasing this course from Datanest Academy!

Getting Started

1. Review course.yaml for the complete course outline, learning objectives, and prerequisites

2. Start with modules/module-01-*.md and progress through each module in order

3. Each module includes hands-on exercises, code examples, and production patterns

Course Structure

course.yaml — Complete course metadata, outline, and prerequisites
modules/ — 8 progressive modules (full premium content)
free-preview/ — Redacted preview versions (for sharing with colleagues)
assets/ — Supporting materials

Module Format

Each module contains:

Learning objectives
Concept explanations with real-world context
Production-ready code examples
Hands-on exercises
Key takeaways and review questions

Support

Questions? Email support@datanest.dev

License

This course content is licensed for personal and organizational use only.

Redistribution is not permitted.

📄 Content Sample guide/01-pyspark-foundations.md

Chapter 01: PySpark Foundations for Data Engineers

Duration: 6 hours | Difficulty: Intermediate | Prerequisites: Python fundamentals, basic SQL

Learning Objectives

By the end of this module, you will be able to:

1. Configure a SparkSession for local development and Databricks workloads

2. Perform complex transformations using the DataFrame API fluently

3. Write efficient column expressions with pyspark.sql.functions

4. Explain why UDFs are costly and when built-in functions are the right choice

5. Read and interpret Spark execution plans to diagnose performance issues

6. Apply partitioning strategies to control parallelism and data layout

1. SparkSession: Your Entry Point to Spark

Every PySpark program begins with a SparkSession. On Databricks, one is pre-configured as spark, but understanding its internals is essential for testing, tuning, and local development.

1.1 Creating a SparkSession


from pyspark.sql import SparkSession

spark = (
    SparkSession.builder
    .appName("DataEngineeringMastery")
    .master("local[*]")  # Use all available cores locally
    .config("spark.sql.shuffle.partitions", "8")
    .config("spark.sql.adaptive.enabled", "true")
    .config("spark.serializer", "org.apache.spark.serializer.KryoSerializer")
    .getOrCreate()
)

1.2 Key Configuration Properties

Property	Default	Recommendation
`spark.sql.shuffle.partitions`	200	Set to 2-3x your cluster cores for small-medium data
`spark.sql.adaptive.enabled`	true (Spark 3.2+)	Always keep enabled
`spark.sql.adaptive.coalescePartitions.enabled`	true	Reduces small partitions after shuffle
`spark.sql.files.maxPartitionBytes`	128 MB	Increase for wide tables, decrease for narrow
`spark.sql.autoBroadcastJoinThreshold`	10 MB	Increase cautiously for dimension tables

... and much more in the full download.

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