Databricks Data Engineer Associate - Complete Guide

Databricks Data Engineer Associate - Complete Step-by-Step Guide

Step 1: Databricks Fundamentals

Theory

• Lakehouse = Data Lake + Data Warehouse
• Built on Apache Spark
• Uses Delta Lake for reliability

Core Components

• Workspace
• Cluster
• Notebook
• Jobs

Practical

spark.range(10).show()

Key Concept: Driver = brain, Workers = execution

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Step 2: Apache Spark

Theory

• DataFrames are distributed tables
• Lazy evaluation (execution happens only on action)

Transformations vs Actions

Type	Example
Transformation	filter, select
Action	show, count

Practical

Read Data

df = spark.read.format("csv").option("header", True).load("/FileStore/data.csv")

Transform

df2 = df.filter(df.age > 30).select("name", "age")

Aggregate

df3 = df.groupBy("city").count()

Join

df.join(df2, "id", "inner")

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Step 3: Delta Lake (Critical)

Theory

• Provides ACID transactions
• Supports updates, deletes, and merges
• Supports time travel

Practical

Create Table

df.write.format("delta").save("/delta/table1")

Read Table

df = spark.read.format("delta").load("/delta/table1")

Update

UPDATE table1 SET age = 40 WHERE id = 1;

Delete

DELETE FROM table1 WHERE id = 2;

Merge (Important)

MERGE INTO target t USING source s ON t.id = s.id WHEN MATCHED THEN UPDATE SET * WHEN NOT MATCHED THEN INSERT *;

Time Travel

SELECT * FROM table1 VERSION AS OF 2;

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Step 4: Data Ingestion

Theory

• Batch = one-time processing
• Streaming = continuous processing

Practical

Batch

df = spark.read.json("/data/input") df.write.format("delta").save("/data/output")

Streaming

df = spark.readStream.format("json").load("/input") df.writeStream .format("delta") .option("checkpointLocation", "/chk") .start("/output")

Important: Checkpointing prevents data loss

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Step 5: ETL Pipeline (Medallion Architecture)

Layer	Purpose
Bronze	Raw data
Silver	Cleaned data
Gold	Aggregated data

Practical

Bronze

df.write.format("delta").save("/bronze/data")

Silver

df_clean = df.filter("age IS NOT NULL") df_clean.write.format("delta").save("/silver/data")

Gold

df.groupBy("city").count().write.format("delta").save("/gold/data")

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Step 6: Databricks SQL

Practical

Create Table

CREATE TABLE users USING DELTA LOCATION '/delta/users';

Query

SELECT city, COUNT(*) FROM users GROUP BY city;

Temp View

CREATE TEMP VIEW temp_users AS SELECT * FROM users;

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Step 7: Jobs & Automation

• Create jobs from notebooks
• Schedule using cron
• Supports task dependencies

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Step 8: Performance Optimization

Practical

Caching

df.cache()

Partitioning

df.write.partitionBy("city").format("delta").save("/data")

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Step 9: Security

• Unity Catalog for governance
• Table-level permissions

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Final Project (Recommended)

• Ingest JSON → Bronze
• Clean → Silver
• Aggregate → Gold
• Query using SQL

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Final Checklist

• Spark transformations
• Delta MERGE, UPDATE, DELETE
• Streaming basics
• ETL pipelines
• Jobs & scheduling

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Pro Tip

If you already have experience with AWS, EMR, or streaming systems, focus mainly on:

• Delta Lake
• Databricks UI