Monitoring and Optimizing Apache Spark Workloads on Databricks

This course explores the Lakehouse architecture and Medallion design for scalable data workflows, focusing on Unity Catalog for secure data governance, access control, and lineage tracking. The curriculum includes building reliable, ACID-compliant pipelines with Delta Lake. You'll examine Spark optimization techniques, such as partitioning, caching, and query tuning, and learn performance monitoring, troubleshooting, and best practices for efficient data engineering and analytics to address real-world challenges.

Languages Available: English | 日本語 | 한국어

Skill Level

Associate

Duration

Prerequisites

- Basic programming knowledge

- Familiarity with Python

- Basic understanding of SQL queries (SELECT, JOIN, GROUP BY)

- Familiarity with data processing concepts

- No prior Spark or Databricks experience required

Outline

Monitoring and Optimizing Apache Spark Workloads on Databricks

Apache Spark and Databricks
Using Apache Spark with Delta Lake
Demo: Introduction to Delta Lake
Lab: Introduction to Delta Lake
Optimizing Apache Spark
Demo: Optimizing Apache Spark
Lab: Optimizing Apache Spark

Upcoming Public Classes

Date	Time	Your Local Time	Language	Price
Date	Time	Your Local Time	Language	Price	Jun 25	01 PM - 05 PM (Europe/London)	-	English	$750.00
Jul 22	01 PM - 05 PM (Australia/Sydney)	-	English	$750.00
Jul 22	09 AM - 01 PM (America/New_York)	-	English	$750.00

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Upcoming Public Classes

Date	Time	Your Local Time	Language	Price
Date	Time	Your Local Time	Language	Price	Jun 25	01 PM - 05 PM (Europe/London)	-	English	$750.00
Jul 22	01 PM - 05 PM (Australia/Sydney)	-	English	$750.00
Jul 22	09 AM - 01 PM (America/New_York)	-	English	$750.00

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