SP821: ETL Part 2: Transformations and Loads (AWS Databricks)
In this course data engineers apply data transformation and writing best practices such as user-defined functions, join optimizations, and parallel database writes. By the end of this course, you will transform complex data with custom functions, load it into a target database, and navigate Databricks and Spark documents to source solutions.
NOTE: This course is specific to the Databricks Unified Analytics Platform (based on Apache Spark™). While you might find it helpful for learning how to use Apache Spark in other environments, it does not teach you how to use Apache Spark in those environments.
3-6 hours, 75% hands-on
The course is a series of seven self-paced lessons available in both Scala and Python. A final capstone project involves writing custom, generalizable transformation logic to population data warehouse summary tables and efficiently writing the tables to a database. Each lesson includes hands-on exercises.
This version of the course is intended to be run on AWS Databricks.
Note: Access to a Databricks workspace is not part of your course purchase price. You are responsible for getting access to Databricks. See the FAQ for instructions on how to get access to an Databricks workspace.
During this course learners
- Apply built-in functions to manipulate data
- Write UDFs with a single DataFrame column inputs
- Apply UDFs with a multiple DataFrame column inputs and that return complex types
- Employ table join best practices relavant to big data environments
- Repartition DataFrames to optimize table inserts
- Write to managed and unmanaged tables
- Course Overview and Setup
- Common Transformations
- User Defined Functions
- Advanced UDFs
- Joins and Lookup Tables
- Database Writes
- Table Management
- Capstone Project: Custom Transformations, Aggregating and Loading
- Primary Audience: Data Engineers
- ETL Part 1 strongly encouraged
- Please be sure to use a supported browser.
This self-paced training course may be used by 1 user for 12 months from the date of purchase. It may not be transferred or shared with any other user.