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.
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
ETL Part 1 self-paced course.
Course Overview and Setup
User Defined Functions
Joins and Lookup Tables
Capstone Project: Custom Transformations, Aggregating and Loading
Supported platforms include Azure Databricks, Databricks Community Edition, and non-Azure Databricks.
If you're planning to use the course on Azure Databricks, select the "Azure Databricks" Platform option.
If you're planning to use the course on Databricks Community Edition or on a non-Azure version of Databricks, select the "Other Databricks" Platform option.
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.
https://www.exitcertified.com/it-training/databricks/etl-transformations-and-loads-56303-detail.htmlETL2-TRAN-SELFETL Part 2 - Transformations and Loadshttps://assets.exitcertified.com/assets/CourseImages/40d1fd1cd4/AdobeStock_224267764__FitMaxWzEwMDAsMTAwMF0.jpg75.00USDInStock/Training/DatabricksIn this course data engineers apply data transformation and writing best practices such as user-defined functions, join...75.00DatabricksSelf Paced2019-03-21T09:21:01+00:00USD