In this course, you will explore the fundamentals of Apache Spark and Delta Lake on Databricks. You will learn the architectural components of Spark, the DataFrame and Structured Streaming APIs, and how Delta Lake can improve your data pipelines. Lastly, you will execute streaming queries to process streaming data and understand the advantages of using Delta Lake.
This course will prepare you to take the Databricks Certified Associate Developer for Apache Spark exam.
2 full days or 4 half days
Define Spark’s architectural components
Describe how DataFrames are transformed, executed, and optimized in Spark
Apply the DataFrame API to explore, preprocess, join, and ingest data in Spark
Apply the Structured Streaming API to perform analysis on streaming data
Use Delta Lake to improve the quality and performance of data pipelines
Completion of Introduction to Python for Data Science & Data Engineering, OR familiarity with Python and basic programming concepts, including data types, lists, dictionaries, variables, functions, loops, conditional statements, exception handling, accessing classes, and using third-party libraries
Basic knowledge of SQL, including writing queries using
SELECT, WHERE, GROUP BY, ORDER BY, LIMIT, and JOIN
Databricks platform overview
DataFrame reader, writer, transformation, and aggregation
Upcoming Public Classes