Apache Spark™ Programming with Databricks

Description

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.

Duration

2 full days or 4 half days

Objectives

• 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

Prerequisites

• 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

Outline

Day 1

• Spark overview

• Databricks platform overview

• Spark SQL

• DataFrame reader, writer, transformation, and aggregation

• Datetimes

• Complex types

Day 2

• User-defined functions (UDFs) and vectorized UDFs

• Spark internals

• Query optimization

• Partitioning

• Streaming API

• Delta Lake

Upcoming Public Classes