Class DB102: 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
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
Location
Online – Virtual
Price
$2000 USD