This course serves as a comprehensive introduction to the Spark Java API. Experienced Java developers will use object-oriented programming (OOP) principles to apply theory to Apache Spark and big data practice.
You’ll learn the basic components and architecture of Spark, a leading framework for building big data applications, before implementing them in Java. You’ll also explore data transformations like grouping, sorting, and joining. Further, you’ll learn to support SQL operations in the database and create a big data and batch template application with Java.
By the end of the course, you’ll be familiar with Apache Spark and know how to integrate big data with Java environments through the Spark Java API. You’ll wrap up by learning about monitoring and support functions for a live Spark Java environment.
Combining a leading big data framework with a leading programming language, this course will empower you to work efficiently with large volumes of data, and process at scale and speed.
This course serves as a comprehensive introduction to the Spark Java API. Experienced Java developers will use object-oriented p...Show More
WHAT YOU'LL LEARN
Learn Apache Spark fundamentals and gain an overview of its building blocks
Learn Advanced Transformations and leverage Spark’s powerful library, Spark SQL
Acquire practical experience through examples, coding, and recipes
Develop a big data batch application with foundations in both design patterns and good programming practices using Spark
Learn Apache Spark fundamentals and gain an overview of its building blocks
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Content
1.
Course Introduction
2 Lessons
Get familiar with Apache Spark, its architecture, Java API, and big data processing.
2.
Spark Introduction and Basics
5 Lessons
Get started with Apache Spark's architecture, in-memory computing, and scalable data processing.
3.
Getting Started with Spark
5 Lessons
Explore setting up and running Spark programs, configuring Maven projects, and utilizing DataFrames.
4.
DataFrame Basic Operations
9 Lessons
Break down complex ideas for effective manipulation of DataFrames and Datasets in Spark.
5.
DataFrame Advanced Operations
8 Lessons
Deepen your knowledge of advanced DataFrame operations like partitioning, joins, and UDFs in Spark.
6.
Spark SQL and Other Functionalities
8 Lessons
Follow the process of leveraging Spark's SQL, schema manipulation, file/database ingestion, and serialization.
7.
Building a Big Data Batch Application
8 Lessons
Piece together the parts of building a Spark batch application, including architecture, driver program design, ingestion, and testing.
8.
Deployment and Cluster Execution
3 Lessons
Try out executing and deploying Apache Spark applications in local and cluster modes.
9.
Monitoring and Performance Fundamentals
4 Lessons
Unpack the core of interpreting Spark logs, using SparkUI, and fundamental performance optimization techniques.
11.
Apendix
2 Lessons
Break down complex tools and techniques for local Spark development and debugging using IntelliJ.
Certificate of Completion
Showcase your accomplishment by sharing your certificate of completion.
Course Author:
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