Gain insights into Spark Java API, learn about data transformations and SQL operations, and discover how to integrate big data and Java for scalable, high-speed processing.

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DataFrameBasicsRun

SparkDataframeExampleDB

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.

Mastering Big Data with Apache Spark and Java

**(True or False)** Local mode is the preferred option when running Spark applications in production environments or real-life situations.


(True or False) Local mode is the preferred option when running Spark applications in production environments or real-life situations.

Either a master node or a resource manager


**(Select all that apply.)** Which of the following components are part of Spark’s cluster mode? 


(Select all that apply.) Which of the following components are part of Spark’s cluster mode?

This is achieved either by including in the code the master node’s IP address and running the application with the `java -jar` command, or by submitting a jar using the `spark-submit` command specifying the option for the master node address or resource manager name.


This is achieved by using the `spark-submit` command in conjunction with the IP address of the master node or the name of a resource manager as command options.


How does the application node communicate with the master node or resource manager while running in a cluster?


How does the application node communicate with the master node or resource manager while running in a cluster?

**(True or False)** A “fat jar” or “uber jar” includes all the libraries used by a Spark application.


(True or False) A “fat jar” or “uber jar” includes all the libraries used by a Spark application.

Spark’s Deployment and Cluster Execution Quiz

Spark’s Deployment and Cluster Execution Quiz

Test your knowledge of Spark's deployment and cluster execution.

Course Introduction

Spark Introduction and Basics

Getting Started with Spark

DataFrame Basic Operations

DataFrame Advanced Operations

Spark SQL and Other Functionalities

Building a Big Data Batch Application

Deployment and Cluster Execution

Monitoring and Performance Fundamentals

Conclusion

Apendix

Quiz Yourself: Deployment and Cluster Execution