tarball.tar.gz

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

## Cluster's components and interactions

The previous [lesson](https://www.educative.io/courses/mastering-big-data-apache-spark-java-api/local-and-cluster-based-execution) introduced a diagram for the most relevant logical and physical parts in Spark cluster mode, and it provided a view of the different interactions between them.

However, to expand on how these interact when a Spark application runs in a cluster, it’s helpful to take a look at a somewhat more dynamic picture, which the below image (similar to the previous diagram) presents, and in which we can highlight the main parts and interactions:



# Cluster's components and interactions

The previous [lesson](https://www.educative.io/courses/mastering-big-data-apache-spark-java-api/local-and-cluster-based-execution) introduced a diagram for the most relevant logical and physical parts in Spark cluster mode, and it provided a view of the different interactions between them.

However, to expand on how these interact when a Spark application runs in a cluster, it’s helpful to take a look at a somewhat more dynamic picture, which the below image (similar to the previous diagram) presents, and in which we can highlight the main parts and interactions:



Learn how to build, deploy, and run a Spark application to execute our application on a cluster.

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

Deploying and Running a Spark Application

Cluster’s components and interactions