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

## Working with the batch project

Let’s dive straight into describing the code and start by analyzing the first feature branch we are going to be working on, which contains the skeleton of the `SparkBatchApp` application.

The widget below contains the codebase, but if we follow the versioning strategy described in the previous lesson, we **branch off** the Main or Development branch by executing a git command with the following structure:

```
git clone --branch feature/app_skeleton [REPOSITORY_URL]
```

> **Note:** The `clone` git command is used here because we are checking the code out for the first time. After this, if we worked in different branches, we simply need to use the `checkout` git command, provided the branches are created in the repository. Examples of this are shown in other lessons of this chapter.




# Working with the batch project

Let’s dive straight into describing the code and start by analyzing the first feature branch we are going to be working on, which contains the skeleton of the `SparkBatchApp` application.

The widget below contains the codebase, but if we follow the versioning strategy described in the previous lesson, we **branch off** the Main or Development branch by executing a git command with the following structure:

```
git clone --branch feature/app_skeleton [REPOSITORY_URL]
```

> **Note:** The `clone` git command is used here because we are checking the code out for the first time. After this, if we worked in different branches, we simply need to use the `checkout` git command, provided the branches are created in the repository. Examples of this are shown in other lessons of this chapter.




Let’s go through the driver's code and highlight the code flow by describing and running the skeleton of the application.

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

Driver Program and Job Implementation: Part I

Working with the batch project