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.

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

## Spark in practice

We are at a point in the course where we can apply some of the concepts learned previously and work with a project closer to a real-world solution. This whole chapter of the course explains how to build a Spark-based, big data batch application. In this lesson, we describe its generic architecture, including the collaborating services that it interacts with.

Next, we delve into the application’s design and explain it in detail.

Once we are familiar with the design and architecture, we move to the code implementation; this last part also produces a template for a big data batch application that we can re-use and adapt to different business needs.

Let’s begin with the architecture.

## Architecture

A possible generic architectural landscape for the batch application is illustrated in the following diagram:



# Spark in practice

We are at a point in the course where we can apply some of the concepts learned previously and work with a project closer to a real-world solution. This whole chapter of the course explains how to build a Spark-based, big data batch application. In this lesson, we describe its generic architecture, including the collaborating services that it interacts with.

Next, we delve into the application’s design and explain it in detail.

Once we are familiar with the design and architecture, we move to the code implementation; this last part also produces a template for a big data batch application that we can re-use and adapt to different business needs.

Let’s begin with the architecture.

# Architecture

A possible generic architectural landscape for the batch application is illustrated in the following diagram:



Get introduced to the general architecture and design guidelines for the batch application we build in this chapter.


The Application Architecture Ecosystem

Get introduced to the general architecture and design guidelines for the batch application we build in this chapter.

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

The Application Architecture Ecosystem

Spark in practice

Architecture