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

## Conclusion

Spark-based application development in Java has traditionally been a somewhat obscure topic, particularly around finding bibliography and materials to quickly get up to speed and learn the Spark framework.

Throughout this course we've tried to structure and provide the necessary knowledge from different sources to get a Java developer well versed on the most important parts and functionalities offered by this amazing technology, as much as any other Spark in Scala or Python book or material can offer.


>**Author’s Note:** Luckily, we live in a time when information is readily available, quickly and globally, and access to different materials has become cheaper than ever before, so should the need to dive deeper into this fascinating Spark big data world arise, I recommend checking the following websites and books.

Websites:


- [Spark Examples](https://spark.apache.org/examples.html)
- [Official Programming Guide: SparkSQL](https://spark.apache.org/docs/3.0.0/sql-programming-guide.html)
- [Official Programming Guide: RDDs](https://spark.apache.org/docs/3.0.0/rdd-programming-guide.html)
<br/>

**Books**

- [*Spark in Action Second Edition*](https://www.manning.com/books/spark-in-action-second-edition)

- [*Apache Spark 2.x for Java Developers*
](https://www.oreilly.com/library/view/apache-spark-2x/9781787126497/)

# Conclusion

Spark-based application development in Java has traditionally been a somewhat obscure topic, particularly around finding bibliography and materials to quickly get up to speed and learn the Spark framework.

Throughout this course we've tried to structure and provide the necessary knowledge from different sources to get a Java developer well versed on the most important parts and functionalities offered by this amazing technology, as much as any other Spark in Scala or Python book or material can offer.


>**Author’s Note:** Luckily, we live in a time when information is readily available, quickly and globally, and access to different materials has become cheaper than ever before, so should the need to dive deeper into this fascinating Spark big data world arise, I recommend checking the following websites and books.

Websites:


- [Spark Examples](https://spark.apache.org/examples.html)
- [Official Programming Guide: SparkSQL](https://spark.apache.org/docs/3.0.0/sql-programming-guide.html)
- [Official Programming Guide: RDDs](https://spark.apache.org/docs/3.0.0/rdd-programming-guide.html)
<br/>

**Books**

- [*Spark in Action Second Edition*](https://www.manning.com/books/spark-in-action-second-edition)

- [*Apache Spark 2.x for Java Developers*
](https://www.oreilly.com/library/view/apache-spark-2x/9781787126497/)

Learn about sources to dive deeper into Spark and Java

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

Where to Go from Here

Conclusion