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

**(True or False)** SparkSQL only utilizes the Spark Session object to run queries against a DataFrame.


(True or False) SparkSQL only utilizes the Spark Session object to run queries against a DataFrame.

Definition or redefinition of a *DataFrame*'s schema

**(Select all that apply.)** Which of the following actions can be performed on a DataFrame’s schema? 


(Select all that apply.) Which of the following actions can be performed on a DataFrame’s schema?

**(True or False)** Spark is able to ingest, or read, multiline JSON files by including an option on the `DataFrameReader` object such as `.option("multiline", true)`.


(True or False) Spark is able to ingest, or read, multiline JSON files by including an option on the <code>DataFrameReader</code> object such as <code>.option(&quot;multiline&quot;, true)</code>.

The process of saving the totality of a DataFrame by only calling the `jdbc(...)` method on a DataFrame object.

The possibility of using both the `jdbc()` method call on a DataFrame and also manually exporting a DataFrame by using transformations such as `MapFunction` or `MapPartitions`.


Which of the following sentences best describes the operation of exporting information from a DataFrame to a database?


Which of the following sentences best describes the operation of exporting information from a DataFrame to a database?

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: SparkSQL and Other Functionalities