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

## Sorting in Spark


In the Spark Java API, there are several sorting methods available to bring some order to DataFrames’ records based on specified criteria. In this lesson, we introduce the two most common ones and go through some examples.

Sorting in Spark is internally a very complex operation that might depend on variables like these below:

-  Types of columns we use, such as date columns, numerical, alphabetical, and so on. 

- Order, and if we’re sorting in ascending or descending order. 
- Where the first and last values reside.
 

The nature of the sorting algorithm used falls outside the scope of this course; however, one thing we can be sure of: since data might be spread across many nodes, Spark cannot simply hold every piece of data in a single location, so shuffling is very likely to happen.


We can find the project for this lesson below:




# Sorting in Spark


In the Spark Java API, there are several sorting methods available to bring some order to DataFrames’ records based on specified criteria. In this lesson, we introduce the two most common ones and go through some examples.

Sorting in Spark is internally a very complex operation that might depend on variables like these below:

-  Types of columns we use, such as date columns, numerical, alphabetical, and so on. 

- Order, and if we’re sorting in ascending or descending order. 
- Where the first and last values reside.
 

The nature of the sorting algorithm used falls outside the scope of this course; however, one thing we can be sure of: since data might be spread across many nodes, Spark cannot simply hold every piece of data in a single location, so shuffling is very likely to happen.


We can find the project for this lesson below:




 Let’s learn how Spark sorts data and how to sort data with the Spark API. 

Sort and OrderBy

Let’s learn how Spark sorts data and how to sort data with the Spark API.

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

Sort and OrderBy

Sorting in Spark