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

## SQL in SparkSQL

**Structured Query Language** (abbreviated SQL) has been a golden standard for manipulating data for many years now. It is a powerful tool used widely, so much that top-of-the-art cloud services (AWS S3, among others) provide SQL-like functionality to inspect and retrieve data. It also provides a human-readable syntax and its learning curve is not too steep.

All these positive aspects made it a reasonable choice to embed SQL into the SparkSQL module, even more so if the data Spark works with, on many occasions, is structured or semi-structured.


This lesson focuses on teaching how to use SparkSQL to make our lives easier as Big Data apprentices.

>If your SQL knowledge be a bit rusty, check some great courses here on educative.io.



## A practical introduction to SparkSQL

Just as SQL relational databases can expose a view of a table that can be accessed by any application needing to interact with the DataSource, SparkSQL follows a similar approach but requires a view as the main entry point to querying a DataFrame.

The below diagram shows how a view is an abstraction on top of the DataFrame when we interact with it through SparkSQL:

 

# SQL in SparkSQL

**Structured Query Language** (abbreviated SQL) has been a golden standard for manipulating data for many years now. It is a powerful tool used widely, so much that top-of-the-art cloud services (AWS S3, among others) provide SQL-like functionality to inspect and retrieve data. It also provides a human-readable syntax and its learning curve is not too steep.

All these positive aspects made it a reasonable choice to embed SQL into the SparkSQL module, even more so if the data Spark works with, on many occasions, is structured or semi-structured.


This lesson focuses on teaching how to use SparkSQL to make our lives easier as Big Data apprentices.

>If your SQL knowledge be a bit rusty, check some great courses here on educative.io.



# A practical introduction to SparkSQL

Just as SQL relational databases can expose a view of a table that can be accessed by any application needing to interact with the DataSource, SparkSQL follows a similar approach but requires a view as the main entry point to querying a DataFrame.

The below diagram shows how a view is an abstraction on top of the DataFrame when we interact with it through SparkSQL:

 

Get introduced to the functionality in Spark that allows the developer to query DataFrames in a relational database manner: Spark SQL.

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

Spark SQL Goodness

SQL in SparkSQL

A practical introduction to SparkSQL