Explore features of Spark SQL in practice on Spark 2.0

Explore features of Spark SQL in practice on Spark 2.0

The goal of this spark project for students is to explore the features of Spark SQL in practice on the latest version of Spark i.e. Spark 2.0.


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James Peebles

Data Analytics Leader, IQVIA

This is one of the best of investments you can make with regards to career progression and growth in technological knowledge. I was pointed in this direction by a mentor in the IT world who I highly... Read More

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Senior Data Scientist at Tiger Analytics

This was great. The use of Jupyter was great. Prior to learning Python I was a self taught SQL user with advanced skills. I hold a Bachelors in Finance and have 5 years of business experience.. I... Read More

What will you learn

Understanding the roadmap of the project
Downloading and installing Spark on Cloudera VM ware
What is difference between Spark and Spark shell
Why you should think Spark SQL before Spark Core
How Directed Acycylic graphs work undercover in RDD
Installing Java development KIt 8
Configuring Spark 2.0 for using Clusters services
When to use Spark Core
Copying the dataset to Hadoop database
Spark SQL and multiple file types: Text File, JSON File, RDBMS Sources, NoSQL Sources
How to read a JSON and a CSV format file
Reading from a Hive table, JDBC and a parquet file
Understanding the usage of Typed and Untyped columns
Spark SQL for SQL-on-Hadoop server
Using Spark SQL as JDBC server and using Spark for Structured data processing

Project Description

Spark 2 offers a huge but yet backward-compatible break from the Spark 1.x, not only in terms of high-level API but also in performance. And spark the module with the most significant new features is Spark SQL.

In this apache spark project, we will explore a number of this features in practice.

We will discuss using various dataset, the new unified spark API as well as the optimization features that makes Spark SQL the first way to explore in processing structured data.

However, there are times when it is inevitable to resort to Spark Core - RDD in Spark 2. We will explore that as well alongside the newest and cool structured streaming API that enables fault-tolerant stream processing engine built on the Spark SQL engine.

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Curriculum For This Mini Project

Project Overview
Manual Installation of Spark 2 on Cloudera Quickstart VM
Introduction to Spark
Difference between Spark 2 and Spark Shell
Spark RDD's and DAG
Install JDK 8
What is Spark SQL?
Installing Spark 2.0
Configurations to add Spark 2.0 to the services in the Cluster
Download Datasets and Copy ot HDFS
Spark Session
Read a JSON File
Dataframe and Dataset[T] in Spark 2
Difference between Dataframe Dataset[T] in Spark 2
Read a CSV file
Read a Hive Table
Read from JDBC
Read from a Parquet File
Why you should think of Spark SQL before Spark Core?
Discussion on the Agenda for Next Session
Recap of the Previous Session
Download the Dataset for the Session
Understanding the usage of Typed and Untyped Columns
Usage of Typed Columns using Airport Dataset
Using Spark SQL as a JDBC Server
When to use Spark SQL
Using Spark SQL for Structured Data Processing using Spark 2 Shell-Example
Structured Streaming Example