Big Data , Hadoop and Spark from scratch by solving a real world use case using Python and Scala
Spark Scala & PySpark real world coding framework.
Real world coding best practices, logging, error handling , configuration management using both Scala and Python.
Serverless big data solution using AWS Glue, Athena and S3
Students should have some programming background and some knowledge of SQL queries.
Get started with Big Data quickly leveraging free cloud cluster and solving a real world use case! Learn Hadoop, Hive , Spark (both Python and Scala) from scratch!
Learn to code Spark Scala & PySpark like a real world developer. Understand real world coding best practices, logging, error handling , configuration management using both Scala and Python.
A bank is launching a new credit card and wants to identify prospects it can target in its marketing campaign.
It has received prospect data from various internal and 3rd party sources. The data has various issues such as missing or unknown values in certain fields. The data needs to be cleansed before any kind of analysis can be done.
Since the data is in huge volume with billions of records, the bank has asked you to use Big Data Hadoop and Spark technology to cleanse, transform and analyze this data.
What you will learn :
Big Data, Hadoop concepts
How to create a free Hadoop and Spark cluster using Google Dataproc
Hadoop hands-on - HDFS, Hive
PySpark RDD - hands-on
PySpark SQL, DataFrame - hands-on
Project work using PySpark and Hive
Spark Scala DataFrame
Project work using Spark Scala
Spark Scala Real world coding framework and development using Winutil, Maven and IntelliJ.
Python Spark Hadoop Hive coding framework and development using PyCharm
Building a data pipeline using Hive , PostgreSQL, Spark
Logging , error handling and unit testing of PySpark and Spark Scala applications
Spark Scala Structured Streaming
Applying spark transformation on data stored in AWS S3 using Glue and viewing data using Athena
Some basic programming skills
Some knowledge of SQL queries
Who this course is for:
Beginners who want to learn Big Data or experienced people who want to transition to a Big Data role
Big data beginners who want to learn how to code in the real world
19 sections • 97 lectures • 8h 39m total length
Big Data concepts
Storing data in HDFS and querying with Hive
PySpark - Spark SQL and DataFrame
Running PySpark on a Hadoop Cluster
Project - Bank prospects marketing data transformation using Hadoop and Spark
Rapid Revision - Big Data, Hadoop and Spark concepts
Spark SQL DataFrame using Scala
Bank prospects marketing project in Scala
AWS data lake - S3, Glue and Athena introduction
Create a data lake on AWS S3
AWS Glue crawler and AWS Athena query tool
ETL transformation using AWS Glue
Triggering AWS Glue job with a serverless AWS Lambda function
Project - Bank prospects data transformation using S3, Glue & Athena services
Fast queries with Hive Partitioning
Fast queries with Hive Bucketing
Advanced Spark datasets
User Defined Function (UDF)
Joins - Left, Right, Inner, Outer
Spark Scala real world coding introduction
Installing JDK on a local Machine
Installing IntelliJ IDEA
Adding Scala Plugin to IntelliJ
Scala basics using IntelliJ
Hello World Spark Scala using IntelliJ
Configuring HADOOP HOME on Windows using Winutils
Enabling Hive Support in Spark Session
psql command line interface for PostgreSQL
Importing a project into IntelliJ
Organizing code with Objects and Methods
Implementing Log4j SLf4j Logging
Exception Handling with try, catch, Option, Some and None