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
Why there was a need for Spark
PySpark RDD – hands-on
PySpark SQL, DataFrame – hands-on
Project work using PySpark and Hive
Spark Scala DataFrame
Project working using Spark Scala
Google Colab environment
Bonus project – Applying spark transformation on data stored in AWS S3 using Glue and viewing data using Athena
Bonus project – Build your first Machine Learning model using Python, Scikit-learn to predict whether a customer will buy or not.
Some basic programming skills
Some knowledge of SQL queries
Learn the advantages of a serverless data lake solution over a Hadoop Platform
Run the bank transformation code using AWS Glue. Store the prospects data in a bucket and apply transformation using the same code that you have executed in the Colab and Dataproc environment