Course Information
Registration period
Year-round Recruitment
Course Level
Study Mode
Duration
1 Hour(s) 21 Minute(s)
Language
English
Taught by
Tushar Bhalla
Rating
2 views
Course Overview
ETL & BI on AWS Cloud
A hands on course that covers majority of the typical data engineering / ETL scenarios.
In this course you will learn:
Different services and concepts of AWS data engineering
Creating serverless data lake using S3, Glue and Athena
Ingesting data using Rest Api
Ingesting data using Sftp server
Ingesting data into Database (AWS RDS - Postgre SQL)
Incremental data loading
Prerequisites:
An active AWS account
Python / SQL knowledge
Course Content
- 3 section(s)
- 14 lecture(s)
- Section 1 Introduction
- Section 2 Data Engineering Services
- Section 3 Live Demos
What You’ll Learn
- Data engineering concepts and AWS services, Fetching data from external Rest Api, Fetching data from SFTP server, Ingesting data into a Database, Creating Serverless Data Lake using S3 and Athena, Creating Glue ETL jobs and Workflows, Transforming data into Parquet format, Learn how to store data in S3 Data lakes using Parquet columnar file formats
Skills covered in this course
Reviews
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MMohan Raj
This course is good for beginners
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MMariano Lo Cane
Hay espacios que no llena como crear conectores. Asimismo la version de AWS con la que trabaja ya es muy diferente al dia de hoy.
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AAleksey Fedotenko
The course touches upon the foundational aspects of AWS Glue with a brief overview of other Data Engineering related AWS services. The course has some demos but quite sadly it lacks the resource files needed to follow the instructions and fulfill all the steps yourself. It's rather good to get an overall understanding of what AWS Glue is. And it could be much better if those missing resource files were shared in some GitHub repo to make it a genuine "follow-me" tutorial.
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KKaustuv Majumder
1. Most of the details have been skipped. 2. Every resource was already created beforehand. The instructor should actually perform the operation during the demo. 3. Oversimplified use cases. No real business scenario.
