Data Engineering, Serverless ETL & BI on Amazon Cloud

Rating 4.36 out of 5 (557 ratings in Udemy)

What you'll learn

Setting up a Data Warehouse on Amazon Cloud using Redshift from scratch
Learn and understand AWS Athena and when to make use of Athena
Learn how to store data in S3 Data lakes using Parquet columnar file formats and optimize the process of data scans using Athena
Learn and automate the ETL processes using different server-less components like AWS Glue , Data Pipeline and Lambda Functions
Data Centralization using Redshift …

Rating 4.36 out of 5 (557 ratings in Udemy)

What you'll learn

Setting up a Data Warehouse on Amazon Cloud using Redshift from scratch
Learn and understand AWS Athena and when to make use of Athena
Learn how to store data in S3 Data lakes using Parquet columnar file formats and optimize the process of data scans using Athena
Learn and automate the ETL processes using different server-less components like AWS Glue , Data Pipeline and Lambda Functions
Data Centralization using Redshift Spectrum
Trigger and Automate Glue jobs using Lambda Functions
Understand how to pull data into QuickSight which is a BI-Reporting/Visualization offering from AWS

Description

AWS Cloud can seem intimidating and overwhelming to a lot of people due to its vast ecosystem, but this course will make it easier for anyone who wants a hands-on expertise in setting up a data-warehouse in Redshift or setup a BI infrastructure from scratch .

Data Scientists/Analysts/Business Analysts will soon be expected to (if not already) become all-rounders and handle the technical aspect of data ingestion/engineering/warehousing .

Anyone who has the basic understanding of how cloud works can benefit from this course because :

- This course is designed keeping in mind end to end life cycle of a typical data engineering project

- Provides a practical solution to real-world use-cases

This Course covers :

Setting up a data warehouse in AWSRedshift from scratch
Basic Data Warehousing Concepts
Writing server-less AWSGlue Jobs (pyspark and python shell) for ETLand batch processing
AWSAthena for ad-hoc analysis (when to use Athena)
AWSData Pipeline to sync incremental data
Lambda functions to trigger and automate ETL/Data Syncing processes
QuickSight Setup , Analyses and Dashboards

Prerequisites for this course are :

Python / Sql (Absolute must)
PySpark (should know how to write some basic Pyspark scripts)
Willingness to explore ,learn and put in the extra effort to succeed
An active AWSAccount

Important Note - This course makes use of the free tiers for Redshift and RDS , so you will not be billed for them unless you exceed the free tier usage which should be more than enough to get enough practice from this course .

Also , this course makes use of AWS UIon the browser for creating clusters and setting up jobs , there is no bash scripting involved. One can use any operating system to perform the lab sessions in this course .

This course is not code-intense or code-heavy ,there is only 35% coding involved , the rest is execution,understanding and chaining different component together. The whole purpose of this course is to make everyone aware of and feel comfortable with all the tools/features used in this course .

Some Tips :

Try to watch the videos at 1.2X speed
Every time you work on a new component or feature , do some research on the other tools that are meant for the same purpose and see how they differ and in what aspects , For Eg Redshift/Athena vs Snowflake or Bigquery , QuickSight vs PowerBi vs Microstrategy

Duration 7 Hours 58 Minutes

Paid

Self paced

Beginner Level

English (US)

4817

Rating 4.36 out of 5 (557 ratings in Udemy)

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