Data Engineering using AWS Data Analytics

Build Data Engineering Pipelines on AWS using Data Analytics Services - Glue, EMR, Athena, Kinesis, Lambda, Redshift

4.45 (2699 reviews)

Udemy

platform

English

language

Data Science

What you will learn

Data Engineering leveraging Services under AWS Data Analytics

AWS Essentials such as s3, IAM, EC2, etc

Understanding AWS s3 for cloud based storage

Understanding details related to virtual machines on AWS known as EC2

Managing AWS IAM users, groups, roles and policies for RBAC (Role Based Access Control)

Managing Tables using AWS Glue Catalog

Engineering Batch Data Pipelines using AWS Glue Jobs

Orchestrating Batch Data Pipelines using AWS Glue Workflows

Running Queries using AWS Athena - Server less query engine service

Using AWS Elastic Map Reduce (EMR) Clusters for building Data Pipelines

Using AWS Elastic Map Reduce (EMR) Clusters for reports and dashboards

Data Ingestion using AWS Lambda Functions

Scheduling using AWS Events Bridge

Engineering Streaming Pipelines using AWS Kinesis

Streaming Web Server logs using AWS Kinesis Firehose

Overview of data processing using AWS Athena

Running AWS Athena queries or commands using CLI

Running AWS Athena queries using Python boto3

Creating AWS Redshift Cluster, Create tables and perform CRUD Operations

Copy data from s3 to AWS Redshift Tables

Understanding Distribution Styles and creating tables using Distkeys

Running queries on external RDBMS Tables using AWS Redshift Federated Queries

Running queries on Glue or Athena Catalog tables using AWS Redshift Spectrum

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Our Verdict

This AWS Data Analytics course offers comprehensive insights into various AWS services for data engineering. While the hands-on approach caters well to active learners, some organizational flaws and outdated content can make navigation challenging. However, with improvements in clarity and platform compatibility, this course has strong potential to empower data engineers.

What We Liked

Comprehensive coverage of data engineering using AWS, including services like EC2, Lambda, Redshift, EMR, DynamoDB.
Hands-on labs and exercises provide practical experience with AWS data analytics tools and technologies.
Instructor is knowledgeable and experienced in data analytics and AWS, providing clear and easy-to-understand explanations.

Potential Drawbacks

Lack of continuity between lessons and exercises, causing repetition and confusion for learners.
Some parts of the course seem outdated, including the interface and console used in lectures.
The speaker talks fast and sometimes breezes through content without providing clear explanations.
Limited support for Windows users, with no mention or note of productionalization differences on non-Mac platforms.