Data Engineering using AWS Data Analytics
Build Data Engineering Pipelines on AWS using Data Analytics Services - Glue, EMR, Athena, Kinesis, Lambda, Redshift
4.48 (2469 reviews)
27,137
students
26.5 hours
content
Nov 2024
last update
$119.99
regular price
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
Related Topics
4242194
udemy ID
8/14/2021
course created date
8/18/2021
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