aws_access_key

aws_secret_access_key

facebook_access_token

bucket_name

opensearch_host

search-domain-opensearch-domain.us-east-1.es.amazonaws.com

opensearch_password

redshift_password

Data Analytics on AWS- An Architectural Guide.png

Dockerfile.tar.gz

connect.py

get-fb-data.py

count-s3.py

send-firehose.py

upload-s3.py

show-logs.py

send-data-stream.py

index-data.py

create-table.py

spark-demo.py

detect-language.py

Due to the widespread adoption of the internet and the prevalence of connected devices, a large amount of data is generated every second. This abundance of data allows businesses to gather valuable insights and reinvent their businesses. Amazon Web Services (AWS) offers a range of solutions to help address these challenges.

This course provides an overview of the data management and analytics services provided by AWS. You’ll begin with understanding different databases and how to migrate data between them. Then, you’ll learn about Amazon Simple Storage Service (S3) and set up data lakes. Next, you’ll learn about AWS data security features, including S3 encryption, Lake Formation access controls, and audit logging. You’ll also set up and run Extract, Transform, and Load (ETL) workflows on AWS Glue. You’ll finish with in-depth purpose-built analytics tools in AWS.

By the end of this course, you’ll have mastered the data analytics and management tools that AWS offers.

Data Analytics on AWS: An Architectural Guide

Conclude the course and reflect on ways to work with data, data analytics, AWS, and beyond.


AWS and Beyond

Overview

Data Sources

Data Ingestion

Scalable Data Lake

Unified Governance

Seamless Data Movement

Purpose-Built Analytics and Insights

Wrap Up

AWS and Beyond