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

Become an AWS pro with hands-on experience in data analytics and management, developed by AWS Solution Certified Architects. No setup, no cleanup, no hassle.

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

Fills the AWS Glue Data Catalog with the schema of the data source

Generates an IAM role with permissions to access the data source and use AWS Glue

Generates metadata for an AWS Lake Formation setup

What's the purpose of a crawler in AWS Glue?

What’s the purpose of a crawler in AWS Glue?

**(True or False)** Before transforming data using AWS Glue DataBrew, it's useful to understand the dataset and/or to run a data profile on the dataset. 

(True or False) Before transforming data using AWS Glue DataBrew, it’s useful to understand the dataset and/or to run a data profile on the dataset.

**(True or False)** Running an AWS Glue DataBrew job on a data sample yields the same results as running it on the full dataset.

(True or False) Running an AWS Glue DataBrew job on a data sample yields the same results as running it on the full dataset.

Seamless Data Movement

Seamless Data Movement

Test your knowledge of Seamless Data Movement. 

Overview

Data Sources

Data Ingestion

Scalable Data Lake

Unified Governance

Seamless Data Movement

Purpose-Built Analytics and Insights

Wrap Up

Quiz: Seamless Data Movement