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

When there's streaming data in Amazon Kinesis

When might it be useful to do data analysis in Managed Service for Apache Flink Studio?

When might it be useful to do data analysis in Managed Service for Apache Flink Studio?

After connecting data to Amazon QuickSight, which feature can be used to create a dashboard with interactive elements?

After connecting data to Amazon QuickSight, which feature can be used to create a dashboard with interactive elements?

**(True or False)** To be able to search data through Amazon OpenSearch, there must be an OpenSearch index and the data must be added to this index.

(True or False) To be able to search data through Amazon OpenSearch, there must be an OpenSearch index and the data must be added to this index.

Through a query user interface such as the Redshift query editor 

Through code that connects to the data warehouse 

Through a business-intelligence tool such as Amazon QuickSight

How can users access and analyze data in a data warehouse such as Amazon Redshift?

How can users access and analyze data in a data warehouse such as Amazon Redshift?

**(True or False)** Amazon EMR or EMR Serverless is required to use open-source big-data tools such as Apache Spark.

(True or False) Amazon EMR or EMR Serverless is required to use open-source big-data tools such as Apache Spark.

**(True or False)** Using Amazon SageMaker Studio Lab requires an AWS account and a credit card. 

(True or False) Using Amazon SageMaker Studio Lab requires an AWS account and a credit card.

When Amazon CodeWhisperer or GitHub Copilot can write most code for us

When existing AI services can achieve the functionality we want at a sufficient quality level, and within budget

When generative AI or ChatGPT is better than humans at most tasks 

When might it make sense to use existing AI services instead of training our own ML models?

When might it make sense to use existing AI services instead of training our own ML models?

**(True or False)** Redshift Spectrum makes sense to try if we already have an Amazon Redshift data warehouse and we wish to query existing data in S3.

(True or False) Redshift Spectrum makes sense to try if we already have an Amazon Redshift data warehouse and we wish to query existing data in S3.

Athena and AWS Glue are separate tools, and Athena doesn't make use of AWS Glue when querying data.

AWS Glue hosts Python notebooks, and Athena now also supports Python notebooks for querying and analyzing data.

Athena can use databases and tables in the AWS Glue Data Catalog to understand the structure of S3 data toward retrieving results for SQL queries on that data.

How does Amazon Athena make use of AWS Glue when querying data?

How does Amazon Athena make use of AWS Glue when querying data?

Purpose-Built Analytics &amp; Insights

Purpose-Built Analytics &amp; Insights

Test your knowledge of purpose-built analytics and insights.

Overview

Data Sources

Data Ingestion

Scalable Data Lake

Unified Governance

Seamless Data Movement

Purpose-Built Analytics and Insights

Wrap Up

Scalable Machine Learning Model for Accurate Predictions on AWS

Quiz: Purpose-Built Analytics & Insights