Overview
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Explore the data services on the Amazon cloud. Learn how to configure relational and NoSQL data storage, data warehouses, graph and ledger databases, data lakes, and more.
Syllabus
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Introduction
- Scalable data solutions on AWS
- What you should know
- Use AWS Cloud services
- Use Amazon Web Services (AWS) data services
- Why use AWS data storage services?
- Explore file and HDFS storage
- Explore AWS data storage
- Understand why AWS Cloud tools matter
- Explore the core file storage choices
- Explore AWS S3
- Explore AWS S3 using AWS CLI
- Explore AWS Glacier storage
- Explore AWS EBS and EFS
- Understand file import dervices for AWS and compare file services
- Scenario: Use cloud file storage
- Explore AWS RDS for managed RDBMS
- Explore AWS RDS MySQL
- Explore AWS RDS Aurora Serverless
- Use RDS query editor
- Scenario: Move on-premises relational data to AWS
- Explore NoSQL options in AWS
- Understand AWS ElastiCache
- Explore AWS DynamoDB
- Use AWS CLI with AWS DynamoDB
- Explore AWS DocumentDB
- Scenario: Work with caching and real-time data
- Explore the AWS data warehouse options and Redshift
- Work with AWS Redshift
- Connect to AWS Redshift with AWS query editor
- Visualize data with AWS QuickSight
- Scenario: Build a data warehouse in the cloud
- Explore AWS specialty databases including graph and ledger
- Explore graphs with AWS Neptune
- Explore ledgers with AWS QLDB
- Explore event databases including Amazon Timestream
- Explore Hadoop and Spark on AWS
- Understand Hadoop jobs and libraries
- Explore AWS EMR with Hadoop and Spark
- Run Spark job on a Jupyter Notebook on AWS EMR
- Understand a data lake pattern with AWS Lake Formation
- Explore AWS Athena
- Explore AWS Glue Data Catalog and Crawlers
- Use AWS Glue for ETL
- Explore a data lake pattern with AWS Lake Formation
- Scenario: Build for the Internet of Things with Hadoop
- Keep learning AWS services
