Almost every workload needs somewhere to keep its data, and the cloud offers many options that differ in how data is structured, how fast it can be read, and what it costs. Matching the right storage or database service to each workload's access pattern is a core cloud architect skill, and this module covers the main choices.
You'll start with the three storage types, object, block, and file storage, using Amazon S3, EBS, and EFS as examples, and learn when each one fits. Then you'll move on to databases: Amazon RDS for managed relational databases, including high availability with Multi-AZ and read replicas, and DynamoDB for fully managed NoSQL workloads.
The module closes with analytics storage, comparing a data warehouse with a data lake and the newer lakehouse approach. Next up is identity and security, which governs who can reach all of this data.
A quick comparison of AWS's three main storage services, S3 for object storage, EFS for file storage, and EBS for block storage, and when to use each one.
How Amazon RDS, AWS's managed relational database service, is designed for availability and scale, including the difference between Multi-AZ deployments and read replicas.
An introduction to DynamoDB, a serverless NoSQL database built for scalability and low latency, covering data modeling, keys, indexes, and capacity.
A plain-language explanation of what a data warehouse, a data lake, and a data lakehouse are, how they differ, and how that relates to ETL and OLAP versus OLTP workloads.