Amazon ElastiCache: In-memory datastore fundamentals, use cases and examples

Databases | 6 Videos | 2 hours, 15 minutes

Learning Path

Access Resources

Download slide decks, check out additional helpful resources, and receive learning path email notifications.


Table of Contents

    • 1. In-Memory Fundamentals & Overview

      Amazon ElastiCache provides two popular in-memory datastores: Redis and Memcached. Learn why these datastores are used to accelerate workloads requiring low latency and high throughput, and how they compare to disk-based databases.
      You'll learn:
      1. Why application performance is business critical
      2. The differences between in-memory datastores and disk-based databases
      3. How Amazon ElastiCache, as a managed service, provides low-latency, high-throughput in-memory datastores
    • 2. Common In-Memory Use-Cases

      Learn why in-memory datastores are used across a growing number of high velocity use cases, including caching, online leaderboards, real-time data streaming with analytics, and geospatial queries.
      You'll learn:
      1. The unique advantage that in-memory data structures provide, compared to data on disk
      2. Common use cases customers implement using Amazon ElastiCache
      3. How Amazon ElastiCache enables these use cases with sub-millisecond access
    • 3. Diving Deep into Redis & Memcached data structures

      In this module you will learn about the different data structures available for Redis and Memchached and how to apply them within your applications.
      You'll learn:
      1. What data structures are available with supported in-memory datastores
      2. An overview of the data structures and their possible impact on performance
      3. Basic examples of how and when to use different data structures
    • 4. Caching patterns for different back-ends

      Virtually any API, datastore or service that can be queried can also be cached. In this module you will learn fundamental caching concepts and caching strategies, and review code examples of caching Amazon Relational Database Service (Amazon RDS) query results and Amazon Simple Storage Service (Amazon S3) objects.
      You'll learn:
      1. Fundamental caching concepts
      2. How to cache data from Amazon RDS and Amazon S3 with code snippets
      3. Tips for memory management, key naming conventions, and client configuration
    • 5. Redis PubSub, Streams and use-cases

      Redis Pub/Sub and Stream features are commonly used to support real-time notifications and streaming data. In this module we'll introduce the specific use cases that leverage these features, along with reviewing how these technologies work.
      You'll learn:
      1. When to use Redis Pub/Sub compared with Redis Streams
      2. Applicable use cases for each and how to implement
      3. Best practices to consider when using Pub/Sub and Streams
    • 6. Amazon ElastiCache Global Datastore for Redis

      Global Datastore in Amazon ElastiCache for Redis provides fully managed, fast, reliable, and secure cross-region replication. With Global Datastore, you can write to your ElastiCache for Redis cluster in one region and have the data available to be read from two other cross-region replica clusters, thereby enabling low-latency reads and disaster recovery across regions. In this module we'll review how to set up Global Datastore, its features and considerations for best utilization.
      You'll learn:
      1. Basics of Global Datastore enabling cross-region disaster recovery and improved read latencies
      2. How scaling and failover work with Global Datastore
      3. Technical considerations to use Global Datastore


Amazon ElastiCache is a managed Redis and Memcached service which allows you to seamlessly set up, run, and scale popular open-source compatible in-memory datastores in the cloud. Build data-intensive apps or boost the performance of your existing databases by retrieving data from high throughput and low latency in-memory datastores. Amazon ElastiCache is a popular choice for real-time use cases like Caching, Session Stores, Gaming, Geospatial Services, Real-Time Analytics, and Queuing.

Learning Objectives

  • Objective One

    Learn why in-memory datastores are uniquely qualified to support low latency and high throughput use cases.

  • Objective Two

    Understand in-memory data structures and where they best serve your application.

  • Objective Three

    Gain a deeper understanding of the caching and streaming use cases.