In this chapter, we will discuss patterns for batch and stream processing, queuing chains, priority queues, and job observers. AWS has many tools to make much of this simple and to remove many potential issues encountered in trying to manage and scale these services.
We will investigate dynamic data patterns such as state sharing, URL rewriting, rewrite/cache proxying, data replication, in-memory caching, and sharding. Identifying appropriate constructs for handling data, designing appropriate intervals for data collection, detecting meaningful patterns in events, and understanding how to share information across instances are the main highlights of this chapter.
The following topics will be covered in this chapter in brief:
- Queueing
- Batching
- Caching
- Event stream processing
- Machine learning
