Iterable uses Akka Streams to manage streams of tens of millions of events daily for some of the world's largest consumer companies. In this talk we'll discuss how we use Akka Streams to ingest streams of event data into Elasticsearch. We'll show how Akka Streams makes writing streaming-related code more elegant, flexible, and performant. We'll discuss the various stream operations such as `mapAsync` and `groupedWithin`, as well as more complex patterns such as MergeHub and PartitionHub. We'll look at how Akka Streams deals with common streaming data issues such as idempotency, ordering of updates, and backpressure. Finally, we'll discuss practical issues such as measuring performance, testing, and tuning an Akka Streams-based system in the context of massive data ingestion into a distributed, eventually consistent database.
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