Data in Motion - Streaming Static Data Efficiently in Akka Persistence

Explore streaming data processing in Scala and the Lightbend Reactive platform through this 47-minute conference talk from Scala Days Berlin 2016. Dive into the advantages and concepts of streaming data processing, focusing on Akka Persistence Query and its implementation in the Cassandra plugin for Akka Persistence. Examine architecture and design considerations, implementation details, performance tuning, and distributed system specifics such as correctness, efficiency, consistency, order, causality, and failure scenario handling. Learn about improvements to the Cassandra plugin, including non-blocking asynchronous Akka Persistence recovery, and discover how to apply these concepts to build modern reactive enterprise stream processing and asynchronous messaging distributed applications. Cover topics like data at scale, streams, log data structures, event time processing, distributed causal stream merging, exactly once delivery, and challenges in distributed systems.

Intro
Data at scale
Streams
Log data structure
Akka Persistence Query
Streaming static data
Pulling data from a log
Actor publisher
Events by persistence id
All persistence ids
Events by tag
Akka Persistence Cassandra Replay
Non blocking asynchronous replay
Benchmarks
Alternative architecture
Event time processing
Ordering
Distributed causal stream merging
Exactly once delivery
Akka Analytics
Distributed systems
Challenges
Conclusion
Questions