This course aims to teach learners the advantages and concepts of processing streaming data efficiently using Akka Persistence Query and Akka Streams. The main goals include understanding the differences between static data and data in motion, implementing stream production specifications in Cassandra plugin for Akka Persistence, and discussing architecture, design considerations, and performance tuning. The course covers topics such as event processing, distributed systems challenges, and building modern reactive enterprise stream processing applications. The intended audience for this course is individuals interested in distributed systems, stream processing, and asynchronous messaging applications.
Data in Motion - Streaming Static Data Efficiently in Akka Persistence
Scala Days Conferences via YouTube
Overview
Syllabus
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
Taught by
Scala Days Conferences
