This course aims to teach learners how to efficiently process streaming static data using Akka Persistence. The main goals include understanding the concepts of streaming data processing, learning about Akka Persistence Query, and implementing stream production specifications in Cassandra plugin for Akka Persistence. The course covers architecture, design considerations, performance tuning, and distributed system specifics. The teaching method involves detailed discussions, implementation details, and practical application of the concepts. The intended audience for this course is individuals interested in building modern reactive enterprise stream processing and asynchronous messaging distributed applications.
Data in Motion - Streaming Static Data Efficiently in Akka Persistence
Scala Days Conferences via YouTube
Overview
Syllabus
Intro
Databases
Batch processing
Data at scale
Streaming static data
Pulling data from source
Inserts
Updates
Pushing data from source
Infinite streams of finite data source
Log data structure
Pulling data from a log
Akka Persistence Query Cassandra
Actor publisher
Events by persistence id
All persistence ids SELECT DISTINCT persistence_id, partition T
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
Optimisation
Table and stream duality
Infinite streams application
Distributed systems
Challenges
Conclusion
Taught by
Scala Days Conferences
