Explore How Apache Kafka and AWS are Powering Distributed Messaging
Build and run real-time streaming data applications. Set up data pipelines to ingest streams of records for Big Data analytics and also for applications that process, store or work with that data in other ways.
Common Challenges when implementing distributed messaging platform
Software Engineering
As application requirements are decomposed into smaller software entities, the modular design, maintainability and extensibility should be consistently maintained.
Concurrency
With resource-hungry applications such as video editing tools and games, state consistency, livelock avoidance and race condition dependant behaviour are a concern.
Fault Tolerance
Distributed networks are inherently complex and fault tolerance is a concern. Based on the CAP theorem, you have to choose only two options among three options; Consistency of data across multiple sites, High Availability of data and Partition Tolerance.
High scaling
Distributed systems span across multiple datacenters and regions which means horizontal scaling that gives performance benefits come at a cost and complexity.
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Build and run real-time streaming data applications. Set up data pipelines to ingest streams of records for Big Data analytics and also for applications that process, store or work with that data in other ways.
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