How to handle billions write event? How to improve performance of distributed workload? How to handle a request by invoking multiple microservices and aggregating the results? Data collection limit to process latency. How to run min downtime? Reprocess historical data.

1. On a different platform, using different programming languages and developer tools
2. Teams can develop, maintain, and deploy each microservice independently.
3. Reduce the time to market and speed up CI/CD pipeline

1. Event Driven Architecture for Improved Performance.
2. Allows Zero-Downtime Maintenance.
3. Supports Traffic Encryption & SSL Termination.
4. Several Load Balancing Algorithms/Configurations.
5. Access to Integrated Server Monitoring Dashboard.
6. Optimized HTTP Header Analysis.

A type of data processing engine that is designed with infinite data sets in mind.

• Spark is popular

  • High throughput, good for many use cases where sub-latency is not required.
  • Fault tolerance by default due to micro-batch nature.
  • imple to use higher level APIs.

• Samza can reprocess historical data quickly

  • Very good in maintaining large states of information.
  • Fault Tolerant and High performant.