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.
- On a different platform, using different programming languages and developer tools
- Teams can develop, maintain, and deploy each microservice independently.
- Reduce the time to market and speed up CI/CD pipeline
- Event Driven Architecture for Improved Performance.
- Allows Zero-Downtime Maintenance.
- Supports Traffic Encryption & SSL Termination.
- Several Load Balancing Algorithms/Configurations.
- Access to Integrated Server Monitoring Dashboard.
- Optimized HTTP Header Analysis.
A type of data processing engine that is designed with infinite data sets in mind.
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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.
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Samza can reprocess historical data quickly
- Very good in maintaining large states of information.
- Fault Tolerant and High performant.