Apache Flink vs. WSO2 Enterprise Integrator

In well-suited scenarios, I would recommend using Apache Flink when you need to perform real-time analytics on streaming data, such as monitoring user activities, analyzing IoT device data, or processing financial transactions in real-time. It is also a good choice in scenarios where fault tolerance and consistency are crucial. I would not recommend it for simple batch processing pipelines or for teams that aren't experienced, as it might be overkill, and the steep learning curve may not justify the investment.

Incentivized

The best-suited scenario is the service chain pattern or all patterns used in online mode. The less appropriate scenario is a batch service the duration time of the service is more than 10minutes because it is necessary to increase the HTTP timeout.

• Low latency Stream Processing, enabling real-time analytics
• Scalability, due its great parallel capabilities
• Stateful Processing, providing several built-in fault tolerance systems
• Flexibility, supporting both batch and stream processing

Incentivized

• Service chain pattern
• Message transformation
• Call more than one backend service
• Call service with different protocoll as the client use
• Use file as input
• Use mail as input

• Python/SQL API, since both are relatively new, still misses a few features in comparison with the Java/Scala option
• Steep Learning Curve, it's documentation could be improved to something more user-friendly, and it could also discuss more theoretical concepts than just coding
• Community smaller than other frameworks

Incentivized

• View deploy artifact in date time order
• Easy way to map data
• Easy way to manage payload

Apache Spark is more user-friendly and features higher-level APIs. However, it was initially built for batch processing and only more recently gained streaming capabilities. In contrast, Apache Flink processes streaming data natively. Therefore, in terms of low latency and fault tolerance, Apache Flink takes the lead. However, Spark has a larger community and a decidedly lower learning curve.

Incentivized

We can start with the community version and then when we moved into production we can buy the supported version. The supported and community version have the same code so we can do every test before deciding to buy the supported version.

• Allowed for real-time data recovery, adding significant value to the busines
• Enabled us to create new internal tools that we couldn't find in the market, becoming a strategic asset for the business
• Enhanced the overall technical capability of the team

Incentivized

• Reduce development time to the service integration
• We can use junior developer
• Clustered in easy way