Apache Flink vs. TIBCO BusinessEvents

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

TIBCO BusinessEvents is part of the CEP (Complex Event Processing) family, this means that it fits perfectly in all those scenarios where a correlation between incoming events is required. Where a stateful process is necessary. It does not fit well for a kind of Process Orchestrator scope, where simple events are coming in, and there is a well-defined behavior the system, would have on incoming request, and no particular reason to use a rule engine and its complexity. Anyway, there are particular cases where BusinessEvents would be a good actor in orchestrating a portion of CEP solutions activities

Incentivized

• 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

• It allows us to build rule-based model-driven application, to collect, filter, analyze, correlate various business events in our real-time event flow
• It makes various business applications/components easy to integrate (loosely decoupled but chained via the events flow) together
• Its distributed rule engine and embedded in-memory data grid (ActiveSpace) gives us a lot of flexibility and room to play with a large amount of rules and data with high performance

Incentivized

• 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

• Better integration with R versions and better debug for R-scripts in Spotfire. There are inconsistencies in syntactic expressions accepted by R-studio and not accepted in Spotfire. Accelerating the debug would be awesome. Having a command like View (data frame) that directly output in the dashboard would be a great accelerator.

Incentivized

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

I was not part of evaluation of the products in this space in my organization. But I feel BE is better in terms of RIO if compared with some commercial products from Orcle, IBM and SAP. I strongly feel difficulty in using cloud native features is one big shortcoming in current product offering. This will tend customer like us to explore options that are well suited with ur cloud first vision.

Incentivized

• 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

• Positive : Real-time event stream processing helps handle transactions very efficiently.
• Negative : Earlier versions of BE (5.0 & 5.1) had major issues in data handling in cache that impacted the data integrity of the application.
• Positive : Decision Tables are a huge plus in TIBCO BE.

Incentivized