Apache Flink vs. IBM Event Automation

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

IBM Event Streams is well suited for companies developing event driven Microservices. One of the biggest challenger with microservices is that your data gets distributed into little silos - event streaming (or better known as event sourcing) allows you to get a central source of truth in your event store. We are taking this approach with IBM Event Streams and it is well suited for building an event streaming / sourcing architecture.

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 is adaptive and helps us create more engaging experiences on our platforms.
• The Key metrics dashboard is rich with insights.

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

• Provide Capabilities to connect the Event Streams via REST Proxy.
• Schema Registry to handle Avro Formats.
• Provide Kafka Connect Sink & Source Connectors.

Incentivized

The product was very user friendly and extremely easy to get started with. The documentation is excellent and the free tier makes it very easy to get started with without having to make deep or long term financial commitments.

Incentivized

I met with the support team and they have deep technical and development understanding of the needs and the problems which IBM Event Streams addresses. If you are looking for a product backed by a highly technical support team then IBM Event Streams is probably the best choice. I was specifically impressed by the level of technical understanding my support team demonstrated.

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

In Event Streams, applications send data by creating a message and sending it to a topic. To receive messages, applications subscribe to a topic. High availability and reliability. Event Streams offers a highly available and reliable Apache Kafka service running on IBM Cloud. Event Streams. Event Streams stores three replicas of your data to ensure the highest level of resilience across three availability zones.

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

• In using downstreams, the minimal features and the rate of releases were slow, makes us feel that there's no upgrades and other than that there's poor marketing of the product.
• The adoption around the service is low, requires focused marketing.
• Lack of visibility into topic depth , Monitoring capabilities

Incentivized