Apache Kafka is an open-source stream processing platform developed by the Apache Software Foundation written in Scala and Java. The Kafka event streaming platform is used by thousands of companies for high-performance data pipelines, streaming analytics, data integration, and mission-critical applications.
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IBM webMethods
Score 8.8 out of 10
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IBM® webMethods offers a hybrid, enterprise-grade integration platform as a service (iPaaS) that allows users to securely control applications, APIs, B2B and files across environments and locations.
Apache Kafka is built for scale. From high throughput and real-time data streaming, it has a strong advantage over RabbitMQ with its low latency. This put Apache Kafka at the forefront as the platform of choice for large datasets messaging and ensuring scalability when data …
Apache Kafka can work at a higher scale as compared to SQS. It can work with higher size per message and millions of messages per second. Moreover it can be scaled horizontally by adding more brokers to the cluster. SQS is good enough for simple use cases like making a task …
I used other messaging/queue solutions that are a lot more basic than Confluent Kafka, as well as another solution that is no longer in the market called Xively, which was bought and "buried" by Google. In comparison, these solutions offer way fewer functionalities and respond …
Apache Kafka is open-sourced, scales great has cloud agnostics and performs better than Amazon Kinesis [in my view]. Amazon Kinesis has some limitations and vendor lockin is not something I [like]. With Confluent operators you can easily install it on a kubernetes cluster.
We really needed to get away from using a SQL database to act as a queue for processing records, so a new solution was needed. Kafka is a leading software application initially designed for queuing messages which is essentially what we were looking for. It has a great user …
For us, Kafka really doesn't have a 1:1 alternative. We have used ActiveMQ extensively and we still use it as a lighter option for small messages. The situation is similar with Redis - although it could be used like a Kafka alternative, we do use it just as a per-component …
Apache Kafka is much more scalable and more reliable. Does not depend on memory, works well on rotational disks and that makes it a cheaper to use solution on low hardware requirements. Running multiple consumers on the same topic can also mean processing the same data again …
All stack tech helps our app and system. These technologies allow us to have the data available faster between different regions (due to our particular configuration) and thus the data and processing load of each system is lower. This allows the systems to be used more …
Kafka is not a real messaging broker implementation as RabbitMQ or TIBCO EMS/JMS are. Although it can be used as messaging, we like the idea behind the Kafka (data isn't "passing by," instead it remains centra, so the client can revisit the data if necessary). This also …
Confluent Cloud is still based on Apache Kafka but it has a subscription fee so, from a long term perspective, it is wiser to deploy your own Kafka instance that spans public and private cloud. Amazon Kinesis, Google Cloud Pub/Sub do not do well for a very number of messages …
I would only use RabbitMQ over Kafka when you need to have delay queues or tons of small topics/queues around. I don't know too much about Pulsar - currently evaluating it - but it's supposed to have the same or better throughput while allowing for tons of queues. Stay tuned - I …
Kafka is faster and more scalable, also "free" as opensource (albeit we deploy using a commercial distribution). Infrastructure tends to be cheaper. On the other hand, projects must adapt to Kafka APIs that sometimes change and BAU increases until a major 1.x version comes out …
IBM webMethods lags in terms of iPass offering as compared to mulesoft and dell Boomi. In terms of api management Apigee is much better than webMethods.
IBM webMethods is far superior when compared to competition like Microsoft Azure. In functionality and capabilities. Better visibility, uptime, efficiency, etc.
webMethods does better for end to end integrations whereas Azure is more focused on APIs. We selected webMethods because we felt their integrations, especially to mainframe environments, were much better than the others. IBM has been in this space for a long time and know it …
Oracle Integration Cloud vs IBM webMethods, hands down IBM webMethods is way better. Lots of features and ease of doing webMethods and super stable stack.
webMethods.io IntegrationDescriptionWe uses webMethods.io Integration to solve some of our application to applications and business to business integration needs. It is the Integration Platform as a Service solution that we use in a mix with our continued use of webMethods …
IBM webMethods was selected vs WSo2 for the reachness of the correspondign features regarding Mulesoft, the flexibility of IBM webMethods compared to the other platform, in relation to the existing architectural landscape was preferred
The two branches of the IBM integration portfolio are coming together nicely, and it's more a question of fully mapping the specific implementation requirements to the respective platform of choice when selecting between IBM Cloud Pak for Integration and IBM webMethods. …
I have seen that other products have different UI to address various business problems like messaging, EDA, MFT. IBM webMethods has unified platform and can also support hybrid Integration. I think it is all rounded iPaaS tool with ease of development and ease of operation. The …
The webMethods platform ranks higher in our ranking because it has more features and has proven itself in many CAC 40 companies in particular.The key elements are robustness, ease of use, a clear business model, and a wide range of features covering all types of enterprise …
IBM webMethods combines all the essential functions for information system integration in its platform. All products are interconnected to facilitate implementation (ESB, API, BPM, message bus). All tools are easy to use.
For brokering messages, Confluent Kafka is well suited since it offers a managed solution ready to use. Scenarios where the solution is not very well suited are for example, where pricing is an issue. The solution costs quite a lot for basic usage (for example: for 3 clusters, pricing is above 100k$ a year).
With its world-class proven technology, the IBM webMethods products are best suited for solving integration needs, including application connectivity, data aggregation, and data synchronization. The business use cases expand across Healthcare, Aviation, Banking, Insurance, etc., and many other mission-critical systems. Though I feel there is room for improvement where webMethods products are utilized in a containerized platform, especially with the API Management suite of products.
Apache Kafka is able to handle a large number of I/Os (writes) using 3-4 cheap servers.
It scales very well over large workloads and can handle extreme-scale deployments (eg. Linkedin with 300 billion user events each day).
The same Kafka setup can be used as a messaging bus, storage system or a log aggregator making it easy to maintain as one system feeding multiple applications.
The Kafka Tool is a community-made Java application that looks and feels from the past century.
Logging can be confusing. This certainly shows when we have to do troubleshooting.
Hybrid scenarios - pub/sub, but there are services in and outside a Kubernetes cluster. Then there are a ~3 options, but only 2 (the harder ones) are production-safe.
Kafka has suited our use case very well so far. Going forward we are planning to expand our platform manifold so the load on Kafka and our reliance on Kafka is going to increase only.
The webMethods platform is a fantastic tool for modernizing information systems. It's easy to use and delivers rapid results.The platform is focused on innovation and is accelerating its improvement with the acquisition by IBM.
Apache Kafka is highly recommended to develop loosely coupled, real-time processing applications. Also, Apache Kafka provides property based configuration. Producer, Consumer and broker contain their own separate property file
The webMethods product has a very user-friendly and easy-to-use interface.A weak point is the My webMethods Server portal (administration and monitoring portal for the on-premise platform). This weakness has been addressed thanks to the control plane on the hybrid version of the product. This version should be highlighted and used to ensure a very fluid and functional interface.
The webMethods platform is very stable and does not cause incidents: if it is well configured and tailored at the base. Infrastructure incidents represent 20% of incidents (full disk, memory peaks, etc.) 80% of incidents come from the implementation of the code in the platform. If a code is not optimized and a high volume is observed in production, this can cause incidents. Similarly, if all error cases or conditions are not handled in the code, this can cause errors. Finally, there can be common errors if the applications connected to the platform do not return quality data or are unavailable.
The webMethods platform is designed to handle a high volume of small messages. It's a tool for continuous processing.The incidents I've seen involving application performance declines are caused by: - Code optimization issues - File size issues or fragmentation of the transmitted file - Misuse of the platform (batch processing) - Monitoring data was not purged, and the user was working with millions of data points
Support for Apache Kafka (if willing to pay) is available from Confluent that includes the same time that created Kafka at Linkedin so they know this software in and out. Moreover, Apache Kafka is well known and best practices documents and deployment scenarios are easily available for download. For example, from eBay, Linkedin, Uber, and NYTimes.
In the majority of the tickets I've created, support has been very responsive and provided the right solutions or solutions.Resolving a ticket also depends on the information provided by the creator. It's important to provide the technical context and information about the environment, as well as information to help the support team reproduce the incident.
We received in-person training from the webMethods team. We received standard training from the vendor and custom training on specific security topics.The training sessions went well but remained very standard and did not adapt to the client's specific business. In-person training is more suitable for rapid skill development. It is necessary to practice for a few weeks to ensure familiarity with the tool.
I found clear and easy-to-follow training with realistic use cases for quick understanding and a 360° view of the features. The lesson format allows you to progress and learn by breaking down the allocated time.The technical courses are described step by step, allowing you to quickly get to grips with the products
When implementing webMethods, it's essential to have the right support and guidance.It's important to map out the interactions, document them, prepare test cases, and implement them while making maximum use of the product's native features.Additional tools must also be planned to automate deployments, visualize logs, and monitor the platform.
Apache Kafka is built for scale. From high throughput and real-time data streaming, it has a strong advantage over RabbitMQ with its low latency. This put Apache Kafka at the forefront as the platform of choice for large datasets messaging and ensuring scalability when data scale up tremendously. RabbitMQ however has its strengths in traditional messaging. Routing and message delivery reliability are the bedrock of RabbitMQ and this is where RabbitMQ excels. In my previous workplace, RabbitMQ was of choice as reliability matters more than scale. In two words. Apache Kafka for scale, RabbitMQ for reliability. And for cloud deployment and large dataset messaging in what I am doing now, Apache Kafka is the default choice.
The two branches of the IBM integration portfolio are coming together nicely, and it's more a question of fully mapping the specific implementation requirements to the respective platform of choice when selecting between IBM Cloud Pak for Integration and IBM webMethods. Pricing and licensing model, cloud strategy, and level of distributed responsibilities and ownership of integration in the organisation can be deciding factors.
Positive: bursts of traffic on special holidays are easy to handle because Kafka can absorb and buffer all the messages we need to process long enough to let an understaffed set of back-end services catch up on processing. Hard to put a number to it but we probably save $5k a month having fewer machines running.
Positive: makes decoupling the web and API services from the deeper back-end services easier by providing topics as an interface. This allowed us to split up our teams and have them develop independently of each other, speeding up software development.
Negative: our engineers have made mistakes such as accidentally dropping a few thousand messages due to the CLI being confusing to use, and as a result a customer lost some of their precious data. I'd say that was more our fault than Kafka's though.
webMethods.io Integration is a cost effective approach to integration in isolation
webMethods.io Integration as a supplement to on-premises integration is pointless and redundant and just adds complexity to the environment and additional costs
webMethods.io Integration is a tough sell for organizations using Microsoft Azure integration products such as Logic Apps
webMethods.io Integration has a faster time to market where the use case means standard provided adapters can be used
