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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Oracle SOA Suite
Score 8.0 out of 10
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The rapid adoption of cloud-based applications by the enterprise, combined with organizations’ desire to integrate applications with mobile technologies, is dramatically increasing application integration complexity. Oracle SOA Suite 12c, the latest version of the company's unified application integration and SOA solution, offers a simplified cloud, mobile, on-premises and Internet of Things (IoT) integration capabilities within a single 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 …
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 …
Oracle Database, Oracle Access Management, Oracle Business Activity Monitoring, Oracle Application Testing Suite, Oracle BPM Suite, Oracle BI Foundation Suite, Oracle Cloud Infrastructure (OCI), Oracle Data Integrator, Oracle Service Cloud, Oracle Customer Care and Billing and …
I used Service Bus while developing SOA Suite composites. It makes development easier. Calls to vendors' web services for instance are done easily. Each vendor has its own protocol and development platform. Without Oracle Service Bus we would have had to thought on how to reach …
When we wanted an enterprise service bus, the AquaLogic Service bus was our first choice and it had very stable features. It was later bought by Oracle and we have used it for several years. Snaplogic and Mule ESB are upcoming and very promising. Snaplogic is completely born in …
Mule ESB is an open source tool and would definitely cost less, however is not as sophisticated a product for the business functionality we need at US Cellular.. I have reviewed IBM WebSphere message broker, is very cumbersome and not very user friendly. Despite some of the …
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).
In comparison to Open source products like Apache Camel and Mule ESB, Oracle ESB is more robust and offers better enterprise capabilities. However, the licensing costs are fairly prohibitive and are preventing widespread product adoption. At our university, we had already purchased the Oracle Campus Solutions ERP suite and hence had little problems integrating their OSB as well.
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.
We have had not many issues with Oracle Service Bus and it's very stable for our requirements. It's highly available and helps us implement Tier1 applications on it.
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
It's an excellent enterprise service bus and has very stable features. We have been using it since 2008. We did hit into some issues. But, recreating the service helped fix many issues. Also, deployment to various environments was easy. Also, the plugin on Eclipse helps to build proxy and business services quick and easy.
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.
We had some issues with MQ connectivity through OSB and our experience was poor with the support team. They do respond. But, it felt like we are ignored and we had bad support. We had to escalate and things used to get dragged for weeks before we get more quality questions on how to pursue investigation.
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.
Mule ESB is an open source tool and would definitely cost less, however is not as sophisticated a product for the business functionality we need at US Cellular.. I have reviewed IBM WebSphere Message Broker, is very cumbersome and not very user friendly. Despite some of the license cost concerns, Oracle Service Bus stands out as an ideal Enterprise Service Bus solution at US Cellular
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.