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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Anypoint Platform
Score 7.8 out of 10
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The Anypoint Platform developed by MuleSoft and acquired by Salesforce in early 2018 is designed to
connect apps, data, and devices anywhere, on-premises or in the cloud. This platform was built to
offer out-of-the-box connectors as well as tools that architects and developers can adopt quickly to
design, build and manage the entire lifecycle of their APIs, applications, and products.
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 …
AWS is a very good tool but still evolving and offers a small set of API management features. The main reason why we chose MuleSoft Anypoint Platform API Manager is because it is well suited with the APIs developed in Mule ESB.
Once we have moved all of our system integration APIs to the MuleSoft Anypoint Platform, we will need to communicate with a wide variety of external systems. All of our business and service logic is stored in the aforementioned core systems. Anypoint Platform (and all of our …
Overall a better experience in pricing negotiation with the Sales team. The breadth of services offered by Mulesoft was really important and the expertise offered by our consulting partner influenced the decision. The overall deployment was completed well within the timelines. …
Once we use [MuleSoft] Anypoint Platform to host all our system integration APIS, we need to exchange data with a lot of different resources. Those listed systems are our main systems that hold all business and logic behind our service. The integration from Anypoint Platform …
We selected Mulesoft for speed of implementation. We did not have the luxury of time and needed a solution that can be learned and implemented within weeks so we can take advantage of newly formed partnerships. The training that was provided to use during our pre-sales and …
We believe Anypoint Platform is best in breed and has features that match nearly any other iPaaS offering. It also allows for accomplishing much more challenging and complex integrations due to the ability to connect to nearly any system, use custom connectors, and embed …
The main problem with this software is the different connector type that is used for different protocols except for a different SAP connector. There is only one connector called SAP. It will be great if there will have a different connectors like idoc, RFC, proxy, etc.
Anypoint has tons of capabilities baked in one platform, and other competitors like Built.io or Workato may not have all those like Anypoint Exchange. However where these products shine are in usability, as it's ridiculously easy to build integrations using Built.io. I was …
Anypoint Platform is really very easy to adopt for application integration. Simple UI and easy data weave language allows user to focus on business portion only. The API management and deployment of application are hassle free. We have selected Anypoint platform because it is …
Dell Boomi is kind of similar in drag and drop implementation. However, it doesn't have as many connectors (shapes in Dell Boomi). Moreover, it also doesn't have deployment stuff on its own.
Informatica is way behind Dell Boomi and Anypoint Platform, but users are still using it …
Compared to Dell Boomi the API management suite of Mulesoft is much mature, however if its only about the integration , then i would always go for Dell Boomi, as there is no infrastructure setup that needs to be done from the developer's end.
The main reason why we chose Anypoint API Manager is because it is well suited with the APIs developed in Mule ESB. AWS, on the other hand, is a very good tool but still evolving and offers a small set of API management features.
API led architecture focus with a decent API ecosystem for developer collaboration, policy management and declarative implementation. Batch processing, visualization, Anypoint MQ and ObjectStore leave a lot to be explored. Multiple deployment models make it worth considering.
It enables customers to expose their APIs in a very simple and fast way. It can be used by non-expert developers. It is compliant with all major connection and data representation world standard.
We provide options to customers to choose from the best tools. Customers are the decision makers. Personally, because of open-source and large community base, MuleSoft is the repo in the market.
It is a powerful tool with smart intelligence. The drag and drop feature makes my work easy. The tool has great connecting support which is the best reason to use the tool on a daily basis. Powerful functionality with designing and implementing APIs. Hosted on public IP.
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).
MuleSoft Anypoint Platform is best tool in the market for developing APIs with complex structures communicating with various different types of applications including web applications as well as legacy applications. Also applications including database connectivity for fetching and updating data in the DB tables. I cant think of any scenario which MuleSoft Anypoint Platform could not be used for developing the integrations.
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.
Provides wide range of popular connectors like salesforce, netsuite, sftp and many more. A user can easily integrate with those services using specific connector.
Support API development using RAML and desing tool. The platform is very good in data type classification.
Cloud deployment and post deployment monitoring is easy. A user can configure application worker as per need. Alert and notifications are real time.
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.
Has more features than what we really need so we're paying for more than we use. Sort of like paying for an Abrams tank when all we really need is a Toyota Corolla.
Not a value product, tends to be expensive.
Takes a while for developers to learn to use Mulesoft Anypoint.
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.
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
MuleSoft Anypoint Platform is really very easy to adopt for application integration. Simple UI and easy data weave language allows user to focus on business portion only. The API management and deployment of application are hassle free.
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.
Anypoint Platform support is very responsive. There is also a huge knowledge base and an active online forum where answers to most questions can be found. When needed support engages the engineering group so adequate solutions or workarounds are always provided.
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.
Once we have moved all of our system integration APIs to the MuleSoft Anypoint Platform, we will need to communicate with a wide variety of external systems. All of our business and service logic is stored in the aforementioned core systems. Anypoint Platform (and all of our APIs) makes it easy to connect to various other platforms. In order to link to these many other systems, connectors and/or components are utilized, and they are simple to configure and integrate.
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.