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.
N/A
Appian
Score 8.5 out of 10
N/A
Appian is a low-code development and business process management platform. It features drag-and-drop design for app building, automated work processes, unified data management, and cloud-based deployment.
$0
Pricing
Apache Kafka
Appian
Editions & Modules
No answers on this topic
Appian Community Edition
$0
Application - Input-Only
$2
per month per user
Application - Infrequent
$9
per month per user
Application - Standard
$75
per month per user
Platform
Custom Quote Priced per user with unlimited apps.
minimum 100 users, no maximum
Unlimited
Custom Quote Priced per development with unlimited apps.
unlimited
Platform
Custom Quote Priced per user with unlimited apps.
Minimum 100, no maximum
Unlimited
Custom Quote Priced per development with unlimited apps.
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 …
Apache Kafka is well-suited for most data-streaming use cases. Amazon Kinesis and Azure EventHubs, unless you have a specific use case where using those cloud PaAS for your data lakes, once set up well, Apache Kafka will take care of everything else in the background. Azure EventHubs, is good for cross-cloud use cases, and Amazon Kinesis - I have no real-world experience. But I believe it is the same.
Appian works great for automating manual processes and integrating multiple systems through its toolset. It gives great flexibility for establishing rules for approvals, routings, escalations, and the like. Because of the low code toolset, it's very easy to deploy and make changes as needed as processes evolve and as the organization learns to utilize the system better. Minimal maintenance is required to support the applications build on the platform. Some of the automated testing integration with tools like Jenkins is limited so that may be an issue for some.
Really easy to configure. I've used other message brokers such as RabbitMQ and compared to them, Kafka's configurations are very easy to understand and tweak.
Very scalable: easily configured to run on multiple nodes allowing for ease of parallelism (assuming your queues/topics don't have to be consumed in the exact same order the messages were delivered)
Not exactly a feature, but I trust Kafka will be around for at least another decade because active development has continued to be strong and there's a lot of financial backing from Confluent and LinkedIn, and probably many other companies who are using it (which, anecdotally, is many).
Allows at a glance workflow documentation which assists in the need we have for information readiation.
Drag and drop interface for workflow development greatly speeds our apps time to market.
Using the advanced features of Appian, we are able to create working sites in a fraction of the time it would take to do so using "traditional" development.
Sometimes it becomes difficult to monitor our Kafka deployments. We've been able to overcome it largely using AWS MSK, a managed service for Apache Kafka, but a separate monitoring dashboard would have been great.
Simplify the process for local deployment of Kafka and provide a user interface to get visibility into the different topics and the messages being processed.
Learning curve around creation of broker and topics could be simplified
Search issues when type ahead and database search are used in the same field.
Buttons implementation where user is require[d] to click on the button description - if clicks on the button outside that text - button will not work.
Problems with using certain off-the-shelf performance tools like WebLoad or Neoload. That is because of different dynamic variables being used internally in Appian - which these tools are unable to correlate. We are still investigating using other tools like Jmeter to overcome dynamic correlation problem for performance testing.
We recently renewed our license with Appian. We are convinced that its flexibility, relative ease of use, the support they provide, there mobile advancements and their general willingness and desire to see us succeed all contributed to our reason to renew our agreement with Appian
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
Appian is a low code environment, because of this, a very good visual interface is required. Appian is providing a feature-rich dashboard [that] we can use for building the dashboards and other interfaces. Appian also provides patches and releases to enhance these features. A developer can start off development just by going through a basic course from the Appian learning community.
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.
Appian is one of the leading low code business automation platforms that support RPA, decision rules, case management, workflow automation, and machine learning all in a single bundle. But it is also harder to implement and replace the traditional business process.
As analyst I participated in a developer boot camp. At times it was hard to keep up but most of the time it made sense. Trainer took the time to explain and slowed pace down to answer questions etc.
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 to other needs.
Appian has enormously transformed and keeps on updating the product every quarter to meet the latest needs of the world with new innovations & technologies being integrated within the platform. What gives more pleasure than a product that keeps on continuous[ly] improv[ing]?
Positive: Get a quick and reliable pub/sub model implemented - data across components flows easily.
Positive: it's scalable so we can develop small and scale for real-world scenarios
Negative: it's easy to get into a confusing situation if you are not experienced yet or something strange has happened (rare, but it does). Troubleshooting such situations can take time and effort.
I believe it has negatively impacted our release dates. There may have been a misunderstanding as to the learning curve, even though it is "low code."
The look and feel of the applications created using Appian have uniformity and it's easier to have "reuse" between applications.
There is less developer control when it comes to features. I think this mainly has to do with the amount of plugins available. I would think there should be many more available plugins. But again, our use case is probably different than most others.
