The Airship Experience Platform provides an end-to-end solution for unifying experiences across channels and capturing value across the entire customer lifecycle.
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Apache Kafka
Score 8.6 out of 10
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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.
Well-Suited for: 1. Mobile App Notifications: Ideal for targeted push notifications in apps. 2. Customer Segmentation: Effective for personalized marketing campaigns based on user data. 3. Event-Triggered Automation: Great for automated messaging based on user actions. 4. A/B Testing: Useful for optimizing campaign messages and strategies. Less Appropriate for: 1. Non-Mobile Channels: Less effective if the primary focus is on non-mobile communications like email or direct mail. 2. Basic Email Marketing: Other platforms might be better suited for simple, broad email campaigns without complex segmentation or personalization needs.
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.
The marketing push notifications are very effective, and it gives us free hand to define different business criteria to target user groups
The user experience or the message content could differ from Android and iOS, and this is a huge benefit for us
As an Architect, troubleshooting an issue is very detailed and the time it takes to troubleshoot an issue is considerably less from our previous product
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).
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
The interface takes a bit getting used to in order to know how to take advantage of everything. Some of the analytics that are available are particularly hard to find, so it's important to pay attention when customer support reviews everything, but everything I'd want and need in terms of Push and In-App messaging is all there.
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
I have not had to interact much with customer support as I have been able to find the vast majority of the answers I'm looking for within their documentation, which I very much appreciate because it saves me a lot of time. Customer support has been responsive and helpful for the most part during the couple of interactions I've had.
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've tested a bunch of different CRM tools over the years and Airship has been a winner for its functionality, features, cost, and ability to integrate with other softwares that we use. It has been great for SMS and mobile in particular. It could certainly be a one stop shop for CRM.
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.
The ROI has increased more than approx. 50% (exact details to be confirmed) based on cross-channel orchestration
Using push notifications alone, we have seen a huge increase in app engagement which was a challenge before to nudge users to get back to the App after initial download
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.
