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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Qlik Cloud Analytics (Qlik Sense)
Score 8.3 out of 10
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Qlik Sense® is a self-service BI platform for data discovery and visualization. It supports a full range of analytics use cases—data governance, pixel-perfect reporting, and collaboration. Its Associative Engine indexes and connects relationships between data points for creating actionable insights.
$200
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Apache Kafka
Qlik Cloud Analytics (Qlik Sense)
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$200
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$825
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Premium
$2750
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Apache Kafka
Qlik Cloud Analytics (Qlik Sense)
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Apache Kafka
Qlik Cloud Analytics (Qlik Sense)
Features
Apache Kafka
Qlik Cloud Analytics (Qlik Sense)
BI Standard Reporting
Comparison of BI Standard Reporting features of Product A and Product B
Apache Kafka
-
Ratings
Qlik Cloud Analytics (Qlik Sense)
7.9
315 Ratings
4% below category average
Pixel Perfect reports
00 Ratings
8.3216 Ratings
Customizable dashboards
00 Ratings
8.0313 Ratings
Report Formatting Templates
00 Ratings
7.4228 Ratings
Ad-hoc Reporting
Comparison of Ad-hoc Reporting features of Product A and Product B
Apache Kafka
-
Ratings
Qlik Cloud Analytics (Qlik Sense)
7.0
326 Ratings
14% below category average
Drill-down analysis
00 Ratings
8.0322 Ratings
Formatting capabilities
00 Ratings
6.0315 Ratings
Integration with R or other statistical packages
00 Ratings
6.0154 Ratings
Report sharing and collaboration
00 Ratings
7.9299 Ratings
Report Output and Scheduling
Comparison of Report Output and Scheduling features of Product A and Product B
Apache Kafka
-
Ratings
Qlik Cloud Analytics (Qlik Sense)
7.8
278 Ratings
5% below category average
Publish to Web
00 Ratings
6.3210 Ratings
Publish to PDF
00 Ratings
7.3257 Ratings
Report Versioning
00 Ratings
8.0176 Ratings
Report Delivery Scheduling
00 Ratings
8.9183 Ratings
Delivery to Remote Servers
00 Ratings
8.4108 Ratings
Data Discovery and Visualization
Comparison of Data Discovery and Visualization features of Product A and Product B
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.
Qlik Sense is a program whose purpose is to greatly improve all your operations and use of all data in an organic way. The mission will always be to increase the economic and commercial processes of the company in a short time. I recommended it for its high technology, which was Created for this area, the results are successful. We have noticed how it has increased relationships with our clients thanks to the credibility and security that we provide.
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
Qlik Sense is a constantly improving it's software and working with its' users to make it better. They are great at keeping their users informed of progress and care about delivering a quality product
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
Qlik Sense has a better and easy to learn user interface compared with other analytics tool which always help us to create regular and adhoc reports within the stipulated time frame and can be easily refreshed at a scheduled time and sent to multiple stakeholders for timely update regarding the Key metrics indicator.
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.
Qlik is great for companies with lots of business domains and departments because it scales well, especially if data that is reported is saved in SQL and similar structures. Its ease of use and good UI enables business units to create and manage their own reports. That removes a great burden of creating and managing/modifying these pages from the IT team. Overall, it's a win-win for both IT and business units.
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 customization of the platform opens up plenty of other options depending on the use cases. The API layer is incredibly rich and makes integration of Qlik based visualization into web pages a simple and effective pattern. It's been very easy to use with a great community made up of professionals. Qlik Sense has introduces artificial Intelligence into my data visualization and reporting activity.
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.