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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Microsoft Power BI
Score 8.5 out of 10
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Microsoft Power BI is a visualization and data discovery tool from Microsoft. It allows users to convert data into visuals and graphics, visually explore and analyze data, collaborate on interactive dashboards and reports, and scale across their organization with built-in governance and security.
$10
per month per user
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Apache Kafka
Microsoft Power BI
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Apache Kafka
Microsoft Power BI
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Apache Kafka
Microsoft Power BI
Features
Apache Kafka
Microsoft Power BI
BI Standard Reporting
Comparison of BI Standard Reporting features of Product A and Product B
Apache Kafka
-
Ratings
Microsoft Power BI
8.4
193 Ratings
3% above category average
Pixel Perfect reports
00 Ratings
8.3164 Ratings
Customizable dashboards
00 Ratings
8.8192 Ratings
Report Formatting Templates
00 Ratings
8.0175 Ratings
Ad-hoc Reporting
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Apache Kafka
-
Ratings
Microsoft Power BI
7.9
191 Ratings
2% below category average
Drill-down analysis
00 Ratings
8.2188 Ratings
Formatting capabilities
00 Ratings
7.7188 Ratings
Integration with R or other statistical packages
00 Ratings
7.3140 Ratings
Report sharing and collaboration
00 Ratings
8.5186 Ratings
Report Output and Scheduling
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Apache Kafka
-
Ratings
Microsoft Power BI
8.1
184 Ratings
2% below category average
Publish to Web
00 Ratings
8.3174 Ratings
Publish to PDF
00 Ratings
8.2169 Ratings
Report Versioning
00 Ratings
7.7141 Ratings
Report Delivery Scheduling
00 Ratings
8.3144 Ratings
Delivery to Remote Servers
00 Ratings
7.9107 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.
Has significantly improved collation of data and visualisation especially with business across Europe. Has given me the ability to see the Site availability at the click of a button to see which Site is in the "money" and seize opportunities based on Market data
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).
Options for data source connections are immense. Not just which sources, but your options for *how* the data is brought in.
Constant updates (this is both good and bad at times).
User friendliness. I can get the data connections set up and draft some quick visuals, then release to the target audience and let them expand on it how they want to.
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
Microsoft Power BI is an excellent and scalable tool. It has a learning curve, but once you get past that, the sky is the limit and you can build from the most simple to the most complex dashboards. I have built everything from simple reports with only a few data points to complex reports with many pages and advanced filtering.
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
Automating reporting has reduced manual data processing by 50-70%, freeing up analysts for higher-value tasks. A finance team that previously spent 20+ hours per week on Excel-based reports now does it in minutes with Microsoft Power BI's automated Real-time dashboards have shortened decision cycles by 30-40%, enabling leadership to react quickly to sales trends, operational bottlenecks, and customer behavior.
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.
It is a fantastic tool, you can do almost everything related with data and reports, it is a perfect substitutive of Power Point and Excel with a high evolution and flexibility, and also it is very friendly and easy to share. I think all companies should have Power BI (or other BI tool) in their software package and if they are in the MS Suite, for sure Power BI should be the one due to all the benefits of the MS ecosystem.
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.
Microsoft Power BI is free. If I didn't want to create a custom platform (i.e. my organization insisted on an existing platform that I *had* to use), I'd use Microsoft Power BI. For any start-up or SMB, I'd just use Claude & Grok to build it quickly, also for free. Would not pay for Tableau or Sigma anymore. Not worth it at all.
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.