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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Tableau Public
Score 9.8 out of 10
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Tableau Public is a free edition of the Desktop product. With this edition, data can only be published to the Tableau public website and does not allow work to be saved or exported locally.
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Apache Kafka
Tableau Public
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Apache Kafka
Tableau Public
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Apache Kafka
Tableau Public
Features
Apache Kafka
Tableau Public
BI Standard Reporting
Comparison of BI Standard Reporting features of Product A and Product B
Apache Kafka
-
Ratings
Tableau Public
9.8
12 Ratings
19% above category average
Pixel Perfect reports
00 Ratings
9.710 Ratings
Customizable dashboards
00 Ratings
10.012 Ratings
Report Formatting Templates
00 Ratings
9.712 Ratings
Ad-hoc Reporting
Comparison of Ad-hoc Reporting features of Product A and Product B
Apache Kafka
-
Ratings
Tableau Public
9.7
12 Ratings
22% above category average
Drill-down analysis
00 Ratings
9.812 Ratings
Formatting capabilities
00 Ratings
9.712 Ratings
Integration with R or other statistical packages
00 Ratings
9.59 Ratings
Report sharing and collaboration
00 Ratings
9.811 Ratings
Report Output and Scheduling
Comparison of Report Output and Scheduling features of Product A and Product B
Apache Kafka
-
Ratings
Tableau Public
9.5
11 Ratings
15% above category average
Publish to Web
00 Ratings
10.011 Ratings
Publish to PDF
00 Ratings
10.09 Ratings
Report Versioning
00 Ratings
9.89 Ratings
Report Delivery Scheduling
00 Ratings
9.69 Ratings
Delivery to Remote Servers
00 Ratings
8.17 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.
Tableau public is the best platform to build dashboards for your personal profile and share with recruiters. It's always good to keep ourselves updated on the latest features, create sample dashboards and save them to a personal profile. Tableau public is free and doesn't need any subscription. anyone can create an account and start building reports.
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).
Data visualization: lots of different options, including bar, scatter, pie, waterfall charts to explore relationships between variables, and to present findings/trends to different teams
Integrates readily with limited, though different data sources: TXT, CSV, TDE, Access
Exports reports for review of different dashboards: client-ready/team-ready, with a clean and tidy presentation in PDF format (or hardcopy)
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
Tableau Public (both Desktop and Server) like their "for a fee" counterparts offer very easy to learn and use tools to transform data into pictures and gain insights into your data. Most organizations report a reduction in development time of 10x vs. other similar tools, due to the intuitive user interface. That said, with Tableau Public, published workbooks are "disconnected" from the underlying data sources and require periodic updates when the data changes. Users are limited to 1 Gb of storage space per user ID and password as well.
I would like to see better options for public sharing of visualizations and data from within the "for a fee" products as more and more organizations are moving in the direction of data sharing with partners and their communities.
It's free, right? I'll keep using the free version. So the real question to ask is this? Will I pay $999 for the Personal version or $1,999 for the Professional? Yikes! That is a big stretch. I'm not sure about that. The product comparison chart is at: http://www.tableausoftware.com/public/comparison
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
Tableau public is a great training tool to understand the basics of Tableau before buying it. A great tool to extend Excel's visualization and to publish data for others. Not useful for anything you need secure. No ability to access databases. Static information only.
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.
Start at the end and work backward. Identify the business case / issue and questions the end users have, then identify the data needed, and where to get it.
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.
Google Charts/Drive is sufficient for simpler data sets, but it does not integrate with other web platforms and the visualization does not look as professional. I'm not aware of any other competitors that offer the same package as Microsoft.
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.