Apache Kafka vs. Yellowfin

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.

We can recommend this software without hesitation, as it has the ability to generate reports and data analysis, as well as process the data automatically, which saves time. It is ideal for small and medium-sized companies that wish to improve their services in the business world

Incentivized

• 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).

Incentivized

• Yellowfin’s Storyboard, Timeline and Collaborative tools make it easy to share data and insights across an organisation.
• Strong Operational and Executive Dashboards that reflect the business KPIs and can send out prompts to people within the business based on rules.
• Yellowfin makes it incredibly easy to create dynamic reports and dashboards which incorporate maps through locational intelligence functionality.
• Yellowfin is a single license and all access is via a web browser making it perfect for both on-premise and cloud deployment. Mobile apps are also available.
• User roles and source filtering allows Admin/IT to offer different functions/views by user group/role.
• Mobile BI applications to support any device ..
• API connectors ( YF 7.2 on the way in Mar16 ) for a range of third-party Web-based applications, data sources, Enterprises CRM/ERP apps & open data platform apps, including Salesforce, Google Analytics, Twitter, Youtube , Zoho CRM, XE.com & more coming to make endusers to integrate their applications for better benefits
• Yellowfin Integration with Predictive analytical and Big Data processing tools like R, UDF, Spark to build rich applications for end users

• 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 ability to predefine a colour for a specific graph as a selection option dynamically.
• The ability to customise the label of a chart, especially with respect to performance related reporting graphs we need an increase in dynamic charting options to meet our more specialised requirements.

Kafka is quickly becoming core product of the organization, indeed it is replacing older messaging systems. No better alternatives found yet

Incentivized

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

Incentivized

As mentioned previously, if a newer company is looking for a strong, polished BI tool, Yellowfin would be a great software vendor to consider. Unfortunately, the high cost might be a barrier to these newer companies who dont have any legacy software/agreements such as an EA agreement with Microsoft.

Incentivized

99.9% availability

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.

Incentivized

They have good support in what they provide. But what they provide is not what is promised. I would stay with them if I can. Be very careful before signing up with them. They are not what they seem in the sales pitches. Be very careful and make sure you test and test before you sign up.

Yellowfin provides 3 levels of training to support consumers, business analytics, developers and Administrations :

1. Yellowfin BI Training - Level 1 (Exploration)
2. Yellowfin BI Training - Level 2 (Advanced)
3. Yellowfin BI Training - Level 3(Expert)

Web-based training for large audiences for Level 1 and Level 2 I would suggest

Design the data model and views for each dashboard is very important and it is the success factor Configuration of cluster for large implementation

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.

Incentivized

Yellow Fin platform is browser agnostic and is accessible from desktops as well as mobile devices. It is very simple and fast, it is integrated with Big Data, clients do not require a great training. It just works. But it’s easier to have things on the cloud, especially if you need to use different computers. Yellow Fin lacks the cloud feature.

Incentivized

- Tested 500 con-current users in Linux platform with 4 cores systems - Tested with cloud based VM - Tested with 1000 reads per minutes

• 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.

Incentivized

• made our org more data centric
• Decisions are made based on data vs opinion
• has created a single source of truth for the bulk of the organization

Incentivized