Apache Kafka vs. Pentaho

For brokering messages, Confluent Kafka is well suited since it offers a managed solution ready to use. Scenarios where the solution is not very well suited are for example, where pricing is an issue. The solution costs quite a lot for basic usage (for example: for 3 clusters, pricing is above 100k$ a year).

Incentivized

Pentaho is very well suited to perform data extraction & data mining from various cloud storage & transform that data using various available data models. However, the software struggles when it comes to visualizing the extracted data in an appealing manner & can be difficult for end-users to get an understanding of data tables created using those models.

Incentivized

• Apache Kafka is able to handle a large number of I/Os (writes) using 3-4 cheap servers.
• It scales very well over large workloads and can handle extreme-scale deployments (eg. Linkedin with 300 billion user events each day).
• The same Kafka setup can be used as a messaging bus, storage system or a log aggregator making it easy to maintain as one system feeding multiple applications.

Incentivized

• Integrate and synchronize with big data easily
• Import data from any sources and different databases
• Managing data in on-premise, hybrid and cloud environments.
• Compatibility and flexibility of the platform with any type of scenario and any business or industry
• Various tools in the software suite to transformation of data
• Simple interface appearance and creative UI graphics

• The Kafka Tool is a community-made Java application that looks and feels from the past century.
• Logging can be confusing. This certainly shows when we have to do troubleshooting.
• Hybrid scenarios - pub/sub, but there are services in and outside a Kubernetes cluster. Then there are a ~3 options, but only 2 (the harder ones) are production-safe.

Incentivized

• I think the relative obscurity of the tool is a downside, not as many developers, consultants or peers you can tap into.
• Lack of a solid user community held us back, looking at Power BI and Qlik, they have huge user communities that help each other out. Would have liked that here.
• Smaller company means smaller sales force, and the lack of a local presence made it hard to only interact online with the account rep. Other companies have someone local who often stops by with pre-sales developers to just pitch in free of charge when they have time.

Incentivized

Kafka has suited our use case very well so far. Going forward we are planning to expand our platform manifold so the load on Kafka and our reliance on Kafka is going to increase only.

I will use Pentaho until I find a better tool with a better, easier to use report designer client. For now, Pentaho has been the most powerful reporting tool for our clients because of its ability to connect to Odoo, integrate in Odoo (reports are accessible in Odoo) and the flexibility in report design and parameter integration

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

Even if Pentaho requires less technical skills to develop a pipeline or ETL project, its learning curve can be a bit slow since there are many ways to do the same thing as in any other platform. However, in Pentaho, some things can be confusing some moments for non-technical teams.

Incentivized

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

We are an Enterprise customer. They handle problems INSTANTLY when they are critical, including initiation an immediate WebEx screen share call when needed. Smaller/less-critical problems are handled within 1-2 days -- and NEVER fall off their radar, no matter how small. As needed, we can also leverage "professional services" from them -- much of which is included in our Enterprise contract. Finally, when a problem I have discovered turns out to be a bug..they create a JIRA for the fix, and make me a watcher. I love seeing notes come in showing me status updates of bugs filed because of something I found. They really are TOP-NOTCH.

Course Taken: DI1000 Pentaho Data Integration Fundamentals Setup A week before your class started, the instructor will start sending out class material and lab setup instructions. This is helpful so that you understand how the environment is laid out and can start reviewing the content. Ultimately it saved about a 1/2 day trying to setup with 10 other people online which was great! The Course The 3-day course was laid out like many other technical classes with 15-30 minutes instruction and 15-60 minutes of lab exercises. The instructor was very knowledgeable with the functionality from version to version and answered questions as we went along. I was amazed at some of the functionality that was available that I was not using at the time and quickly implemented changes to many existing transformations and jobs. The novice users seemed to catch on quickly and more experienced users explained how some of the functionality was used in their home environments. Towards the end there was enough time so that we were able to ask very directed questions about our own environments. Overall, I really found the class to be informative and deliver enough information to be dangerous. My skills improved and I was able to design better and efficient transformations for the HIE. Course Description: https://training.pentaho.com/instructor-led-training/pentaho-data-integration-fundamentals-di1000

Get the right people in before starting implementation. Start small and build as you go approach is time consuming and involves lot of rework. Evangalize within the organization the capabilities and limitations equally so that correct delivery expectations are set. Set expectations with the Customer that the tool cannot replace proprietary software in terms of stability/usability and that timelines could change given the new ness of the product.

Incentivized

Apache Kafka is built for scale. From high throughput and real-time data streaming, it has a strong advantage over RabbitMQ with its low latency. This put Apache Kafka at the forefront as the platform of choice for large datasets messaging and ensuring scalability when data scale up tremendously. RabbitMQ however has its strengths in traditional messaging. Routing and message delivery reliability are the bedrock of RabbitMQ and this is where RabbitMQ excels. In my previous workplace, RabbitMQ was of choice as reliability matters more than scale. In two words. Apache Kafka for scale, RabbitMQ for reliability. And for cloud deployment and large dataset messaging in what I am doing now, Apache Kafka is the default choice.

Perhaps Snowflake and SalesForce have some components which align with the Pentaho tools. The Pentaho tools have integrations with these technologies to add more value to the final users. Perhaps the only weakness I can honestly find in the Pentaho tools right now is the lack of a powerful web interface for data transformations. There is a web component from which you can access existing data transformations created with the Pentaho Data Integration tool. Still, the web component only allows visualization of the data transformation and remote execution. A complete web interface with remote execution would be excellent, and I'm sure that we might see something like this available at some point in the future.

Incentivized

• Positive: bursts of traffic on special holidays are easy to handle because Kafka can absorb and buffer all the messages we need to process long enough to let an understaffed set of back-end services catch up on processing. Hard to put a number to it but we probably save $5k a month having fewer machines running.
• Positive: makes decoupling the web and API services from the deeper back-end services easier by providing topics as an interface. This allowed us to split up our teams and have them develop independently of each other, speeding up software development.
• Negative: our engineers have made mistakes such as accidentally dropping a few thousand messages due to the CLI being confusing to use, and as a result a customer lost some of their precious data. I'd say that was more our fault than Kafka's though.

Incentivized

• Pentaho has improved our overall business process.
• Pentaho has helped the Managers and Directors to analyze the numbers going up and down from time to time.
• We have a started a big project using Pentaho that is going to include all the business processes in the organization.

Incentivized