Pentaho is a suite of open source business intelligence and analytics products, now offered and supported by Hitachi Data Systems since the June 2015 acquisition.
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StreamSets
Score 8.4 out of 10
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StreamSets in San Francisco offers their DataOps Platform, a subscription based streaming analytics platform including StreamSets Data Collector data source management, Control Hub for data movement architecture management, StreamSets Data Collector Edge IoT manager, DataFlow Performance Manager (DPM), and StreamSets Data Protector compliance (e.g. GDPR) compliance module.
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.
Majorly for all Batch and Streaming Scenarios we are designing StreamSets pipelines, few best suited and tried out use cases below : 1. JDBC to ADLS data transfer based on source refresh frequency. 2. Kafka to GCS. 3. Kafka to Azure Event. 4. Hub HDFS to ADLS data transfer. 5. Schema generation to generate Avro. The easy to design Canvas, Scheduling Jobs, Fragment creation and utilization, an inbuilt wide range of Stage availability makes it an even more favorable tool for me to design data engineering pipelines.
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.
Monitoring/Visualization can be improvised and enhanced a lot (e.g. to monitor a Job to see what happened 7 days back with data transfer).
The logging mechanism can be simplified (Logs can be filtered with "ERROR", "DEBUG", "ALL" etc but still takes some time to get familiar for understanding).
Auto Scalability for heavy load transfer (Taking much time for >5 million record transfer from JDBC to ADLS destination in Avro file transfer).
There should be a concept of creating Global variables which is missing.
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
The Pentaho tools are designed so you can start playing around on your own. Of course, you will need guidance at some point, but the training teams are good at guiding new users, and the online documentation is usually pretty up-to-date.
Some of the tools, such as the Pentaho Data Integration tool and the Pentaho Server, are pretty self-explanatory. The other tools maybe are not so quickly and obvious to use, but again, with some documentation and some customer support, you can find your way around them.
They were responsive to our questions when we raised issues. They gave us workarounds when required. They were quite knowledgeable when it came to issue analysis and providing fixes. They were forthright in informing us if a bug was not due for release soon.
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.
Since the Pentaho platform offers a range of broad functionality across data preparation and advanced analytics, it also can be easily integrated to support many data sources and machine-learning frameworks. Based on that fact, we selected Pentaho to be used in our internal department. It also supports many of our BI use cases as required by company management or the business user. Last but not least, the Pentaho license is cheaper than their competitor.
StreamSets is a one-stop solution to design Data engineering Pipelines and doesn't require deep Programming knowledge, It's so user-friendly that anyone in Team can contribute to the Idea of pipeline design. In Hadoop One has to be programming proficient to use its various components like Hive, HDFS, Kafka, etc but in StreamSets all these stages are built-in and ready to use with minor configuration.
