Informatica PowerCenter (legacy) vs. RapidMiner

1.- Scenaries with poor sources of data is not recomended (Very bad ROI). The solution is for medium-big enterprises with a lot of sources of data and users. 2.- Bank and finance enviroment to integrate differente data form trading, Regulatory reports, decisions makers, fraud and financial crimes because in this kind of scenary the quality of data is the base of the business. 3.- Departments of development and test of applications in enterprises because you can design enviroments, out of the production systems, to development and test the new API's or updateds made.

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RapidMiner is really fantastic to perform fast ETL processes and work on your data as you want, no matter what is the source. You will really save a lot of time when you learn how to use it. You can create mining analysis with several algorithms, and thanks to add-ons, you can apply a lot of techniques. It will not replace a business intelligence dashboard but it allows to create great datamarts for your BI tools. One negative thing is that It's no easy to share your outputs.

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• Informatica Powercenter is an innovative software that works with ETL-type data integration. Connectivity to almost all the database systems.
• Great documentation and customer support.
• It has a various solution to address data quality issues. data masking, data virtualization. It has various supporting tools or MDM, IDQ, Analyst, BigData which can be used to analyze data and correct it.

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• I am very impressed at how easily you can work within RapidMiner without much data analytics training. Plus with the help of the crowd, you can see what steps others have taken with their data analytics projects.
• Text mining was simple and clean. We used this for our call transcription problem where we didn't have the resources to listen to each call. We needed to qualify each call based on some key phrases.
• Our direct mail program was large and not very targeted. Using RapidMiner, we were able to isolate a predictive level we felt comfortable with and decided not to send to anyone below that level. We saved quite a bit of money.

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• There are too many ways to perform the same or similar functions which in turn makes it challenging to trace what a workflow is doing and at which point (ex. sessions can be designed as static or re-usable and the override can occur at the session or workflow, or both which can be counter productive and confusing when troubleshooting).
• The power in structured design is a double edged sword. Simple tasks for a POC can become cumbersome. Ex. if you want to move some data to test a process, you first have to create your sources by importing them which means an ODBC connection or similar will need to be configured, you in turn have to develop your targets and all of the essential building blocks before being able to begin actual development. While I am on sources and targets, I think of a table definition as just that and find it counter intuitive to have to design a table as both a source and target and manage them as different objects. It would be more intuitive to have a table definition and its source/target properties defined by where you drag and drop it in the mapping.
• There are no checkpoints or data viewer type functions without designing an entire mapping and workflow. If you would like to simply run a job up to a point and check the throughput, an entire mapping needs to be completed and you would workaround this by creating a flat file target.

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• I hope RapidMiner would be the first data science platform that allows data scientists to change the behaviour of a machine learning algorithm that already exists in the repository. For example, I want to be able to change the way a genetic algorithm mutates.
• Automatic programming: One day, I hope RapidMiner can automatically generate codes in any 4th generation programming language based on the developed model.
• More tutorials/samples needed: Why doesn't RapidMiner becomes the next 'UC Irvine Machine Learning Repository'? Provide real examples and real cases for users to study and understand the best practices in modelling. RapidMiner already has some datasets for a tutorial. Besides the existing samples, I hope RapidMiner can provide more sample data and examples.

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Our team enjoys using Informatica and feels that it is one of the best ETL tools on the market.

Very fast and user-friendly tool

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Positives; - Multi User Development Environment - Speed of transformation - Seamless integration between other Informatica products. Negatives; - There should be less windows to maintain developers' focus while using. You probably need 2 big monitors when you start development with Informatica Power Center. - Oracle Analytical functions should be natively used. - E-LT support as well as ETL support.

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Very use to use and learn

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PowerCenter is robust and fast, and it does a great job meeting all the needs, not just the most commercially vocal needs. In the hands of an expert power user, you can accomplish almost anything with your data. It is not for new users or intermittent users-- for that the Cloud version is a better fit. Be prepared for costly connectors (priced differently for each source or destination you are working with), and just be planful of your projects so you are not paying for connectors you no longer need or want

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Informatica power center is a leader of the pack of ETL tools and has some great abilities that make it stand out from other ETL tools. It has been a great partner to its clients over a long time so it's definitely dependable. With all the great things about Informatica, it has a bit of tech burden that should be addressed to make it more nimble, reduce the learning curve for new developers, provide better connectivity with visualization tools.

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While Talend offers a much more comfortable interface to work with, Informatica's forte is performance. And on that front, Informatica Enterprise Data Integration certainly leaves Talend in the dust. For a more back-end-centric use case, Informatica is certainly the ETL tool of choice. On the other hand, if business users would be using the tool, then Talend would be the preferred tool.

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We tried different data tools and we figured we give RapidMinder Studio a shot as one of our employees had experience with it, and when compared to some of the other tools that we used it was the best fit among the test group that we used. Overall it was a little more fluid and user-friendly.

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• The data pipeline automation capability of Informatica means that few resources are needed to pre-process the data that ultimately resides in a Data Warehouse. Once a workflow is implemented, manual intervention is not needed.
• PowerCenter did require more resources and time for installation and configuration than was expected/planned for.
• The lack of or minimal support of unstructured data means that newer sources of dynamic/changing data cannot be easily processed/transformed through PowerCenter workflows.

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• Thanks to the patters that RapidMiner has detected, we have been able to follow clues in the right direction, both for the Protein Interaction Network Analysis and for the Epilepsy Research
• Students and participants of the machine learning workshops have learned about this technology and about the tool

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Independently Invited by TrustRadius

A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info