I am, for the most part, a do-it-yourself type of person. I fix my own car if I can; I even have four healthy tomato plants growing in pots outside as we speak — the plants will take that little extra CO2 out of the air and give me great tasting tomatoes (soon… i hope!)

But I digress.

Whether to use an ETL tool such as Kettle (aka Penatho Data Integration) for a project involving large data transfers is a typical “build vs. buy” type of decision, one that is fairly well understood and I don’t wish to repeat it all here — putting together some Perl scripts to do the job, you typically get great performance, development speed and accessibility. This would need to be balanced against the benefits of ETL tools and their potential drawbacks (development speed, license costs and performance …

Recently I have been asked by my company to make a case for open-source ETL-data integration tools as an alternative for the commercial data integration tool, Informatica PowerCenter.
So I did a lot of research and I'm going to try my best, considering I have never used the open-source tools nor the commercial one.

I found plenty of information about comparisons between Pentaho Kettle and Talend, which were 2 of the open-source tools I was supposed to research.
Now, without getting in a big arguement (or matt casters posting on my blog), I'd like to attempt to compare the two, very briefly.
And again, this is ONLY from the research I did online and not based on my experience using the tools (since I dont really have any).


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Since we released our first Beta, we have been working on various examples to demonstrate the capabilities and benefits of a resource oriented approach to data integration.

One of the examples we have been working on is a data mart for SugarCRM opportunity analysis. We have now published that example on our content download site, packages.snaplogic.org , where you can download it, and try it out. (You will also need a SnapLogic server installation, to run the pipelines.)

The general idea behind data marts is simple – they are subject specific alternatives to a full blown data warehouse. The primary benefits of using a separate database to analyze an operational system are the ability to look at a snapshot of the constantly changing data, and the offloading of the queries to a separate database which is optimized for analysis using a star schema instead of a normalized …