#PENTAHO DATA INTEGRATION NVL HOW TO#
#PENTAHO DATA INTEGRATION NVL UPDATE#
#PENTAHO DATA INTEGRATION NVL CODE#

For example, if your data should change state one year after insertion, you can set the date in your input data rows to 364, 365 and 366 days from your test date. I canвЂ™t think of any downside, so this is my recommended method.ДЄfter this setup is done, you can pivot your test data around the hardcoded test date. The benefit to this is that you arenвЂ™t necessarily making another database call and it is more understandable. This date can be pulled from a file (for test), or using the вЂGet System InfoвЂ™ step, or a database lookup (for production). The second is just to have a variable that is set previously in the job and is passed in to a transformation as a named parameter. The disadvantage is that youвЂ™re making another database call and mostly just for testing purposes. The benefit to this is that you can make this SQL call in your transformation, and everything stays tidily in there. For testing, the statement returns some known date: вЂ select str_to_date(вЂвЂІ,вЂ™%Y-%m-%dвЂ™)вЂ for mysql. For the production job, the statement simply pulls the current date from the database: вЂ select curdate()вЂ for mysql. The first is to have a SQL statement that is pulled in via a вЂGet VariablesвЂ™ step. I have handled the time dimension in one of two ways. Testing transformations that depend on the date will often be required when you only want to process new data, or if you want to treat events that happened in the past differently depending on how long ago they occurred. Testing time dependent kettle transformations If youвЂ™d like to get on a low traffic list and hear more of my thoughts about Pentaho Data Integration Development, sign up for the PDI development newsletter. And IвЂ™m bummed Matt Casters didnвЂ™t fill it out! I would have thought that there would be a higher percentage of new users, at least from the forums. Or maybe IвЂ™m just not aware of these resources.ДЄgain, it is interesting to me that most of the people (over 75%) who responded to my survey have over two years of PDI experience. It was interesting to me that so many folks were developers, but that the resources for testing PDI (and version control and other kinds of developer type tasks) are so sparse. The вЂotherвЂ™ category ranged from вЂњI do everythingвЂќ to вЂњData Warehouse ArchitectвЂќ. So these results are of mostly anecdotal interest. I received almost 20 responses, but I know that this is only a tiny portion of the PDI userbase (after all, in the last week, kettle 4.4.0 has been downloaded over 110,000 times since it was released last November).

#PENTAHO DATA INTEGRATION NVL HOW TO#

I marketed this on the pentaho forums and twitter (got a nice retweet from I wasnвЂ™t sure how to reach more of the PDI audience. I just wanted to post results from the PDI/Kettle user survey I ran a few weeks ago. HereвЂ™s a list of open source ETL tools to evaluate. When you are confronted with a data movement problem, take a look at an ETL tool like Kettle and see if you can stand on the shoulders of giants.

#PENTAHO DATA INTEGRATION NVL CODE#

Every line of code you write is another you have to maintain. I did.ДonвЂ™t re-invent the wheel! Your data movement problem may very well be a super special snowflake, but chances are it isnвЂ™t.

You have to roll your own testing framework.

Some components (at least for Kettle) are not open source.

Limits of components mean you sometimes have to contort your data flows, or drop down to write your own component.

Possible to version control, impossible to merge.

You can write your own components that leverage existing libraries.

There is support for other performance features like parallel jobs that you can configure.

You operate at a higher level of abstraction.

ItвЂ™s hard to over emphasize how much time this can save, allowing you to focus on business logic.

There are pre-built components for common data tasks (connecting to a database, parsing a flat file) that have been tested and debugged by many many people.

This is more true now than in the past because there is a lot more data flowing everywhere, and there are several viable open source ETL tools, so you donвЂ™t have to spend thousands or tens of thousands of dollars to get started. While ETL systems are not without their flaws, I think their strengths are such that everyone who is moving data around should consider them.

#PENTAHO DATA INTEGRATION NVL UPDATE#

When I was working for 8z, we used it heavily to pull data from other systems, process it, and update our databases. The one with which I am most familiar is Kettle, aka Pentaho Data Integration.

Pentaho data integration nvl