What comes before data extraction? In any data intensive project be it a data warehouse, data migration, master data there is a data discovery phase which has to be completed before any movement of data can be performed.
big data’s little jobs
I happened across an interesting article by David Gee recently. It was entitled “A guide to the little jobs that will make big data work.” (http://www.itnews.com.au/blogentry/a-guide-to-the-little-jobs-that-will-make-big-data-work-409655)
Understanding data models in SAP BW is a challenge
Here is another in our occasional series of data models. This one is a little different, in that it is about SAP Business Warehouse (BW).
WHat exactly is a data lake?
Recently I have been attending some vendor webinars on Big Data, Data Lake strategy, Hadoop in the context of Data Warehousing and Analytics in an attempt to gain a clearer understanding both of the technology platforms available and more importantly some knowledge of the use cases and benefits a business might expect to accrue from them.
I have been reading quite a lot recently about the applicability of Agile methodologies for Data Warehousing amongst other initiatives and it appears to me that often the topic of source data analysis is under-represented in the literature and blogs so I thought I would discuss the subject of application metadata in an Agile data warehouse world.
“Garbage out, garbage in”
I know, I know I have got that the wrong way round. Well, actually I did it on purpose to illustrate a problem with BI/Data Warehousing projects and incidentally many other Information Management initiatives. That problem is the challenge of source data analysis – particularly with SAP, Oracle and Salesforce packages.
A few months ago I was at a meeting with a customer who was involved with a BI project that was pulling data from an SAP system into a Data Mart.
I realise this might be controversial but I do believe it. IT professionals have long talked about Process Modelling as the way to understand what a complex system does, while Data Modelling tends to be seen as the ‘Cinderella’ of the modelling world.