There are many ways to do data integration. Those include: Extract, transform and load (ETL) – which moves and transforms data (with some redundancy) from a source to a target. While ETL can be implemented (somewhat) in real time, it is usually executed at intervals (15 minutes, 30 minutes, 1
As a youngster in the 70s and 80s, Star Trek inspired my imagination and fostered a great love for science, technology and reading. (See the embedded Star Trek infographic for some interesting factoids – did you know that there were 28 crew member deaths by those wearing red shirts?) Captain Kirk and the
In the UK, technology trends move a little slower than for our US counterparts. It was about 5 years ago when I first met a data leader at a conference on this side of the pond who was actively engaging in large scale big data projects. This wasn’t a presenter
Data integration, on any project, can be very complex – and it requires a tremendous amount of detail. The person I would pick for my data integration team would have the following skills and characteristics: Has an enterprise perspective of data integration, data quality and extraction, transformation and load (ETL): Understands
In my prior two posts, I explored some of the issues associated with data integration for big data and particularly, the conceptual data lake in which source data sets are accumulated and stored, awaiting access from interested data consumers. One of the distinctive features of this approach is the transition
Integrating big data into existing data management processes and programs has become something of a siren call for organizations on the odyssey to become 21st century data-driven enterprises. To help save some lost time, this post offers a few tips for successful big data integration.
There is a time and a place for everything, but the time and place for data quality (DQ) in data integration (DI) efforts always seems like a thing everyone’s not quite sure about. I have previously blogged about the dangers of waiting until the middle of DI to consider, or become forced
While not on the same level of Rush, I do fancy myself a fan of The Who. I'm particularly fond of the band's 1973 epic, Quadrophenia. From the track "5:15": Inside outside, leave me alone Inside outside, nowhere is home Inside outside, where have I been? The inside-outside distinction is rather apropos
In my last post, I noted that the flexibility provided by the concept of the schema-on-read paradigm that is typical of a data lake had to be tempered with the use of a metadata repository so that anyone wanting to use that data could figure out what was really in
I've spent a great deal of time in my consulting career railing against multiple systems of record, data silos and disparate versions of the truth. In the mid-1990s, I realized that Excel could only do so much. To quickly identify and ultimately ameliorate thorny data issues, I had to up
