We’re rounding the corner to the second half of December, which means it’s time for my favorite holiday: Festivus! Like many of you, I enjoy gathering around the Festivus pole and sharing the time-honored traditions such as the Feats Of Strength and the Airing Of Grievances.
But my favorite Festivus tradition takes place right here on this blog: the Eleven Days of Festivus. Each year, I write a daily blog post each of the eleven days leading up to Festivus, usually around a central theme.
Tim has three posts up so far. First is around jumping straight into the code-writing phase:
Most data architects and developers are intensely curious folks. When we see a set of data, we want to immediately step into a data whisperer role. Where others may see a jumbled mess, we see an opportunity to discover patterns and answers. The best data architects crave those data discovery finds the same way a baseball player craves a bottom-of-the-9th game-winning home run.
That kind of intellectual curiosity is a necessary trait for data architects, but it can lead to a rush straight into writing ETL code. I’ve seen this a lot, and have done it myself (and admittedly still do it on occasion): skipping past the business-value analysis and diving straight into the haystack looking for needles. Getting raw data into a format that can easily be analyzed and validated is a critical part of the ETL development life cycle, but rarely is it the first step.
Second, processing too much data:
A common design flaw in enterprise ETL processes is that they are processing too much data. Having access to a great breadth and depth of data opens up lots of options for historical reporting and data analytics, but very often it is mistakenly assumed that all of the available data must be processed through ETL.
Although it may sound counterintuitive, there are many cases where purposefully leaving some data out of the ETL process leads to a better outcome.
Earlier this year, I wrote about the concepts of incremental loads and discussed the benefits of loading data incrementally. To recap: an incremental load moves only the new and changed data from each source – rather than the entire bulk of the source data – through the ETL pipeline.
Using incremental loads can improve both the speed and accuracy of data movement and transformation. The time required to process data increases with the volume of said data, and extracting only the new and changed data from the source can ensure an accurate ‘point-in-time’ representation of the data. For these reasons, loading data incrementally is, for most data load needs, the better way to go.
This is a good series to track.