Use Google Sheets to automate your data pipeline development:
This isn’t the first time I’ve talked about a code generator or SQL Generation on this blog, but it’s worth discussing again because I wanted to talk about a recent project where I upped the ante on not just generating SQL, but generating the DDL & DML to support an entire ETL pipeline — all while self-documenting everything!
Throughout the project I was able to quickly test different indexing strategies without writing a single line of code.
A common gripe I have when reviewing and maintaining other people’s SQL, aside from the obvious readability due to poor formatting, is their lack of aliases. In this post, I’d like to talk about why you should always reference aliases. The one major reason why, and the one minor reason when not to.
When maintaining and improving upon legacy code it’s easy to redefine standards, or define nonexistent standards, which have cascading effects on the pipeline. Take something as simple as an ill-conceived naming convention which, in hindsight, turned out to not make sense over time. At some point the benefits of rewriting lots of code outweigh simply sticking with the bad convention. In these cases consistency over standards become the convention.
I recently was tasked with building a new table with two Type-2 tables as a source and, not only maintain the history but, rebuild the history. Let’s look at the best way to join two type-2 tables together and then, more specifically, how to join the two historicized tables together while preserving the logical history of changes as though they were one.
Imagine you have two tables, typically type-2 dimensions, which have persisted historical records of changes: One for capturing your customer’s name over time, and another for tracking your customer’s phone number changes over time. In some cases, you may not have a historical record in one table, or the other, leaving an unknown name or phone number captured for a given customer. When re-building history you have to take care of these new periods of time where something hasn’t happened yet.
Now imagine that a customer changed her name three times and only on that third time did she also provide a phone number. That means, our third historical entry of her name change could potentially turn into five records, depending on when the phone number insert/update occurred.
Regardless of your RDBMS you’ll find more than a handful of scripts online to add a Date Dimension or Calendar Table to your schema. One problem I’ve seen is they lack an every-other-week column. So, let’s explore a common SQL Server script and how to add an Every Other Week flag to our date dimension.
This method is written for Microsoft’s SQL Server, but it is ANSI standard and will work with any date dimension which has an integer day-of-week column, which I’ve never seen one that doesn’t…
In ETL, we often have to load many targets from a common set of base tables. Inevitably the targets are different enough that we have to create multiple queries or views to populate the many outputs of data. Which is fine, except now you’ve got yourself a maintenance nightmare, one that is avoidable. I’d like to share a trick with you to take a single query that can be recursively modified to dynamically change its structure to get different outputs. I call it, dynamic querying using block quotes.
The business case:
The sales department would like to take a single report that already exists and split it into two reports. The additional report will require different fitlers, aggregates, columns, and even joins!
When dealing with transactional data often there are many levels of granularity lying within. Finding these granularities exposes how your data is shaped as it accumulates and helps paint a better picture of what I like to call Lifes within the data. In this post, I want to share a technique I use to find data patterns which will be beneficial for everyone from the analyst to the architect.
Why do I refer to these data patterns as Lifes?
I haven’t found anything transactional in nature that doesn’t have some sort of recurring theme, with a distinct beginning and end, that couldn’t tell a story. It is these finite beginning/ends, start/stops, on/offs that paint the picture that is the “life” of the data. The life of these stories often have many sub-narratives and are interwoven within a single holistic life of the data. A great example is the familiar case of a customer purchase history. The customer is the holistic life of the data, their purchase orders, individual line items, and even a particular line item purchased over several purchase orders are all examples of sub-narratives within the story of a single customer.
Just for fun: Let’s look at some interesting ways to solve a multi-column pivot or unpivot (aka crosstab). Some methods are not ANSI standard and others simply won’t work on certain RDBMSs, but we’ll play around with some methods that I came up with using SQL Server 2016.