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.
For my Business Objects (BOBJ) SAP Data Services (BODS) folks out there, this post will indeed be helpful at some point during your exposure to the ETL tool. You’ll eventually find yourself digging through the Data Services repository trying to locate details about your jobs, reports, or anything that makes your dynamic processing easier. There are loads of forum posts on the subject and even a few nice posts that attempt to make sense of what the schema has to offer. But, there’s one thing that I couldn’t find and am sharing with you today: How to find BODS Workflow to Dataflow to Target Metadata for your target tables within BODS.
In my case, I needed to retrieve the number of rows inserted/discarded/deleted/updated for all target tables within all dataflows within a particular workflow. Since my job routine is divided into workflows, I didn’t need to take the granularity any farther but is certainly possible. Still, in my case, I had the need to retrieve metadata about what my workflow just did with having many dataflows within it all running in parallel. This parallelism introduced the need to dynamically pull these statistics from the repository rather than after each dataflow execution.
The topic of dynamic dimension descriptions isn’t new, but the method for which I’ve come up with is a hybrid of several other methodologies, making for a great subject piece. Using codes and descriptions in your warehouse dimensions is standard practice, even in some cases the use-case may require displaying a code versus a description, for example: displaying USA instead of United States of America, or even in situations where the business understands what a “TPS” report type is more so than the formal “Testing Procedure Specification” description. In any case, you’ll want a method that enables the ability to add and modify additional codes and their descriptions without having to perform risky manual updates to the warehouse.
In this post, I will discuss a rigid yet tolerant way for properly implementing dynamic dimension descriptions without ever directly modifying a warehouse dimensional table. Instead, we’ll implement a lookup table that an end user can insert and update freely, along with a robust ETL process that uses this same lookup table to perform description updates and even type 2 historical tracking, if necessary.
Dynamic Dimension Descriptions Using An ETL Lookup Table
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.
One of my favorite uses of set-data manipulation involves using NULL values to my advantage; from NULLIF to COALESCE, we’ll explore some creative use of null values. These tips & tricks aren’t just a way to convert a NULL to another value, they’re a multi-purpose, insanely powerful way to massage and combine data.
Level Set: What is a NULL?
In SQL a NULL value isn’t a value at all – it’s lack of value. It’s a value that is indicative of not having a value. Think of it as if you asked someone a question but they didn’t respond, their response was a NULL value. So, therefore you cannot compare someone’s non-response to someone else’s non-response, while they seem like the same answer they’re likely not even the same question!
Null logical comparison
There are some exceptions to this rule (thanks, Microsoft) but, like most of my blog posts, I try to stick with ANSI standard rules. While some RDBMSs treat NULL differently and even have switches that can be set during runtime to alter how NULL logic works, we’re not going to go there. Just assume ANSI 99.
I’ve since evolved a little since I posted this and would like to hone my focus of this post to a clearer target: Look For Ridiculous instead.
This is my hands-down, go-to interview question to weed out non-technical candidates. I have had such great success with this interview question and have shared it with other colleagues who have experienced equal success that I think it deserves a place on this blog. I’ve talked about this before, a little over a year ago is when I made my first public announcement of my new-found favorite interview question:
How do you open notepad on windows?
I typically preface the question with a short disclaimer like, “This question is a bit unorthodox and there really isn’t a wrong answer, having more than one answer is also equally acceptable…” This sets the stage for the actual question which I’ll phrase like “On a windows machine, regardless of version specifics, how do YOU open windows notepad?”