SQL Foo | Method to Find Data Patterns

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.

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SQL Foo | Creative Use of Null Values

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.

NULL Galaxy - Creative use of null values
NULL Galaxy – Creative use of null values

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!

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.

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SQL Foo | Count within a case statement

Just the other day I was asked to help a peer solve an SQL problem they were having; they were trying to count within a case statement which was proving to be problematic. They had several columns within the query, most using an analytical function to find the sum, min or max of various fields – but there was one instance where she needed to count the number of occurrences of a situation, a situation that required several other fields to determine, and without adding those other fields to the GROUP BY would break the logic.

Counting like a ninja with SQL.
Like a ninja, count within a case statement.

How to count within a case statement?

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Interview Question | How to find all duplicates in a table?

One of my favorite interview questions is to hand the candidate a marker and ask them to write out how to find all duplicates in a table. This should be straightforward and weeds out anyone who struggles with SQL; and even if they don’t struggle with SQL, this will be a good way to gauge where they’re at. But, it doesn’t stop there!

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.

After they successfully give an answer, typically one involving grouping by the business key having a count greater than one, which I’ll show in the first example below, that is a go-to correct response to this question. But, I throw them a curve ball, I’ll say “Great! Show me another way.” Then, I ask for another, and another, and another… with great power comes great responsibility being the interviewer is nefariously fun!

How to find all duplicates in a table.
How to find all duplicates in a table.

So, let’s take a look at some ways on how to find all duplicates in a table by exploring all the ways that I’ve come up with. Here’s our test data we’ll be working with, which has 2 sets of duplicates:

The goal is to learn how to find all the duplicates in a table and return only 2 rows of data, ie: that have more than one identical row, even though there are 5 rows of duplicates (three dupes of one row and two of another).

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Row Number Window Function | First Occurrence In A Series

Using the Row Number Window Function to flag records if that row is the first occurrence in a series is a performant way to extract more value from your data. By using a Partition By clause we can group chunks of data together while ordering them to figure out which is the first in that series of data. Row Number, with the addition of the Over clause, allows us to achieve this without the use of a subquery by simply wrapping the function in a case statement.

A meme of how we put the fun in window functions!
A meme of how we put the fun in row number window functions!

An example of how to find the first occurrence in a series

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Catch-All Join To A Lookup Dimension

I recently ran into an interesting problem that I’d like to share and show how I resolved it. The solution involves a catch-all join to a lookup dimension table.

ERD Diagram of wildcard lookup status table.
ERD Diagram of wildcard lookup status table.

Imagine having many employees that work in many departments. Each department has their own way of determining the employee’s status; Some departments use the status code that was given in the source system, other departments rely solely on the department they’re from and others use a combination of both! Oh yeah, the fun bit, this status logic can change…

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