Adventures in Machine Learning

Streamlining Data Analysis: The Power of SQL Patterns

Introduction to SQL Patterns

Are you familiar with SQL patterns? If you have worked with databases before, you might have come across this term.

However, if not, don’t worry. In this article, we will explain SQL patterns, their importance in reporting, and their usefulness in simplifying database queries.

Importance of Pivoting Data in Reporting

Reporting is a critical aspect of any business. Companies rely on reports to make informed decisions about their operations.

Hence, the accuracy and effectiveness of any report depend on the quality of the data. Pivoting data is an essential technique used in reporting.

It involves transforming table data from rows to columns, making it easier to analyze and present. This method is useful for summarizing information in a comprehensible format.

For instance, we can easily analyze sales data for a particular product, market, or time period using a pivot table.

Usefulness of Pivoting Data

Pivoting data is not only useful for reporting purposes; it is also beneficial for various database queries. This technique makes it easier to analyze complex data sets, allowing data analysts to identify patterns and trends quickly.

By pivoting data, it is easier to aggregate and summarize information, leading to more informed decision-making.

Concept of Pivot in SQL

SQL is the standard language used to manage relational databases. It provides a way to interact with a database by writing queries to retrieve, insert, update, or delete data.

In SQL, the PIVOT command is used to transform data sets from rows to columns. It is a powerful tool that makes it easier to query information and supports the aggregation of data.

Use of CASE Statement for Pivoting Data

In SQL, the CASE statement is used with the PIVOT command to pivot data. The CASE statement allows the application of custom rules for pivoting, whereas the PIVOT command provides a more structured approach.

By using the CASE statement when pivoting data, it is possible to apply additional filters and transformations to the results. Additionally, the CASE statement can be used to format column headings, making it easier to interpret the results.

Difference between PIVOT SQL Clause and CASE Statement for Pivoting Data

The main difference between the PIVOT SQL clause and the CASE statement is the level of customization. The PIVOT SQL clause provides a more structured approach to pivot data.

In contrast, the CASE statement allows for more complex rules and transformations during the pivot process. Additionally, the PIVOT SQL clause is limited to a fixed set of column names, whereas the CASE statement allows more flexibility when defining column headings.


Pivoting data is a valuable technique for simplifying database queries and enhancing reporting. In SQL, the PIVOT SQL clause and the CASE statement are essential tools for pivoting data.

Understanding these techniques enables data analysts to work more efficiently with large data sets and produce more insightful and effective reports. Apply these techniques to your SQL queries today to streamline data analysis and enhance reporting.

Understanding the Pivot SQL Pattern

The Pivot SQL pattern is a powerful tool that allows data analysts to pivot table data from rows to columns, making it easier to analyze and present. In this section, we’ll examine a practical example of a transactional table and demonstrate how the Pivot SQL pattern can be used to extract valuable information from it.

Example of Transactional Table Data

Let’s begin by considering a simple transactional table that records transactions made by customers. The table consists of three columns; transaction id, customer id, and transaction type, as shown below:


Transaction ID | Customer ID | Transaction Type


1 | 101 | Payment

2 | 101 | Purchase

3 | 102 | Payment

4 | 103 | Purchase

5 | 104 | Payment

6 | 104 | Payment


This table represents a typical transactional record that tracks a list of transactions made by various customers.

The table can be pivoted to summarize transaction details based on the transaction type and customer.

Sum of all Transactions from all Customers Divided by Transactional Types

Using the Pivot SQL pattern, we can transform the above transactional table into a summarized tabular view. For instance, we can obtain the total sum of transactions made by each customer and show the breakdown of transactional types.

The desired output would look something like this:


Customer ID | Payment | Purchase


101 | 1 | 1

102 | 1 | 0

103 | 0 | 1

104 | 2 | 0


The above result shows the total number of transactions for each transaction type made by each customer. For example, Customer 101 made one payment and one purchase, while Customer 104 made two payments and no purchases.

Use of CASE Pattern for Pivoting Data

The Pivot SQL pattern can be combined with the CASE statement to pivot table data. The CASE pattern is a powerful tool that allows data analysts to introduce custom rules and transformations during pivoting.

To pivot the above transactional table using the CASE pattern, we can use the following query:


SELECT customer_id,

SUM(CASE WHEN transaction_type = ‘Payment’ THEN 1 ELSE 0 END) AS ‘Payment’,

SUM(CASE WHEN transaction_type = ‘Purchase’ THEN 1 ELSE 0 END) AS ‘Purchase’

FROM transactions

GROUP BY customer_id;


The above query groups the transactional table by customer ID, and applies the custom rules specified in the CASE statements. The result is the summarized table that shows the number of transactions made by each customer, broken down by transaction type.

Try It Yourself

Now that we have seen how the Pivot SQL pattern can be used to transform transactional table data, let’s try to create a counting query using Vertabelo. Vertabelo is an online SQL modeling tool used for creating and managing SQL databases.

Instruction to Create Counting Query

Let’s create a counting query that shows the total number of products sold by a particular store. Here are the steps:


Open Vertabelo and create a new SQL model. 2.

Create a new table named “transactions” with the following columns:

– transaction_id (int)

– store_id (int)

– product_id (int)

– date (date)

– quantity (int)

3. Add some sample data to the “transactions” table.

4. Write a counting query that sums up the total number of products sold by a particular store, grouped by transaction date.

The query should look something like this:


SELECT date, SUM(quantity) AS ‘Total Sold’

FROM transactions

WHERE store_id =

GROUP BY date;


Vertabelo Table Definition

Here is an example of how the “transactions” table should be defined in Vertabelo:


CREATE TABLE transactions (

transaction_id INT,

store_id INT,

product_id INT,

date DATE,

quantity INT



Encouragement to Experiment with the Concept

The Pivot SQL pattern is a powerful tool that can be used to pivot table data and simplify complex queries. By experimenting with the tool in Vertabelo, data analysts can streamline their data analysis process and produce reports with valuable insights.

We encourage you to experiment with the Pivot SQL pattern and gain a better understanding of how it can enhance your data analysis. In conclusion, SQL patterns are essential techniques used to simplify complex queries and enhance data analysis.

Pivoting data using SQL patterns is particularly crucial, as it makes it easier to summarize and present data in a comprehensible format. The Pivot SQL pattern, when combined with the CASE statement, is a powerful tool that allows data analysts to extract valuable insights from large data sets.

By applying the concepts discussed in this article, data analysts can streamline their analysis and reporting processes, leading to more informed decision-making. Experimenting with these techniques is highly encouraged to gain insight and increase proficiency with these tools.