For an instructor lead, in-depth look at learning SQL click below.
Data transformation is an essential step in any data processing flow. It involves the conversion of data from one format or structure into another. It plays an integral role in data integration, data warehousing, data migration, and analytics. In today’s post, we will be looking at some techniques and strategies for SQL data transformation in Databricks.
Understanding Databricks
Databricks is a data analytics platform built around Apache Spark, an open-source, scalable data processing engine. It provides a platform for data science and data engineering to collaborate on massive datasets. Databricks enables users to run SQL (Structured Query Language) queries in Spark, adding simple data transformation methods and visualization capabilities.
Transforming Data With SQL
Now, let’s move on to some examples to illustrate how you can transform your data using SQL in Databricks. We will use the Table 1 as our primary data source.
|
1 2 3 4 5 6 7 8 9 10 11 12 13 14 |
/* Assume we have a table "Table 1" with the following schema and data */ CREATE TABLE Employees ( ID INT, Name VARCHAR(50), Age INT, Salary INT ); INSERT INTO Employees (ID, Name, Age, Salary) VALUES (1, 'John', 30, 70000), (2, 'Jane', 35, 80000), (3, 'Doe', 40, 90000); |
1. Changing Data Types
The CAST and CONVERT functions can be used to change the data type of a column. In the code snippet below, we show how to convert the Salary column from an INT to a FLOAT:
|
1 2 3 4 |
SELECT ID, Name, Age, CAST(Salary AS FLOAT) AS Salary FROM Employees; |
2. Filtering Data
SQL’s WHERE clause is used to filter data. For instance, if you want to select everyone in your database who is over 30 years of age:
|
1 2 3 4 5 |
SELECT * FROM Employees WHERE Age > 30; |
3. Sorting Data
The ORDER BY clause in SQL is utilized to sort data. For example, if we want to sort our data by Age in descending order:
|
1 2 3 4 5 |
SELECT * FROM Employees ORDER BY Age DESC; |
4. Aggregating Data
We can use the GROUP BY clause in combination with aggregate functions like COUNT, MAX, MIN, SUM, AVG, etc. The following code snippet shows how to get the total salary (sum) of all employees:
|
1 2 3 4 |
SELECT SUM(Salary) AS Total_Salary FROM Employees; |
Conclusion
Data transformation is a critical step to prepare your data for further analysis. SQL, combined with a powerful platform like Databricks, provides a versatile and efficient way to transform your data. The examples shown here are fundamental and just scratch the surface of what is possible. As you gain experience, you will likely encounter more complex data types and transformation scenarios. Always remember that the best practices in data transformation involve clear planning, understanding your data requirements, and diligently testing your results.