For an instructor lead, in-depth look at learning SQL click below.
Introduction
The modern enterprise landscape is dominated by an abundance of data. SQL, a robust and time-tested language aimed at managing data, has scaled up to address the needs of Machine Learning (ML) applications. In this blog post, we will explore how SQL can be used for ML models and algorithms in Databricks, becoming an essential tool for data scientists who need to create readable, efficient and scalable code.
Writing an SQL Query for Machine Learning
The key to effective SQL programming for machine learning applications lies in understanding how to write efficient SQL queries. Here’s a basic example:
|
1 2 3 4 5 6 7 |
-- SQL Query Example SELECT * FROM train_data WHERE train_data.age > 25 ORDER BY train_data.income DESC; |
This basic SQL query is selecting all columns from a training dataset where the age of individuals is older than 25, and ordered by their income in descending order.
Using SQL for Building Models
With Databricks, you can utilize built-in Machine Learning Libraries which makes the entire process significantly more straightforward. Let’s train a simple linear regression model as an example.
|
1 2 3 4 5 6 7 8 9 10 11 12 |
-- SQL Linear Regression Example CREATE OR REPLACE MODEL mydataset.mymodel OPTIONS(model_type='linear_reg') AS SELECT label_col, feature1, feature2, feature3 FROM mydataset.mytraining_table; |
The above SQL script creates a new linear regression model on a dataset with target column ‘label_col’ and three feature columns.
Conclusion
That brings us to the end of this post! As you can see, SQL coupled with Databricks provides a highly flexible and powerful platform for analyzing data and building ML models. With the help of good SQL writing practices and Databrick’s ML Libraries, you can focus more on the analysis and algorithm selection, and less on the programming aspects.