For an instructor lead, in-depth look at learning SQL click below.
The following blog post will guide you on how to use SQL in conjunction with Databricks – an analytic platform powered by Apache Spark. We’ll demonstrate by implementing a simple machine learning model. But first, let’s establish an understanding of why SQL is crucial for data analytics and machine learning.
Why SQL in Machine Learning?
Structured Query Language (SQL) is a highly powerful tool loved by data enthusiasts for its simplicity and potent ability to manipulate data. Although SQL isn’t commonly associated with the development of machine learning models, it is extensively used in data gathering, a vital step in the machine learning pipeline. SQL plays a crucial role in ML where data inspection, cleaning, and transformation tasks are involved.
Databricks And Machine Learning
Databricks is a Unified Data Analytics Platform that allows data scientists and engineers to build machine learning models effortlessly. It also supports SQL, enabling you to process your data within the same platform.
Example of SQL Code in Databricks
Create a simple database and table:
|
1 2 3 4 5 6 7 8 9 10 11 12 13 |
CREATE DATABASE IF NOT EXISTS db_ml; USE db_ml; CREATE TABLE IF NOT EXISTS Persons ( PersonID int, LastName varchar(255), FirstName varchar(255), Address varchar(255), City varchar(255) ); INSERT INTO Persons (PersonID, LastName, FirstName, Address, City) VALUES (1, 'Doe', 'John', '123 Elm St', 'Anytown'); |
To fetch the data from the table we created:
|
1 2 3 |
SELECT * FROM db_ml.Persons; |
Using SQL with Machine Learning In Databricks
In Databricks, you can use SQL to query your data, then use the results for machine learning tasks. Below is a simple linear regression model using the Spark MLlib library:
|
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 |
-- Import the necessary libraries %python from pyspark.ml.regression import LinearRegression from pyspark.ml.feature import VectorAssembler -- Create a vector representation of the features assembler = VectorAssembler( inputCols=["PersonID"], outputCol="features") output = assembler.transform(Persons) -- Split the data into training and test sets (trainingData, testData) = output.randomSplit([0.7, 0.3]) -- Create and fit the model lr = LinearRegression(maxIter=10, regParam=0.3, elasticNetParam=0.8) model = lr.fit(trainingData) -- Make predictions on the test data predictions = model.transform(testData) predictions.show() |
This model is a simple yet effective way for you to deploy machine learning model using SQL in Databricks. Data cleaning, preparation, and analysis can be done using SQL, and the results fed into Spark’s machine learning libraries. This is a clear demonstration of the power and flexibility that the SQL- Databricks combination offers for machine learning.
Conclusion
This glimpse into the use of SQL in Databricks to execute machine learning models shows the potential for how these tools can be combined. Experimenting with these concepts further will give you more opportunities to test and perfect your skills. So, keep learning and keep experimenting!