For an instructor lead, in-depth look at learning SQL click below.
Data analysis is an essential part of today’s business landscape. Knowing how to make sense of massive amounts of data can give your business an edge over the competition. In this guide, we will look at how to perform data analysis using SQL on Databricks, a unified data analytics platform.
Step 1: Setting up your Databricks environment
Before you can run SQL commands, you need to ensure that your Databricks environment is correctly set up. This involves creating clusters, notebooks and tables.
Step 2: Creating Clusters
In the Databricks workspace, navigate to the Clusters page and click on “Create Cluster”. For a standard setup, select the Databricks Runtime Version to be 6.6 or above.
Step 3: Creating Notebooks
In the Databricks workspace, navigate to the workspace, click on “Create”, then “Notebook”. In the “Create Notebook” window, enter a name for the notebook, select Python as the language, and select the cluster you created earlier.
Step 4: Creating Tables
Now, we need to create a table with the data we want to analyze. Consider the following code example:
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CREATE TABLE ProductSales ( ProductID INT, SaleDate DATE, UnitsSold INT ) |
Step 5: Data Analysis with SQL
Once we have our environment set up and our data loaded, we can begin to perform data analysis with SQL. Here is an example of a SQL query that fetches total units sold for each product:
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SELECT ProductID, SUM(UnitsSold) as TotalUnitsSold FROM ProductSales GROUP BY ProductID |
This SQL statement sums up the units sold for each product and groups the results by the ProductID.
Conclusion
This was a basic guide to get started with SQL data analysis on Databricks. There are plenty more commands and techniques in SQL that can be utilized for more advanced and complicated data analysis. The key is to practice and become familiar with many different scenarios and commands, and you’ll soon become an expert in SQL data analysis yourself!