Essential Databricks SQL Commands Every Data Engineer Must Know

 Introduction 

If you're working with data in Databricks, you’ll likely come across something called SQL—short for Structured Query Language. It's one of the most important tools for reading, organizing, and changing data. 

Whether you're building dashboards, cleaning up raw data, or just exploring what’s inside a table, SQL commands are your best friends. 

In this blog, we’ll walk through the most useful SQL commands for Databricks users—especially data engineers. You don’t need to be an expert. These are simple, practical commands that can save you time and effort every day. 

 

Agenda 

1. Viewing Tables and Schemas 
2. Exploring Data with SELECT 
3. Filtering Rows with WHERE 
4. Sorting Results with ORDER BY 
5. Grouping Data with GROUP BY 
6. Creating Tables and Views 
7. Inserting and Updating Data 
8. Joining Tables Together 
9. Cleaning Up with DELETE and DROP 
10. Tips for Working in Databricks SQL 

 

1. Viewing Tables and Schemas 

Before you touch any data, it helps to know what's available. 

SHOW TABLES; 
  

This shows a list of tables in your current workspace. If you want to see what’s inside a specific table: 

DESCRIBE table_name; 
  

It tells you the column names and data types, which is super useful when working with unfamiliar data. 

 

2. Exploring Data with SELECT 

Want to see what your data looks like? Use SELECT to fetch rows from a table. 

SELECT * FROM sales_data; 
  

This shows all columns and rows in the sales_data table. If you only want a few columns: 

SELECT name, amount FROM sales_data; 
  

 

3. Filtering Rows with WHERE 

Sometimes you only want specific rows—like sales from a certain city. 

SELECT * FROM sales_data 
WHERE city = 'Mumbai'; 
  

You can also use conditions like: 

WHERE amount > 1000 
  

or 

WHERE date >= '2024-01-01' 
  

 

4. Sorting Results with ORDER BY 

To see results in a certain order (like biggest to smallest): 

SELECT * FROM sales_data 
ORDER BY amount DESC; 
  

Use ASC for ascending (small to big) and DESC for descending. 

 

5. Grouping Data with GROUP BY 

If you want to summarize your data—like total sales per city: 

SELECT city, SUM(amount) as total_sales 
FROM sales_data 
GROUP BY city; 
  

You can also count rows: 

SELECT city, COUNT(*) as num_sales 
FROM sales_data 
GROUP BY city; 
  

 

6. Creating Tables and Views 

If you have raw data and want to create your own table: 

CREATE TABLE new_table ( 
  id INT, 
  name STRING, 
  amount DOUBLE 
); 
  

To make a view (like a saved query): 

CREATE VIEW big_sales AS 
SELECT * FROM sales_data 
WHERE amount > 5000; 
  

Views are great when you want to reuse a filter or logic across multiple places. 

 

7. Inserting and Updating Data 

To add a new row: 

INSERT INTO sales_data VALUES (101, 'Anil', 1500, 'Chennai'); 
  

To update values: 

UPDATE sales_data 
SET amount = 2000 
WHERE id = 101; 
  

This is useful for correcting data or making changes after reviewing results. 

 

8. Joining Tables Together 

If your data is split across multiple tables, you can join them: 

SELECT a.name, b.product 
FROM customers a 
JOIN orders b ON a.customer_id = b.customer_id; 
  

This pulls data from both tables where the IDs match. You can also do LEFT JOIN, RIGHT JOIN, and FULL JOIN depending on what you want to keep. 

 

9. Cleaning Up with DELETE and DROP 

To delete rows: 

DELETE FROM sales_data 
WHERE amount < 100; 
  

To completely remove a table or view: 

DROP TABLE sales_data; 
-- or 
DROP VIEW big_sales; 
  

Use these with care—once gone, the data can’t be recovered unless you have backups. 

 

10. Tips for Working in Databricks SQL 

1. Always test your SQL queries with LIMIT 10 before running on full datasets. 
2. Use comments (--) to make your SQL readable. 
3. Use CREATE OR REPLACE VIEW to update views without deleting first. 
4. Try EXPLAIN before running a slow query—it shows how Databricks will process it. 
5. Save queries in notebooks to organize your work and share with teammates. 

 Conclusion 

You don’t need to learn 100+ SQL commands to work well in Databricks. Just knowing the essential ones—like SELECT, WHERE, JOIN, and GROUP BY—can help you unlock powerful insights from your data. 

Whether you’re exploring raw files, building dashboards, or setting up pipelines, these SQL commands form the building blocks of your day-to-day tasks. 

What’s Next? Start Querying Smarter 

Knowing your way around Databricks SQL isn’t just a nice skill—it’s the foundation for real data engineering impact. With the essential commands under your belt, you’re ready to explore, analyze, and transform data like a pro. 

But why stop at just running queries? 

At AccentFuture, we help you move from learning to real-world implementation with: 

✅ Hands-on Databricks projects and guided labs 
✅ SQL for big data use cases and pipeline building 
✅ Expert mentorship on ETL, dashboards, and automation 
✅ Career prep for data engineer roles—interview to offer 

📘 Whether you’re just getting started or want to go deeper, we’ve got your learning path covered. 

🔗 Learn more: https://www.accentfuture.com/enquiry-form/ 
📧 contact@accentfuture.com 
📞 +91–9640001789 
🌐 www.accentfuture.com