# Learn Basic SQL with Databricks

### **Introduction**

SQL (Structured Query Language) is a powerful language for managing and querying relational databases. Databricks, built on top of Apache Spark, offers a unified platform that simplifies big data and machine learning processes. In this blog, we’ll explore how to get started with SQL on Databricks.

### **Sample test data files for Hands-on**

1. **Employee:** [https://github.com/vipinputhanveetil/ctb\_blog\_demo\_files/blob/main/databricks\_files/employees.csv](https://github.com/vipinputhanveetil/ctb_blog_demo_files/blob/main/databricks_files/employees.csv)
    
2. Orders: [https://github.com/vipinputhanveetil/ctb\_blog\_demo\_files/blob/main/databricks\_files/orders.csv](https://github.com/vipinputhanveetil/ctb_blog_demo_files/blob/main/databricks_files/orders.csv)
    
3. Products: [https://github.com/vipinputhanveetil/ctb\_blog\_demo\_files/blob/main/databricks\_files/products.csv](https://github.com/vipinputhanveetil/ctb_blog_demo_files/blob/main/databricks_files/products.csv)
    
4. Sales: [https://github.com/vipinputhanveetil/ctb\_blog\_demo\_files/blob/main/databricks\_files/sales.csv](https://github.com/vipinputhanveetil/ctb_blog_demo_files/blob/main/databricks_files/sales.csv)
    

### **Getting Started with Databricks**

#### **Step 1: Sign Up and Set Up**

1. **Refer this blog on how to Sign Up and Set Up databricks @** [https://vipinmp.hashnode.dev/kickstart-your-spark-data-exploration-journey-with-databricks](https://vipinmp.hashnode.dev/kickstart-your-spark-data-exploration-journey-with-databricks)
    

#### **Step 2: Upload Data**

1. **Go to catalog tab and upload the test data csv file to practice the basic SQL commands**.
    
    ![](https://cdn.hashnode.com/res/hashnode/image/upload/v1716631593513/434c9f5c-c31e-41bd-b267-7501611168c4.png align="center")
    
2. ![](https://cdn.hashnode.com/res/hashnode/image/upload/v1716623398259/4f9ba16d-afe9-44f8-a916-cdf2b7b74e35.png align="center")
    
    **Writing SQL in Databricks Notebooks**
    

#### **Step 3: Create a Notebook**

1. **Create a New Notebook:** From your workspace, create a new notebook. You can choose the language of your notebook; select SQL for this example.
    
2. **Attach the Notebook to a Cluster:** Make sure your notebook is attached to the cluster you created.
    

### **Basic SQL Queries**

Now that our data is loaded into a table, let’s run some basic SQL queries.

1. **SELECT:**
    
    ![](https://cdn.hashnode.com/res/hashnode/image/upload/v1716623828278/cbe39749-f610-4faf-ad2b-ba56da596539.png align="center")
    
    This query selects all records from the `sales` table.
    
2. **WHERE**
    
    ```sql
    SELECT * FROM sales WHERE amount > 100
    ```
    
    This query outputs only certain rows based on whether they meet certain conditions.
    
3. **AND**
    
    ```sql
    SELECT * FROM sales WHERE amount > 100 AND amount < 200
    ```
    
    Logical operator AND is used to select rows that meet both of two specified conditions.
    
4. **OR**
    
    ```sql
    SELECT * FROM sales WHERE amount > 100 AND amount < 200
    ```
    
    Logical OR operator is used to select rows that meet either of two specified conditions.
    
5. **NOT**
    
    ```sql
    SELECT * FROM sales WHERE NOT amount=100
    ```
    
    Logical NOT operator is used to display records that do not meet a specified condition.
    
6. **BETWEEN**
    
    ```sql
    SELECT * FROM sales WHERE amount BETWEEN 150 AND 200
    ```
    
    BETWEEN is a logical operator that selects values falling within a specified range.
    
7. **IN**
    
    ```sql
    SELECT * FROM sales WHERE product_id IN (101,102)
    ```
    
    IN operator is used to specify the list of values in the WHERE clause.
    
8. **LIKE**
    
    ```sql
    SELECT * FROM sales WHERE region LIKE 'We%'
    ```
    
    The LIKE operator is used in a WHERE clause to search for a specific pattern within a column.
    
9. **ORDER BY**
    
    ```sql
    SELECT * FROM sales ORDER BY product_id DESC
    ```
    
    The **ORDER BY** statement in **SQL** is used to **sort the fetched data** in either ascending or descending
    

### **Conclusion**

Databricks makes it easy to work with big data using SQL. By leveraging Databricks' powerful platform and the simplicity of SQL, you can quickly perform data analysis and gain valuable insights. Whether you’re just starting out or looking to enhance your data skills, Databricks provides the tools you need to succeed.