# Load Data and Run Queries into SingleStore

This tutorial shows how to create a database, load sample data, and run queries using SingleStore Helios workspace.

It tutorial includes the following steps:

1. Create a SingleStore Helios workspace.

2. Create a database and a table.

3. Create an S3 pipeline and run it to load approximately 18 million rows of sample sales data for a retail store into the SingleStore database.

4. Run queries against the loaded data.

## Create a SingleStore Helios Workspace

If a workspace already exists, skip to the next step to create the database. Navigate to the [Cloud Portal](https://portal.singlestore.com/) and create a [workspace](https://docs.singlestore.com/cloud/getting-started-with-singlestore-helios/compute-workspaces.md).

## Create a Database and a Table

Connect to your SingleStore deployment and run the following SQL commands to create a database named **sales** and associated table. Free shared workspace users cannot create new databases.

For SingleStore Helios deployments, you can also run these commands in the SQL Editor, select **Cloud Portal > Editor > Open SQL Editor**.

> **📝 Note**: The **SQL Editor** only runs the queries that you select, so ensure you have them all selected before selecting **Run**.

```sql
CREATE DATABASE sales;
USE sales;

CREATE TABLE SalesData (
    Date TEXT COLLATE utf8mb4_bin,
    Store_ID BIGINT(20) DEFAULT NULL,
    ProductID TEXT COLLATE utf8mb4_bin,
    Product_Name TEXT COLLATE utf8mb4_bin,
    Product_Category TEXT utf8mb4_bin,
    Quantity_Sold BIGINT(20) DEFAULT NULL,
    Price FLOAT DEFAULT NULL,
    Total_Sales FLOAT DEFAULT NULL
);
```

## Create and Run an S3 Pipeline

This step shows how to ingest the sample sales data from a public AWS S3 bucket into the SingleStore database using pipelines. [Pipelines](https://docs.singlestore.com/cloud/load-data/about-singlestore-pipelines.md) enable you to extract, transform, and load external data without needing third-party tools or middleware.

> **📝 Note**: For this example, you must set `data_conversion_compatibility_level` to a value of `7.5` or lower.

Run the following SQL command to create the pipeline:

```sql
CREATE PIPELINE SalesData_Pipeline AS
LOAD DATA S3 's3://singlestoreloaddata/SalesData/*.csv'
CONFIG '{ "region": "ap-south-1" }'
INTO TABLE SalesData
FIELDS TERMINATED BY ','
LINES TERMINATED BY '\n'
IGNORE 1 lines;
```

Run the following SQL command to start the pipeline:

```sql
START PIPELINE SalesData_Pipeline;
```

The pipeline ingests 18 million rows of data, which takes some time.

## Verify the Pipeline Success

Query the `pipelines_files` information schema view to inspect the progress of the pipelines. The following command to check whether the `SalesData_Pipeline` pipeline has finished ingesting data.

```sql
SELECT * FROM information_schema.pipelines_files 
WHERE pipeline_name = "SalesData_Pipeline";
```

You can also run the pipelines in the foreground to easily verify that all the data has been ingested.

```sql
START PIPELINE <pipeline_name> FOREGROUND;
```

For SingleStore Helios deployments, you can also monitor the progress of your pipelines on the Cloud Portal, select **Ingestion > Pipelines**. Select your workspace from the list at the top of the page.

## Run Queries on the Data

Once the data is loaded into your SingleStore database, the following queries can be used on the dataset.

## Query 1: Total Sales for Each Product Category

This query finds the total sales for each product category.

```sql
SELECT Product_Category, SUM(Total_Sales) AS Total_Category_Sales
FROM SalesData
GROUP BY Product_Category
ORDER BY Total_Category_Sales DESC;


```

```output

+------------------+----------------------+
| Product_Category | Total_Category_Sales |
+------------------+----------------------+
| Furniture        |            473073264 |
| Clothing         |            472839616 |
| Toys             |            472607632 |
| Groceries        |            472568544 |
| Pharmacy         |            472009800 |
| Electronics      |            471887584 |
| Pet Supplies     |            470901324 |
+------------------+----------------------+

```

## Query 2: Top Five Best-Selling Products by Quantity Sold

This query identifies the top five products with the highest quantity sold.

```sql
SELECT Product_Name, SUM(Quantity_Sold) AS Total_Quantity_Sold
FROM SalesData
GROUP BY Product_Name
ORDER BY Total_Quantity_Sold DESC
LIMIT 5;


```

```output

+--------------+---------------------+
| Product_Name | Total_Quantity_Sold |
+--------------+---------------------+
| Coats        |              542856 |
| Jeans        |              541885 |
| Vests        |              539984 |
| Sweaters     |              539455 |
| Jackets      |              538003 |
+--------------+---------------------+

```

## Query 3: Monthly Sales Trend for a Specific Year

This query shows the monthly sales in the year 2024.

```sql
SELECT DATE_FORMAT(Date, '%Y-%m') AS Month, SUM(Total_Sales) AS Monthly_Sales
FROM SalesData
WHERE YEAR(Date) = 2024
GROUP BY Month
ORDER BY Month;


```

```output

+---------+--------------------+
| Month   | Monthly_Sales      |
+---------+--------------------+
| 2024-01 | 559552342.1877503  |
| 2024-02 | 523234602.4183636  |
| 2024-03 | 560848984.9077282  |
| 2024-04 | 542002073.6334558  |
| 2024-05 | 560440739.4689851  |
| 2024-06 | 541728606.9727273  |
| 2024-07 |  36056514.9656029  |
+---------+--------------------+

```

## Query 4: Top Three Stores with the Highest Sales in the Electronics Category

This query identifies the top three stores with the highest sales in the Electronics category.

```sql
SELECT Store_ID, SUM(Total_Sales) AS Electronics_Sales
FROM SalesData
WHERE Product_Category = 'Electronics'
GROUP BY Store_ID
ORDER BY Electronics_Sales DESC
LIMIT 3;


```

```output

+----------+-------------------+
| Store_ID | Electronics_Sales |
+----------+-------------------+
|     1080 |   23858838.78125  |
|     1014 |   23779519.59375  |
|     1062 |   23777307.40625  |
+----------+-------------------+

```

## Query 5: Total Revenue by Product Category for a Specific Store

This query finds the total sales revenue for each product category for a specific store (Store ID 1084).

```sql
SELECT
   Store_ID,
   Product_Category,
   SUM(Total_Sales) AS Total_Revenue
FROM
   SalesData
WHERE
   Store_ID = 1084
GROUP BY
   Store_ID, Product_Category;


```

```output

+----------+------------------+----------------+
| Store_ID | Product_Category | Total_Revenue  |
+----------+------------------+----------------+
|     1084 | Toys             | 23508569.87500 |
|     1084 | Electronics      | 23364933.40625 |
|     1084 | Pharmacy         | 23310525.65625 |
|     1084 | Furniture        | 23373549.37500 |
|     1084 | Groceries        | 23128677.03125 |
|     1084 | Pet Supplies     | 23458593.21875 |
|     1084 | Clothing         | 23447724.09375 |
+----------+------------------+----------------+

```

## Query 6: Top Three Most Profitable Days by Total Sales

This query identifies the top three days with the highest total sales across all stores.

```sql
SELECT Date, SUM(Total_Sales) AS Daily_Total_Sales
FROM SalesData
GROUP BY Date
ORDER BY Daily_Total_Sales DESC
LIMIT 3;


```

```output

+------------+-------------------+
| Date       | Daily_Total_Sales |
+------------+-------------------+
| 2024-03-12 |   46100391.87500  |
| 2023-10-04 |   46064107.06250  |
| 2023-08-10 |   45997295.31250  |
+------------+-------------------+

```

## Query 7: Yearly Sales Growth Comparison

This query compares the total sales by year to analyze growth.

```sql
SELECT YEAR(Date) AS Year, SUM(Total_Sales) AS Annual_Sales
FROM SalesData
GROUP BY YEAR(Date)
ORDER BY Year;


```

```output

+------+--------------+
| Year | Annual_Sales |
+------+--------------+
| 2023 |   8225504096 |
| 2024 |   8308831376 |
+------+--------------+

```

## Query 8: Most Expensive Product Sold

This query finds the most expensive product sold, along with its details.

```sql
SELECT Product_Name, Price, Total_Sales
FROM SalesData
ORDER BY Price DESC
LIMIT 1;


```

```output

+----------------------+-------+-------------+
| Product_Name         | Price | Total_Sales |
+----------------------+-------+-------------+
| Ginkgo Biloba 120 mg |   100 |         700 |
+----------------------+-------+-------------+
```

## Query 9: Average Quantity Sold per Product Category

This query calculates the average quantity sold for each product category.

```sql
SELECT Product_Category, AVG(Quantity_Sold) AS Average_Quantity_Sold
FROM SalesData
GROUP BY Product_Category;


```

```output

+------------------+-----------------------+
| Product_Category | Average_Quantity_Sold |
+------------------+-----------------------+
| Groceries        |               10.5019 |
| Pet Supplies     |               10.4948 |
| Clothing         |               10.4956 |
| Electronics      |               10.5035 |
| Pharmacy         |               10.4952 |
| Toys             |               10.4935 |
| Furniture        |               10.4928 |
+------------------+-----------------------+

```

***

Modified at: June 17, 2026

Source: [/cloud/getting-started-with-singlestore-helios/next-steps-and-examples/sample-data/load-data-and-run-queries-into-singlestore/](https://docs.singlestore.com/cloud/getting-started-with-singlestore-helios/next-steps-and-examples/sample-data/load-data-and-run-queries-into-singlestore/)

(An index of the documentation is available at /llms.txt)