How to use IS NUMERIC in Snowflake?

The IS_NUMERIC function in Snowflake is an essential tool for data validation. It allows you to quickly check if a value is a number, which is especially useful when working with large datasets from various sources. In this guide, we’ll break down what IS_NUMERIC does, provide a step-by-step guide on how to use it, cover common mistakes, and share some optimization tips. By the end, you'll know how to ensure your data is clean and ready for analysis.

What is IS_NUMERIC in Snowflake?

The IS_NUMERIC function checks if a given value can be interpreted as a number and returns true if it is, and false if it's not. This is useful when you're cleaning data that may have mixed entries, such as numbers, text, or symbols. IS_NUMERIC helps ensure that your numerical operations like summation, average calculations, or data transformations don’t fail due to non-numeric data. By incorporating this function into your data validation process, you can improve the quality of your data and enhance the reliability of your analysis.

How IS_NUMERIC Works

The IS_NUMERIC function evaluates values from columns like VARCHAR (text), INTEGER (whole numbers), or FLOAT (decimal numbers). If a value can be converted to a number, IS_NUMERIC will return true. If the value is text, symbols, or anything that can't be turned into a number, it returns false. This allows you to filter out invalid entries.

Example

Consider a table containing product prices, where some values may have been entered incorrectly:

If you need to filter out non-numeric entries in the Price column, you can use IS_NUMERIC to exclude values like "Free" and "$40."‍

‍This query will return only the rows where the price is numeric, helping you avoid errors during calculations.

Why IS_NUMERIC is Important for Data Validation

Data validation ensures that the information you're working with is accurate and reliable. The IS_NUMERIC function is a crucial part of this process, especially when you’re dealing with inconsistent data. By filtering out non-numeric values, you ensure that your calculations and analyses are performed only on valid data.

Real-World Example

Imagine you’re working with a sales dataset that contains revenue figures. If some cells have text (like "N/A" or "Unknown") instead of numbers, your calculations will be thrown off. Using IS_NUMERIC allows you to filter out these problematic values before performing any calculations, ensuring your results are accurate.

‍Here, IS_NUMERIC helps you sum up only the valid numeric revenue figures, excluding non-numeric values.

How to Use IS_NUMERIC in Snowflake: Step-by-Step

Step 1: Set Up Your Snowflake Environment

Before using IS_NUMERIC, ensure that your Snowflake environment is properly set up. This means you have the necessary permissions to run queries, access to the right database, and a good understanding of basic SQL syntax. If you're new to Snowflake, review Snowflake’s setup documentation, which covers how to create accounts, set up warehouses, and connect using SQL clients.

Step 2: Writing the IS_NUMERIC Query

Once your environment is ready, you can write your query to use IS_NUMERIC. Here's a simple example that filters a column for numeric values:

This query checks the values in column_name and returns only those that are numbers. This is especially useful for columns that are supposed to contain only numeric values but may have text or symbols due to human error.

Handling Null Values

It's important to note that null values (empty cells) are not considered numeric, so IS_NUMERIC will return false for nulls. If you want to keep track of null values separately, you can modify your query to include them:

This query will return both numeric values and empty cells, so you can handle them appropriately.

Common Mistakes and Troubleshooting

1. Data Type Mismatch

One common issue is applying IS_NUMERIC to data types that it doesn’t support. Make sure the column you’re checking contains values like VARCHAR, INTEGER, or FLOAT. Avoid applying it to unsupported types like dates or complex objects.

Example Mistake: Using IS_NUMERIC on a date field can result in unexpected behavior.

2. Handling Text with Numbers

Sometimes, text strings may contain numbers mixed with symbols or letters, like "$100" or "1,000 units." IS_NUMERIC will return false for these cases because they’re not purely numeric.

Solution: To handle cases like "$100", you may need to clean the data first, removing symbols or commas before using IS_NUMERIC.

‍
This query removes the dollar sign before checking if the value is numeric.

Optimizing the Use of IS_NUMERIC

1. Use IS_NUMERIC Selectively

Avoid using IS_NUMERIC on every column of a large table, as this can slow down your queries. Instead, focus on the columns that need numeric validation. For example, it’s unnecessary to run IS_NUMERIC on columns that are already restricted to numerical data types, like INTEGER or DECIMAL.

2. Combine with Other Validation Techniques

For more robust data validation, combine IS_NUMERIC with other functions. You can use regular expressions or other validation rules to check for specific formats or patterns. For example, if you're validating phone numbers, you can use a combination of IS_NUMERIC and REGEXP to ensure they meet the required format.‍

‍This query checks that the phone number is numeric and matches a 10-digit format.

Conclusion: Maximizing Data Accuracy with IS_NUMERIC

The IS_NUMERIC function in Snowflake is a powerful tool for ensuring data accuracy by filtering out non-numeric values. By incorporating it into your data validation process, you can ensure that only valid, numerical data is included in your calculations, reducing the risk of errors.

Key Takeaways:

• IS_NUMERIC helps you determine if a value is numeric, which is critical for clean data.
• It is easy to implement with a simple query, and it works with common data types like text and numbers.
• Be mindful of common mistakes, such as data type mismatches and handling text with symbols.
• You can optimize IS_NUMERIC usage by combining it with other validation techniques.

By using IS_NUMERIC effectively, you ensure cleaner datasets, more accurate analyses, and smoother operations when working with numbers in Snowflake.

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