Efficiently Fetching Segmentation Data from Snowflake using External Functions

Discover the best practices for retrieving segmentation data from Snowflake to APIs, efficiently and cost-effectively, utilizing Azure Functions and MongoDB.
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How to Efficiently Fetch Segmentation Data from Snowflake

In today's data-driven environment, businesses want timely information about their users to enhance their applications. A common hurdle is efficiently transferring user segmentation data from a data warehouse, like Snowflake, to an application. This guide will outline a cost-effective and efficient approach to achieve this, addressing a real-world scenario that includes the use of external functions and MongoDB.

Understanding the Problem

Imagine you're working on a segmentation project to gather valuable user data in real-time. You have a system that connects user actions in an app to a Snowflake data table, tracking segmentation information like user behaviors and trends. For example, you may have users segmented by their gameplay streaks (e.g., 5-day, 10-day, and 15-day streaks).

The goal? When the game boots up, you want to fetch the relevant segments for a user efficiently and without compromising performance or incurring excessive costs, particularly since you expect a high volume of users—about 300,000 to 500,000 per day.

The Solution: Using External Functions and MongoDB

Step 1: Setting Up External Functions

To address high concurrency and ensure lower costs, we can create an external function on Snowflake using Azure Functions. This setup allows you to efficiently push updates to a MongoDB instance, which can manage high volumes of concurrent connections at a lower cost.

Why MongoDB?

Concurrency Handling: MongoDB is suitable for handling multiple simultaneous connections, which is crucial for an application expecting high traffic.

Cost-Efficiency: Running MongoDB on a local server eliminates hefty data transfer costs associated with public cloud services.

Step 2: Data Flow and Upserting to MongoDB

Here’s how the system works step-by-step:

Triggers from Snowflake: Each time data is stored or updated in Snowflake, your procedure should identify any segments that have changed (i.e., where new rows are added or the DeletedAt field is not null).

Calling the External Function: This function is then triggered, which takes the relevant segment data and upserts it into MongoDB using the pymongo client.

Use of Azure Functions: With Azure, you might need to configure networking settings like a VNET, NAT Gateway, and a static outbound IP address to ensure smooth communication between Snowflake and the MongoDB instance.

Step 3: Querying Segment Data

When fetching segment data for the client, your application makes an API call that retrieves user data from MongoDB, allowing quick access for personalization based on segments.

Here's a simplified SQL fetch command that would represent the logic in your MongoDB retrieval process:

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Example Response:

If there’s a match, you might receive a JSON response like this:

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This represents a user who has qualified for segment IDs 2 and 3, indicating their gameplay streaks.

Conclusion

By utilizing external functions in Snowflake to push updates to MongoDB, this method not only increases efficiency in fetching segmentation data but also significantly reduces the associated costs for high volume queries.

This approach allows you to maintain a performant application, equipping your team to better engage with users based on their behaviors and preferences. As data volume continues to grow, leveraging data warehouse capabilities efficiently will remain a crucial aspect of building scalable applications.

Ready to Implement?

If you’re looking to enhance your API interactions and streamline your data flow efficiently, co