Handling JSON Data in Data Vault Satellites on Snowflake

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Handling JSON Data in Data Vault Satellites on Snowflake

In this blog post, we’ll discuss the challenges and best practices for handling JSON data in Data Vault architectures, specifically when using Snowflake. This question from our audience raises a common scenario in modern data environments where semi-structured data is prevalent. Let’s dive into how storing JSON in Data Vault Satellites works, when to extract fields, and the performance considerations for Snowflake.

Why Store JSON in the Satellite?

With the increasing use of APIs and complex data integrations, many businesses are receiving data as JSON. JSON is a flexible, semi-structured format that allows for varying structures and nested elements, making it ideal for some applications. However, this flexibility also introduces complexities when using a structured data model like the Data Vault.

The challenge here is that when you receive a JSON payload, it may contain hundreds of fields, many of which may not be immediately needed. Extracting every JSON field to store as individual columns in the Raw Data Vault can be inefficient and unnecessary. Therefore, one solution is to store the JSON “as-is” in the Satellite and extract only what’s required later in the Business Vault.

Choosing When to Extract Fields from JSON

As a general rule, it’s beneficial to extract attributes that are needed for reporting, security, or compliance early in the process. Relational attributes stored directly as columns in the Raw Data Vault are easier to query, and they’re stored more efficiently in structured databases.

For other fields in the JSON payload that are less critical or not immediately required, you can keep the data in its JSON format. This approach minimizes upfront work and allows for flexible data exploration later without overloading the Raw Data Vault with unnecessary fields.

For example, consider an e-commerce environment where transaction data is received in JSON format. The Raw Data Vault could extract only essential fields like transaction ID, customer ID, and total amount while leaving other detailed information about the items purchased in JSON format.

JSON and Snowflake: Optimized for Performance

One of Snowflake’s strengths is its ability to store and query JSON data natively. Snowflake’s support for semi-structured data formats like JSON and its efficient handling of this data type make it possible to perform queries directly on JSON attributes without needing to first extract them into columns.

This capability allows you to store JSON data in the Raw Data Vault and query it directly, even in production, without significant performance penalties. This approach works well for data that may only be needed occasionally or where performance is not a major concern. Snowflake’s built-in functions for working with JSON make it easy to extract specific attributes on demand and use them in downstream reporting.

Handling Business Logic in the Business Vault

In Data Vault, business rules are often applied within the Business Vault layer, which is designed for derived and calculated data. When working with JSON in this context, it’s common to keep the original JSON structure in the Satellite and then perform transformations as needed in the Business Vault.

In Snowflake, you can take advantage of virtual views to simplify your data pipeline. Rather than physically extracting and storing each JSON attribute in the Business Vault, you can create virtual views that access JSON fields as needed. This approach offers the benefit of flexibility and reduces storage requirements since you avoid duplicating data.

Structuring Semi-Structured Data Over Time

Storing JSON in the Satellite provides flexibility, but as your business needs evolve, you may find that some attributes become more relevant for analysis. At this point, you may consider structuring your JSON data incrementally, extracting only the fields that are frequently queried or required for compliance.

This gradual structuring process aligns with the Data Vault’s design principles. Over time, your semi-structured data will naturally become more organized as business requirements clarify which fields are necessary for analytics or reporting. You can continue to leave less critical fields in JSON format, maintaining flexibility while optimizing performance where it matters.

Privacy and Security Considerations with JSON in Satellites

One important consideration when storing JSON data in the Satellite is ensuring compliance with data privacy regulations such as GDPR. It’s essential to classify personal or sensitive data in the JSON payload and handle it appropriately.

In cases where sensitive data is involved, you may choose to split your Satellites by data classification, isolating high-sensitivity fields into their own Satellite tables with stricter access controls. Alternatively, you can extract and separate specific fields that require special handling and leave the rest in JSON format.

Working with JSON Arrays and Nested Objects

JSON data often includes nested structures, such as arrays, that add complexity to handling data in a relational model. For instance, customer records might contain an array of phone numbers or addresses. In these cases, Snowflake offers tools for managing nested data without requiring extensive restructuring.

One option is to store the entire JSON array as-is within a JSON attribute in the Satellite. For example, a JSON array containing multiple phone numbers could be stored directly in the Satellite, eliminating the need for a separate table to manage multi-active data. This approach provides a simpler alternative to the traditional multi-active Satellite design.

Snowflake allows you to work with nested JSON data using its FLATTEN function, which simplifies querying nested structures without requiring complex joins or data transformations. This method can be particularly useful when dealing with highly nested data, especially in real-time applications.

Virtualizing JSON Access in Snowflake

Snowflake’s JSON handling capabilities allow you to query JSON fields dynamically, making it possible to virtualize the access to JSON attributes in your Satellite tables. Virtual views can be used to transform JSON data for reporting without needing to physically extract every field into columns.

This approach offers significant flexibility. By keeping the JSON data in its original form, you avoid unnecessary data transformations and can leverage Snowflake’s powerful JSON functions to create reports on demand. Virtualization is an effective way to simplify your data pipeline, particularly when dealing with complex JSON structures.

Conclusion

Storing JSON in the Data Vault Satellite layer on Snowflake is a sound approach, especially when dealing with semi-structured or complex data. By extracting only the fields that are critical and leaving the rest in JSON format, you balance flexibility with efficiency. Snowflake’s support for JSON makes it possible to work with this data in real-time, leveraging virtual views to keep your pipeline agile.

Consider implementing a mix of JSON storage and relational attributes, structuring your data incrementally as your business needs evolve. With the right approach, Snowflake’s JSON capabilities allow you to handle diverse data types efficiently, supporting both compliance requirements and analytics needs.

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