Unlocking Semi-Structured Data in Snowflake: Mastering the FLATTEN Function

Data in the real world often comes packaged in a mess. Instead of neat rows and columns, you might have complex, semi-structured data lurking within your Snowflake tables. These structures could be JSON objects, arrays, or a mix of both – all bundled within a single column. The Snowflake FLATTEN function is your key to untangling this complexity.

What is FLATTEN?

FLATTEN is a robust table function in Snowflake. Its primary job is to explode or unnest compound data structures (VARIANT, OBJECT, ARRAY) into multiple rows. Imagine it as a tool that takes a nested structure and stretches it out into a more traditional, relational format.

Why Flatten?

1. Normalization: Semi-structured data can be tricky to analyze directly. By flattening it, you normalize the data, making it easier to query and join with other tables.
2. Extracting Insights: Valuable information lies within those nested structures. FLATTEN lets you access individual elements, revealing hidden insights previously locked away.
3. Flexibility: The FLATTEN function, in combination with other Snowflake tools, provides a high degree of flexibility when manipulating complex data structures.

FLATTEN in Action: A Practical Example

Suppose you have a table named ‘user_data’ with a column called ‘events’ storing JSON data like this:

JSON

{

  “user_id”: 123,

  “events”: [

    {“event_type”: “login”, “timestamp”: “2023-11-22 10:30:00”},

    {“event_type”: “purchase,” “item”: “Widget A,” “price”: 25.99}

  ]

}

Use code with caution.

content_copy

We can flatten the ‘events’ array:

SQL

SELECT 

  user_id, 

  value:event_type::string AS event_type,

  value:timestamp::string AS timestamp

FROM user_data, LATERAL FLATTEN(INPUT => events); 

Use code with caution.

content_copy

This query produces a result where each event within the array becomes a separate row, making it easier to analyze event data granularly.

Key Points to Remember

• Lateral Joins: FLATTEN usually complements LATERAL joins, allowing you to correlate data from the flattened rows with the original row.
• Path: For deeply nested structures, provide a path within the FLATTEN function to target the specific elements you want to expand.
• Error Handling: Consider using the OUTER=>TRUE argument in FLATTEN to handle cases where arrays or objects might be empty.

Beyond the Basics

While we’ve focused on the core FLATTEN function, Snowflake offers other related tasks like:

• ARRAY_FLATTEN: Specifically designed to flatten arrays of arrays.
• FLATTEN_INPUT: This lets you flatten multiple input expressions at once.

Let’s Get Meta!

If you want a more engaging blog post, feel free to add some real-world analogies or scenarios. For example, consider how FLATTEN is like unpacking a suitcase or sorting through a box of mixed items.

In Conclusion

FLATTEN is an essential weapon in your Snowflake data-wrangling arsenal. By understanding how it works, you can transform complex semi-structured data into a format ready for analysis and insights.