Working With Semi-structured Data

Mastering Semi-Structured Data: Key Approaches and Best Practices

Semi-structured data, such as JSON, is increasingly common in modern data ecosystems. But how should you store and handle it? Should you store the data as-is or flatten its structure? Both approaches have unique advantages and limitations, and understanding these can help you make informed decisions based on your use cases.

Key Considerations

• Expected Data Structure: Is the schema likely to change? Are nested objects (hierarchies) present?
• Velocity & Size: How large and fast-moving is your data?
• Database Capabilities: Does your system support efficient queries and manage large datasets?
• Use Cases: What operations will you perform on the data?

Approach 1: Store Data As-Is

This method involves storing the data in its original format. It’s ideal for flexibility but has limitations:

• Pros: Quick to ingest, accommodates changing schemas, suitable for unknown operations.
• Cons: Struggles with large files and nested queries.

Approach 2: Flatten Nested Structures

Flattening the structure simplifies data querying and scalability. However, it also has trade-offs:

• Pros: Easy querying, no file size constraints, better for fixed schemas.
• Cons: Complexity in handling hierarchies, loss of schema flexibility.

Data Vault Modeling: A Flexible Solution

Data Vault modeling supports both approaches:

• Storing As-Is: Store files as non-historized links or satellites, keeping the original file in a single column. Virtual structures can be built on top.
• Flattening Before Loading: Create standard Data Vault entities while storing the original files in a Data Lake for reference.

Choosing the right strategy depends on your operational needs and database capabilities. By considering these factors, you can efficiently work with semi-structured data while optimizing performance and flexibility.

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