As a data engineer, I’ve spent years wrestling with how to best document and manage data lineage in corporate environments. Getting it right is critical; knowing where your data comes from and how it flows ensures trust, compliance, and efficiency. After experimenting with various formats, I’ve found that YAML stands out as a superior choice for data lineage best practices, especially when compared to spreadsheets and JSON. Here’s why, along with a straightforward way to make the switch.

Data serialization is the process of converting data into a format, such as text or binary, that can be stored, transmitted, and reconstructed later. YAML, a human-readable data serialization format, shines here. Its simple syntax, built on indentation and key-value pairs, is easy to write and understand. Unlike other formats, YAML’s clean structure makes it a breeze for large language models (LLMs) to parse accurately, which is key for automating data lineage tasks.

Spreadsheets like Microsoft Excel or Google Sheets are common in corporate settings, but they have serious flaws that hinder effective data lineage documentation:

• Empty cells bloat files: Their tabular structure stores empty cells, inflating file sizes unnecessarily.
• Character limits: Cells cap at about 32,000 characters, making them impractical for storing code or detailed metadata.
• Access issues: LLMs often lack permissions to read organizational folders, complicating automation.
• No versioning: Spreadsheets don’t natively track changes, making lineage updates messy.
• Confusing formulas: Complex cell formulas trip up LLMs, unlike YAML’s straightforward structure.
• Inconsistent formats: Varied formats (.xlsx, .csv) create compatibility issues, while YAML is standardized.
• Interlinked worksheets: Linked sheets create dependencies that LLMs struggle to follow.
• Cluttered code: Large code chunks in cells obscure data clarity.
• Older LLM struggles: Legacy LLMs misinterpret spreadsheet structures.
• Scattered context: Multiple worksheets fragment data, disrupting LLM understanding, unlike YAML’s unified format.

Switching to YAML sidesteps these issues, providing a clean, LLM-friendly format for documenting data lineage.

JSON is a solid serialization format, but it has its own drawbacks compared to YAML for data lineage:

• Hard to read: JSON’s code-like syntax feels less intuitive than YAML’s clean layout.
• Nested complexity: Its hierarchy is tough to follow visually compared to YAML’s indentation.
• Strict syntax: JSON requires precise braces and brackets, which can lead to errors, while YAML is more forgiving.
• No comments: JSON lacks native comment support, limiting documentation, unlike YAML.
• Verbose structure: Repeated keys bloat JSON files, while YAML stays compact.
• Multi-line string issues: JSON struggles with multi-line strings, unlike YAML, which handles them clearly.
• No reuse features: JSON repeats data without anchors, while YAML reduces redundancy.
• Ambiguous types: JSON lacks built-in support for data types like dates, unlike YAML’s clarity.
• No schema validation: JSON risks inconsistent data, while YAML supports schema standards.
• Unordered sequences: JSON can jumble data order, while YAML preserves sequence clarity.

For data lineage, YAML’s readability and flexibility make it a better fit for both humans and LLMs.

Given these flaws, YAML emerges as a top choice for documenting data lineage in corporations. Its structured yet simple format ensures LLMs can process lineage data accurately, while humans can easily review and maintain it. This balance is crucial for implementing data lineage best practices that scale across teams and systems.

Switching from spreadsheets to YAML is easier than you might think. Here’s a quick guide:

• Export to CSV: Save your Excel or Google Sheets file as a CSV to strip away complex formulas, formatting, and styling. This simplifies the data for LLM processing.
• Trim unused worksheets: Remove any irrelevant worksheets from the CSV to focus on lineage-relevant data.
• Feed to an LLM: Upload the CSV to your LLM with a clear prompt: “Convert this CSV to YAML. Use clear key-value pairs and nested structures where needed. Make headers keys, omit empty cells, preserve data types, and output clean, readable YAML.”
• Review the output: Check the YAML file for accuracy and proper formatting.

For data lineage best practices, YAML outshines spreadsheets and JSON in corporate settings. Its readability, flexibility, and LLM compatibility make it ideal for documenting how data flows through your organization. By converting spreadsheets to YAML, you’ll streamline lineage tracking, reduce errors, and empower both teams and LLMs to work smarter. Give it a try, your data governance will thank you.