Having recently undertaken a comprehensive documentation effort for my team's procurement data lifecycle, I thought it would be beneficial to share my methodology here. This exercise was prompted by recurring questions from our analytics team regarding data provenance and transformation logic, which were causing delays in report generation and eroding trust in our core procurement metrics. The goal was to create a living document that serves both data engineers and analysts, detailing the journey from vendor invoices in our SAP system to aggregated spend dashboards in BigQuery.
My approach was structured around three core pillars: Source System Mapping, Transformation Logic Specification, and Orchestration & Dependency Documentation. I utilized a combination of Markdown for narrative and YAML for structured metadata, stored in a version-controlled repository alongside our actual pipeline code (Airbyte configurations and dbt models). This ensures the documentation evolves with the pipelines themselves.
**1. Source System Mapping**
I began by cataloging every source system and table involved, which was more complex than anticipated due to legacy systems.
```yaml
sources:
- system: "SAP ECC"
module: "MM (Materials Management)"
critical_tables:
- name: "EKBE"
description: "Purchasing Document History"
extraction_method: "Full read via ODP with incremental delta"
refresh_frequency: "hourly"
primary_keys: ["MANDT", "EBELN", "EBELP", "ZEKKN"]
owner: "Procurement Operations Team"
sla: "Data available with 2-hour latency"
```
**2. Transformation Logic Specification**
For each dbt model, I extended the standard documentation with business context. I created a companion `_business_logic.md` file for key models, such as `int_procurement_spend_aggregated.sql`.
```sql
-- Example from int_procurement_spend_aggregated.sql
-- Business Logic:
-- 1. Currency conversion is applied using the daily rate from table 'md_exchange_rates' at the document date.
-- 2. Spend is categorized using a mapping table maintained by the procurement category manager.
-- 3. Only invoices with a status of 'Released' (REL) are considered for approved spend.
-- 4. Spend is rolled up to the 'Cost Center' and 'General Ledger Account' level.
```
**3. Orchestration & Dependency Visualization**
Using the Airbyte API and dbt DAG, I generated a simple Mermaid diagram embedded in the README to illustrate the pipeline flow. This visual has been invaluable for onboarding and impact analysis.
```mermaid
graph TD
A[SAP ECC - EKBE] -->|Airbyte Sync| B(stg_procurement_document_items);
B --> C(int_procurement_enriched);
C --> D(int_procurement_spend_aggregated);
D --> E(fct_procurement_monthly_spend);
F(ref_exchange_rates) --> C;
G(ref_spend_category_map) --> C;
```
The primary challenges were reconciling differing business definitions between teams and maintaining the discipline to update the documentation with each pull request. We addressed this by integrating a lightweight review step in our CI/CD process, where modifications to core models require a cursory update to the corresponding logic file. The outcome has been a significant reduction in ad-hoc clarification requests and a more robust, self-service data discovery experience for our analytics consumers. I am curious if others have adopted similar or alternative frameworks for documenting complex, multi-stage data pipelines within their organizations.
Extract, transform, trust
Ah, source system mapping. That's where the fun begins, isn't it? You think you know your landscape until you have to write it down, and then three people from three different departments casually mention "the old approval system" that still feeds into a staging table once a quarter.
The YAML for structured metadata is a solid move. I've found that forcing it to live right next to the pipeline code, in version control, is the only way to keep it from becoming a museum piece. My one addition to this has been a simple `last_validated_date` field in that metadata. When you're in a rush and tweak an Airbyte config, you can skip the docs. But if you see that date is from six months ago, it gives you that little pang of guilt. It's saved my bacon more than once when onboarding a new team member.
How did you handle the diagrams or data flow visuals? Did you bake those in somehow, or keep them separate? I've gone back and forth on that.
it worked on my machine