tracing-upstream-lineage

作者： astronomer

追蹤上游資料血緣，以識別提供表格或欄位的來源、DAG 及相依性。支援三種目標類型：表格、欄位與 DAG；透過 Airflow DAG 原始碼與任務檢查來找出產生管線。處理 SQL 來源（FROM 子句）、外部系統（S3、Postgres、Salesforce、HTTP API）及檔案型來源；遞迴追蹤上游鏈。包含透過 DAG 程式碼中的直接對應、轉換與聚合進行欄位層級的血緣追蹤...

npx skills add https://github.com/astronomer/agents --skill tracing-upstream-lineage

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Upstream Lineage: Sources

Trace the origins of data - answer "Where does this data come from?"

Lineage Investigation

Step 1: Identify the Target Type

Determine what we're tracing:

Table: Trace what populates this table
Column: Trace where this specific column comes from
DAG: Trace what data sources this DAG reads from

Step 2: Find the Producing DAG

Tables are typically populated by Airflow DAGs. Find the connection:

Search DAGs by name: Use af dags list and look for DAG names matching the table name
- load_customers -> customers table
- etl_daily_orders -> orders table
Explore DAG source code: Use af dags source <dag_id> to read the DAG definition
- Look for INSERT, MERGE, CREATE TABLE statements
- Find the target table in the code
Check DAG tasks: Use af tasks list <dag_id> to see what operations the DAG performs

On Astro

If you're running on Astro, the Lineage tab in the Astro UI provides visual lineage exploration across DAGs and datasets. Use it to quickly trace upstream dependencies without manually searching DAG source code.

On OSS Airflow

Use DAG source code and task logs to trace lineage (no built-in cross-DAG UI).

Step 3: Trace Data Sources

From the DAG code, identify source tables and systems:

SQL Sources (look for FROM clauses):

# In DAG code:
SELECT * FROM source_schema.source_table  # <- This is an upstream source

External Sources (look for connection references):

S3Operator -> S3 bucket source
PostgresOperator -> Postgres database source
SalesforceOperator -> Salesforce API source
HttpOperator -> REST API source

File Sources:

CSV/Parquet files in object storage
SFTP drops
Local file paths

Step 4: Build the Lineage Chain

Recursively trace each source:

TARGET: analytics.orders_daily
    ^
    +-- DAG: etl_daily_orders
            ^
            +-- SOURCE: raw.orders (table)
            |       ^
            |       +-- DAG: ingest_orders
            |               ^
            |               +-- SOURCE: Salesforce API (external)
            |
            +-- SOURCE: dim.customers (table)
                    ^
                    +-- DAG: load_customers
                            ^
                            +-- SOURCE: PostgreSQL (external DB)

Step 5: Check Source Health

For each upstream source:

Tables: Check freshness with the checking-freshness skill
DAGs: Check recent run status with af dags stats
External systems: Note connection info from DAG code

Lineage for Columns

When tracing a specific column:

Find the column in the target table schema
Search DAG source code for references to that column name
Trace through transformations:
- Direct mappings: source.col AS target_col
- Transformations: COALESCE(a.col, b.col) AS target_col
- Aggregations: SUM(detail.amount) AS total_amount

Output: Lineage Report

Summary

One-line answer: "This table is populated by DAG X from sources Y and Z"

Lineage Diagram

[Salesforce] --> [raw.opportunities] --> [stg.opportunities] --> [fct.sales]
                        |                        |
                   DAG: ingest_sfdc         DAG: transform_sales