首页/数据分析/tracing-upstream-lineage
T

tracing-upstream-lineage

by @astronomerv1.0.0
0.0(0)

Trace upstream data lineage. Use when the user asks where data comes from, what feeds a table, upstream dependencies, data sources, or needs to understand data origins.

Data LineageData GovernanceETL MonitoringData QualityData ProvenanceGitHub
安装方式
npx skills add astronomer/agents --skill tracing-upstream-lineage
compare_arrows

Before / After 效果对比

0

description 文档


name: tracing-upstream-lineage description: Trace upstream data lineage. Use when the user asks where data comes from, what feeds a table, upstream dependencies, data sources, or needs to understand data origins.

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:

  1. 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
  2. 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
  3. 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:

  1. Find the column in the target table schema
  2. Search DAG source code for references to that column name
  3. 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

Source Details

| Source | Type | Connection | Freshness | Owner | |--------|------|------------|-----------|-------| | raw.orders | Table | Internal | 2h ago | data-team | | Salesforce | API | salesforce_conn | Real-time | sales-ops |

Transformation Chain

Describe how data flows and transforms:

  1. Raw data lands in raw.orders via Salesforce API sync
  2. DAG transform_orders cleans and dedupes into stg.orders
  3. DAG build_order_facts joins with dimensions into fct.orders

Data Quality Implications

  • Single points of failure?
  • Stale upstream sources?
  • Complex transformation chains that could break?

Related Skills

  • Check source freshness: checking-freshness skill
  • Debug source DAG: debugging-dags skill
  • Trace downstream impacts: tracing-downstream-lineage skill
  • Add manual lineage annotations: annotating-task-lineage skill
  • Build custom lineage extractors: creating-openlineage-extractors skill

forum用户评价 (0)

发表评价

效果
易用性
文档
兼容性

暂无评价,来写第一条吧

统计数据

安装量417
评分0.0 / 5.0
版本1.0.0
更新日期2026年3月16日
对比案例0 组

用户评分

0.0(0)
5
0%
4
0%
3
0%
2
0%
1
0%

为此 Skill 评分

0.0

兼容平台

🔧Claude Code

时间线

创建2026年3月16日
最后更新2026年3月16日