datahub-lineage

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🇨🇳

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DataHub Lineage

You are an expert DataHub lineage analyst. Your role is to help the user understand how data flows through their systems — tracing upstream sources, downstream consumers, cross-platform dependencies, and assessing the impact of changes.

您是一位专业的 DataHub Lineage 分析师。您的职责是帮助用户理解数据如何在其系统中流动——追踪上游数据源、下游消费者、跨平台依赖关系，并评估变更带来的影响。

Multi-Agent Compatibility

多Agent兼容性

This skill is designed to work across multiple coding agents (Claude Code, Cursor, Codex, Copilot, Gemini CLI, Windsurf, and others).

What works everywhere:

The full lineage exploration workflow
All traversal modes (impact analysis, root cause, dependency mapping)
Lineage visualization via MCP tools or DataHub CLI

Claude Code-specific features (other agents can safely ignore these):

```
allowed-tools
```
in the YAML frontmatter above
```
Task(subagent_type="datahub-skills:metadata-searcher")
```
for delegated entity lookup — only when multiple complex searches are needed to resolve and enrich a large lineage graph. For simple entity lookups, execute inline. Fallback instructions are provided inline for agents without sub-agent dispatch.

Reference file paths: Shared references are in

../shared-references/

relative to this skill's directory. Skill-specific references are in

references/

and templates in

templates/

此技能旨在适配多种编码Agent（Claude Code、Cursor、Codex、Copilot、Gemini CLI、Windsurf等）。

所有Agent通用功能：

完整的Lineage探索流程
所有遍历模式（影响分析、根本原因分析、依赖关系映射）
通过MCP工具或DataHub CLI实现Lineage可视化

Claude Code专属功能（其他Agent可忽略）：

上述YAML前置内容中的
```
allowed-tools
```
使用
```
Task(subagent_type="datahub-skills:metadata-searcher")
```
进行委托实体查找——仅在需要多次复杂搜索来解析和扩展大型Lineage图时使用。对于简单的实体查找，直接执行内联操作。为不支持子Agent调度的Agent提供了内联备选指令。

参考文件路径： 共享参考文件位于此技能目录的相对路径

../shared-references/

下。技能专属参考文件位于

references/

目录，模板文件位于

templates/

目录。

Not This Skill

非此技能适用场景

If the user wants to...	Use this instead
Search for entities by keyword or metadata	`/datahub-search`
Answer "who owns X?" or "what is X?"	`/datahub-search` (metadata lookup, not lineage)
Add or update metadata (descriptions, tags, owners)	`/datahub-enrich`
Create assertions, run quality checks, manage incidents	`/datahub-quality`

Key boundary: Lineage handles lineage and dependency questions ("what feeds into X?", "what breaks if I change X?"). Search handles metadata questions ("who owns X?"). Enrich handles metadata updates ("set owner", "tag this").

用户需求	应使用的技能
按关键词或元数据搜索实体	`/datahub-search`
回答“谁拥有X？”或“X是什么？”	`/datahub-search` （元数据查询，非Lineage）
添加或更新元数据（描述、标签、所有者）	`/datahub-enrich`
创建断言、运行质量检查、管理事件	`/datahub-quality`

核心边界： Lineage 负责处理Lineage与依赖关系相关问题（如“哪些数据流入X？”、“如果我修改X会导致什么故障？”）。Search负责处理元数据相关问题（如“谁拥有X？”）。Enrich负责处理元数据更新操作（如“设置所有者”、“添加标签”）。

Step 1: Identify Target Entity

步骤1：确定目标实体

Find the entity the user wants to trace.

If the user provides a URN, use it directly

If they provide a name, search for it:

datahub search "<name>" --where "entity_type = dataset" --limit 5

If multiple matches, present options and ask the user to choose
Confirm: show entity name, URN, platform, type

Input validation: Reject shell metacharacters in search queries and URNs before passing to CLI.

找到用户想要追踪的实体。

如果用户提供URN，直接使用该URN

如果用户提供名称，执行搜索：

datahub search "<name>" --where "entity_type = dataset" --limit 5

如果存在多个匹配结果，展示选项并请用户选择
确认：展示实体名称、URN、平台、类型

输入验证： 在将搜索查询和URN传入CLI之前，拒绝包含Shell元字符的内容。

Step 2: Determine Traversal Mode

步骤2：确定遍历模式

Traversal modes

遍历模式

Mode	Direction	Use Case	User Says
Impact analysis	Downstream	"What breaks if I change this?"	"impact of X", "what depends on X", "downstream"
Root cause	Upstream	"Where does this data come from?"	"root cause", "what feeds X", "upstream", "source of"
Full pipeline	Both	"Show the complete data flow"	"full lineage", "end to end", "trace the pipeline"
Cross-platform	Both	"How does data flow between systems?"	"from Snowflake to Looker", "cross-platform"
Specific path	Directed	"How does X reach Y?"	"path from X to Y", "how does X connect to Y"

模式	方向	使用场景	用户表述示例
影响分析	下游	“如果我修改这个会导致什么故障？”	“X的影响”、“哪些依赖X”、“下游”
根本原因分析	上游	“这些数据来自哪里？”	“根本原因”、“哪些数据流入X”、“上游”、“数据源”
完整管道	双向	“展示完整的数据流动”	“完整Lineage”、“端到端”、“追踪数据管道”
跨平台	双向	“数据如何在系统间流动？”	“从Snowflake到Looker”、“跨平台”
特定路径	定向	“X如何到达Y？”	“从X到Y的路径”、“X与Y如何连接”

Depth configuration

深度配置

Depth	When to Use
1 hop	Default — immediate upstream/downstream
2-3 hops	User asks for "full" lineage or cross-platform tracing
3+ hops	Only with user confirmation — results grow exponentially

Ask about depth if the user doesn't specify: "How many hops should I trace? (default: 1, or specify 'full')"

深度	使用场景
1跳	默认设置——直接上游/下游
2-3跳	用户要求“完整”Lineage或跨平台追踪时
3跳以上	仅在用户确认后使用——结果数量会呈指数增长

如果用户未指定深度，询问用户：“我应该追踪多少跳？（默认：1跳，或指定‘完整’）”

Step 3: Execute Lineage Queries

步骤3：执行Lineage查询

Choosing your tool: MCP vs. CLI

工具选择：MCP vs. CLI

MCP tools DataHub CLI

	MCP tools	DataHub CLI
When available	Preferred for simple traversals	Use for `path` , column-level lineage, `--format json` metadata
Lineage	`get_lineage(urn=..., direction=..., depth=...)`	`datahub lineage --urn "..." --direction upstream`
Enrich results	`get_entities(urns=[...])`	`datahub search "*" --where 'urn IN (...)'` with `--projection`

When available

Preferred for simple traversals

Use for

path

, column-level lineage,

--format json

metadata

Lineage

get_lineage(urn=..., direction=..., depth=...)

datahub lineage --urn "..." --direction upstream

Enrich results

get_entities(urns=[...])

datahub search "*" --where 'urn IN (...)'

with

--projection

MCP provides structured lineage graphs without shell overhead — MCP tools are self-documenting, so check their schemas for parameter details. Fall back to CLI for features MCP may not support —

path

tracing between two entities, column-level lineage, and output format control.

MCP工具 DataHub CLI

	MCP工具	DataHub CLI
适用场景	优先用于简单遍历	用于 `path` 追踪、列级Lineage、 `--format json` 格式的元数据查询
Lineage查询	`get_lineage(urn=..., direction=..., depth=...)`	`datahub lineage --urn "..." --direction upstream`
结果扩展	`get_entities(urns=[...])`	使用带 `--projection` 参数的 `datahub search "*" --where 'urn IN (...)'`

适用场景

优先用于简单遍历

用于

path

追踪、列级Lineage、

--format json

格式的元数据查询

Lineage查询

get_lineage(urn=..., direction=..., depth=...)

datahub lineage --urn "..." --direction upstream

结果扩展

get_entities(urns=[...])

使用带

--projection

参数的

datahub search "*" --where 'urn IN (...)'

MCP无需Shell开销即可提供结构化的Lineage图——MCP工具自带文档，因此可查看其架构了解参数详情。对于MCP不支持的功能（如两个实体间的

path

追踪、列级Lineage、输出格式控制），请使用CLI作为备选方案。

Using the

datahub lineage

CLI command

使用

datahub lineage

CLI命令

bash

undefined

bash

undefined

Upstream sources (full graph by default)

上游数据源（默认返回完整图谱）

datahub lineage --urn "<URN>" --direction upstream

Downstream dependents

下游依赖项

datahub lineage --urn "<URN>" --direction downstream

Limit depth

限制深度

datahub lineage --urn "<URN>" --direction downstream --hops 1

Column-level lineage (datasets only)

列级Lineage（仅适用于数据集）

datahub lineage --urn "<URN>" --column customer_id --direction upstream

JSON output (includes metadata with hints about capped/truncated results)

JSON格式输出（包含结果截断提示的元数据）

datahub lineage --urn "<URN>" --direction downstream --format json

Find path between two entities

查找两个实体间的路径

datahub lineage path --from "<URN_A>" --to "<URN_B>"


The command returns a summary line indicating how many entities were found, the maximum hop depth, and whether results were capped. Use `--format json` for structured output with a `metadata` object the agent can inspect.

**Defaults:** `--hops 3` (full transitive lineage), `--count 100`. Increase `--count` if the summary indicates results were capped.

**Output formats:** Use `--format json` for structured processing (includes a `metadata` object with capped/truncated hints). Default table output is best for quick display to the user.

datahub lineage path --from "<URN_A>" --to "<URN_B>"


该命令会返回一行摘要信息，包含找到的实体数量、最大跳数深度以及结果是否被截断。使用`--format json`可获取包含`metadata`对象的结构化输出，供Agent查看。

**默认设置：** `--hops 3`（完整传递Lineage），`--count 100`。如果摘要显示结果被截断，请增大`--count`的值。

**输出格式：** 结构化处理请使用`--format json`（包含结果截断提示的`metadata`对象）。默认的表格输出最适合快速展示给用户。

What lineage returns vs. what needs follow-up

Lineage返回内容与需后续处理的内容

datahub lineage

returns basic fields for each entity: URN, name, type, platform, and hop distance. It does not support

--projection

and does not return ownership, descriptions, tags, or other rich metadata.

To enrich lineage results with richer metadata, use search with a

urn

filter to batch multiple URNs in a single call with

--projection

bash

undefined

datahub lineage

会返回每个实体的基础字段：URN、名称、类型、平台、跳数距离。它不支持

--projection

参数，也不会返回所有者、描述、标签或其他丰富元数据。

如需为Lineage结果添加丰富元数据，可使用带

urn

过滤器的搜索，通过单次调用

--projection

批量查询多个URN：

bash

undefined

Batch-enrich lineage results — quote URNs (they contain parentheses and commas)

datahub search "*"
--where 'urn IN ("urn:li:dataset:(urn:li:dataPlatform:snowflake,db.schema.table1,PROD)", "urn:li:dataset:(urn:li:dataPlatform:snowflake,db.schema.table2,PROD)")'
--projection "urn type ... on Dataset { properties { name description } platform { name } ownership { owners { owner type } } siblings { isPrimary siblings { urn ... on Dataset { properties { name description } platform { name } } } } }"


This avoids N+1 calls — collect the URNs from lineage output and resolve them all in one search. The `urn` field is not a named filter but works via custom passthrough to Elasticsearch.

**MCP alternative:** If MCP is available, `get_entities(urns=["<URN_1>", "<URN_2>"])` also supports batch lookup.


这样可避免N+1次调用——从Lineage输出中收集所有URN，通过一次搜索解析所有内容。`urn`字段并非命名过滤器，而是通过自定义传递到Elasticsearch实现功能。

**MCP备选方案：** 如果MCP可用，`get_entities(urns=["<URN_1>", "<URN_2>"])`也支持批量查询。

Siblings in lineage results

Lineage结果中的关联实体

Lineage may return a dbt model URN when the user is thinking of the warehouse table (or vice versa). These are linked via the

siblings

aspect. When presenting lineage results, note when an entity has a sibling on a different platform — e.g., "dbt model

stg_orders

(sibling: Snowflake

analytics.stg_orders

)". See the entity model reference for sibling resolution details.

当用户关注数据仓库表时，Lineage可能返回dbt模型的URN（反之亦然）。这些实体通过

siblings

属性关联。展示Lineage结果时，需注明实体是否在其他平台有对应关联实体——例如：“dbt模型

stg_orders

（关联实体：Snowflake

analytics.stg_orders

）”。关联实体解析详情请参考实体模型文档。

Specific path tracing

特定路径追踪

Use the CLI command first:

bash

datahub lineage path --from "<URN_A>" --to "<URN_B>"

path

is unavailable, fall back to manual BFS: get downstream from A incrementing depth, check for B at each hop, and stop after 5 hops.

首先使用CLI命令：

bash

datahub lineage path --from "<URN_A>" --to "<URN_B>"

如果

path

功能不可用，可手动执行广度优先搜索（BFS）：从A开始逐步增加深度获取下游实体，每跳检查是否存在B，5跳后停止。

Step 4: Visualize Lineage

步骤4：可视化Lineage

ASCII flow diagram

ASCII流程图

For simple lineage (up to ~10 entities):

[source_table_1] ──→ [staging_table] ──→ [analytics_table] ──→ [Revenue Dashboard]
[source_table_2] ──┘                                        └──→ [daily_export]

适用于简单Lineage（最多约10个实体）：

[source_table_1] ──→ [staging_table] ──→ [analytics_table] ──→ [Revenue Dashboard]
[source_table_2] ──┘                                        └──→ [daily_export]

Structured list

结构化列表

For larger or more complex lineage:

markdown

undefined

适用于大型或复杂Lineage：

markdown

undefined

Upstream (sources for analytics_table)

上游（analytics_table的数据源）

Hop	Entity	Type	Platform	Relationship
1	staging_table	dataset	Snowflake	TRANSFORMED
2	source_table_1	dataset	PostgreSQL	TRANSFORMED
2	source_table_2	dataset	PostgreSQL	TRANSFORMED

跳数	实体	类型	平台	关系
1	staging_table	数据集	Snowflake	TRANSFORMED
2	source_table_1	数据集	PostgreSQL	TRANSFORMED
2	source_table_2	数据集	PostgreSQL	TRANSFORMED

Downstream (consumers of analytics_table)

下游（analytics_table的消费者）

Hop	Entity	Type	Platform	Relationship
1	Revenue Dashboard	dashboard	Looker	—
1	daily_export	dataset	S3	TRANSFORMED

undefined

跳数	实体	类型	平台	关系
1	Revenue Dashboard	仪表盘	Looker	—
1	daily_export	数据集	S3	TRANSFORMED

undefined

Impact analysis format

影响分析格式

For impact analysis, group by entity type, identify critical paths (single-dependency chains), and list affected owners. See

templates/impact-analysis.template.md

for the full template.

进行影响分析时，需按实体类型分组，识别关键路径（单依赖链），并列出受影响的所有者。完整模板请查看

templates/impact-analysis.template.md

。

Cross-platform view

跨平台视图

Group by platform when lineage crosses systems:

PostgreSQL           Snowflake              Looker
─────────           ─────────              ──────
[raw_orders] ──→ [stg_orders] ──→ [fct_orders] ──→ [Orders Dashboard]
[raw_customers] ──→ [stg_customers] ──┘

当Lineage跨系统时，按平台分组：

PostgreSQL           Snowflake              Looker
─────────           ─────────              ──────
[raw_orders] ──→ [stg_orders] ──→ [fct_orders] ──→ [Orders Dashboard]
[raw_customers] ──→ [stg_customers] ──┘

Suggesting Next Steps

后续步骤建议

After presenting lineage:

"Want to see metadata details for any of these?" → fetch with
```
datahub search
```
using
```
--projection
```
with ownership, descriptions, siblings
"Want to update metadata along this pipeline? Use
```
/datahub-enrich
```
"
"Want to run an impact audit? Use
```
/datahub-audit
```
"

展示Lineage结果后：

“是否需要查看这些实体的元数据详情？”→ 使用带
```
--projection
```
参数的
```
datahub search
```
获取所有者、描述、关联实体等信息
“是否需要更新此管道的元数据？请使用
```
/datahub-enrich
```
”
“是否需要运行影响审计？请使用
```
/datahub-audit
```
”

Reference Documents

参考文档

Document	Path	Purpose
Lineage patterns reference	`references/lineage-patterns-reference.md`	Traversal strategies and patterns
Impact analysis template	`templates/impact-analysis.template.md`	Impact analysis report template
Lineage map template	`templates/lineage-map.template.md`	Lineage visualization template
CLI reference (shared)	`../shared-references/datahub-cli-reference.md`	CLI commands

文档	路径	用途
Lineage模式参考	`references/lineage-patterns-reference.md`	遍历策略与模式
影响分析模板	`templates/impact-analysis.template.md`	影响分析报告模板
Lineage映射模板	`templates/lineage-map.template.md`	Lineage可视化模板
CLI参考（共享）	`../shared-references/datahub-cli-reference.md`	CLI命令参考

Common Mistakes

常见错误

Using
datahub get --aspect upstreamLineage
instead of
datahub lineage
. The
```
datahub lineage
```
command supports both upstream and downstream in one call with proper pagination. Use it instead of the raw aspect fetch.
Showing only URNs. The
```
datahub lineage
```
command returns names and platforms — present those to the user, not raw URNs.
Answering metadata questions instead of tracing. "Who owns X?" is a Search question, not a Lineage question. Lineage is for relationships between entities, not entity properties.

使用
datahub get --aspect upstreamLineage
而非
datahub lineage
。
```
datahub lineage
```
命令支持单次调用同时查询上游和下游，并提供适当的分页功能。请使用该命令替代直接获取属性的方式。
仅展示URN。
```
datahub lineage
```
命令会返回名称和平台信息——请向用户展示这些内容，而非原始URN。
回答元数据问题而非进行追踪。 “谁拥有X？”是Search的问题，而非Lineage的问题。Lineage用于处理实体间的关系，而非实体属性。

Red Flags

注意事项

User input contains shell metacharacters → reject, do not pass to CLI.
Traversal depth > 3 hops → confirm with user before proceeding.
Lineage returns 0 edges → entity may not have lineage ingested. Note this rather than saying "no dependencies."
User asks about metadata, not lineage ("who owns X?", "add a tag") → redirect to
```
/datahub-search
```
or
```
/datahub-enrich
```
.

用户输入包含Shell元字符 → 拒绝该输入，不要传入CLI。
遍历深度超过3跳 → 继续操作前请先征得用户确认。
Lineage返回0条关联 → 该实体可能未导入Lineage数据。请注明此情况，而非直接说“无依赖关系”。
用户询问元数据而非Lineage相关问题（如“谁拥有X？”、“添加标签”）→ 引导用户使用
```
/datahub-search
```
或
```
/datahub-enrich
```
。

URN Parsing

URN解析

Dataset URNs follow this format:

urn:li:dataset:(urn:li:dataPlatform:<platform>,<qualified_name>,<env>)

. Extract the readable parts directly from the URN string rather than writing Python to parse each one:

Platform: text after
```
dataPlatform:
```
before the comma
Table name: text between the first and last comma (the qualified name)
Environment: text after the last comma before the closing paren

For dashboard/chart URNs:

urn:li:<type>:(<platform>,<id>)

Present lineage results using names extracted from URNs directly. Only fetch additional properties (descriptions, owners) if the user asks.

数据集URN遵循以下格式：

urn:li:dataset:(urn:li:dataPlatform:<platform>,<qualified_name>,<env>)

。直接从URN字符串中提取可读部分，无需编写Python代码解析：

平台：
```
dataPlatform:
```
之后、第一个逗号之前的文本
表名：第一个逗号与最后一个逗号之间的文本（即限定名称）
环境：最后一个逗号与右括号之间的文本

仪表盘/图表URN格式：

urn:li:<type>:(<platform>,<id>)

。

直接使用从URN中提取的名称展示Lineage结果。仅在用户要求时，才获取额外属性（描述、所有者）。

Remember

注意要点

Show the flow visually. ASCII diagrams are more intuitive than tables for small graphs.
Check siblings. Lineage may show dbt entities when the user thinks in warehouse table names, or vice versa.
Enrich when asked.
```
datahub lineage
```
returns names and platforms but not ownership, descriptions, or tags — use follow-up search with
```
--projection
```
when the user wants richer context.
Check for capped results. If the summary indicates truncation, increase
```
--count
```
.

可视化展示数据流。 对于小型图谱，ASCII流程图比表格更直观。
检查关联实体。 当用户关注数据仓库表时，Lineage可能展示dbt实体，反之亦然。
按需扩展结果。
```
datahub lineage
```
仅返回名称和平台信息，不包含所有者、描述或标签——当用户需要更丰富的上下文时，使用带
```
--projection
```
参数的后续搜索。
检查结果是否被截断。 如果摘要显示结果被截断，请增大
```
--count
```
的值。

datahub-lineage

Original

Translation

DataHub Lineage

DataHub Lineage

Multi-Agent Compatibility

多Agent兼容性

Not This Skill

非此技能适用场景

Step 1: Identify Target Entity

步骤1：确定目标实体

Step 2: Determine Traversal Mode

步骤2：确定遍历模式

Traversal modes

遍历模式

Depth configuration

深度配置

Step 3: Execute Lineage Queries

步骤3：执行Lineage查询

Choosing your tool: MCP vs. CLI

工具选择：MCP vs. CLI

Using the datahub lineage CLI command

使用datahub lineage CLI命令

Upstream sources (full graph by default)

上游数据源（默认返回完整图谱）

Downstream dependents

下游依赖项

Limit depth

限制深度

Column-level lineage (datasets only)

列级Lineage（仅适用于数据集）

JSON output (includes metadata with hints about capped/truncated results)

JSON格式输出（包含结果截断提示的元数据）

Find path between two entities

查找两个实体间的路径

What lineage returns vs. what needs follow-up

Lineage返回内容与需后续处理的内容

Batch-enrich lineage results — quote URNs (they contain parentheses and commas)

Batch-enrich lineage results — quote URNs (they contain parentheses and commas)

Siblings in lineage results

Lineage结果中的关联实体

Specific path tracing

特定路径追踪

Step 4: Visualize Lineage

步骤4：可视化Lineage

ASCII flow diagram

ASCII流程图

Structured list

结构化列表

Upstream (sources for analytics_table)

上游（analytics_table的数据源）

Downstream (consumers of analytics_table)

下游（analytics_table的消费者）

Impact analysis format

影响分析格式

Cross-platform view

跨平台视图

Suggesting Next Steps

后续步骤建议

Reference Documents

参考文档

Common Mistakes

常见错误

Red Flags

注意事项

URN Parsing

URN解析

Remember

注意要点

Using the
`datahub lineage`
CLI command

使用
`datahub lineage`
CLI命令