/datalineage-bigquery-asset-impact-analysis

name: datalineage-bigquery-asset-impact-analysis
metadata:
  version: "1.0.2"
  category: BigDataAndAnalytics
description: >-
  Analyzes the downstream impact (blast radius) using data lineage on Google Cloud when a BigQuery table or view is broken, stale, or modified.
  Identifies all downstream tables, dashboards, and processes that will be affected.
  Use when:
  - Performing a blast radius or impact analysis for a BigQuery table or view.
  - Assessing the consequences of modifying, deleting, or pausing updates to a BigQuery asset.
  - Identifying downstream dependencies (tables, dashboards, processes) of a BigQuery asset.
  Don't use for:
  - General BigQuery querying or data analysis (use BigQuery-related tools instead).
  - Non-BigQuery assets (e.g., Cloud Storage files) unless they are part of the BigQuery lineage.
  - Creating or modifying lineage links directly.

BigQuery Asset Impact Analysis

This skill guides the agent in performing a downstream impact analysis (blast radius assessment) when a BigQuery table or view is reported as broken, stale, missing, or when a user is planning maintenance and wants to know the consequences of modifying or pausing updates to an asset.

It relies primarily on the **data lineage (Knowledge Catalog) MCP Server** on Google Cloud to discover relationships between assets.

Attribution

**MUST READ**: Before executing any `gcloud`, REST API, or MCP command, you MUST read [Usage Attribution](references/attribution.md). For commands executed directly in the terminal (never in generated scripts, Terraform, or user code), prefix `gcloud` with `CLOUDSDK_METRICS_REQUEST_ATTRIBUTION="agent-skills/1.0 (skill:datalineage-bigquery-asset-impact-analysis)"` and append `agent-skills/1.0 (skill:datalineage-bigquery-asset-impact-analysis)` to `User-Agent` headers.

Prerequisites

This skill requires access to the data lineage API on Google Cloud and an active client connection to the Data Lineage MCP Server. For detailed connection configurations and tool schemas, refer to [MCP Usage](references/mcp-usage.md).

Analysis Workflow

1. Resolve the Asset's Fully Qualified Name (FQN)

• Ensure you have the correct FQN format for the BigQuery asset:
• *Format:* `bigquery:{project_id}.{dataset_id}.{table_or_view_id}`
• *Example:* `bigquery:my-prod-project.analytics.orders`

2. Determine Locations and Parent Path

Identify the locations to search and construct the Data Lineage API request:

• **Discover Asset Location**: Run the command `bq show --format=json

{project_id}:{dataset_id}` and extract the `location` field (e.g., `us-central1` or `us`). If location discovery fails due to permissions or missing tools, prompt the user for the dataset's location.

• **Set Parent Path**: Set the `parent` path using the project ID and the

MCP server's location. Consult the `DataLineageServer` tool definition to find the configured region or location (e.g., `us`). The format is: `projects/{project_id}/locations/{mcp_server_location}`.

• **Configure Search Scope**: Include the discovered asset location in the

`locations` array of the payload (e.g., `["us-central1"]` or `["us", "us-central1"]`).

3. Retrieve the Downstream Lineage Graph

Call the `DataLineageServer:search_lineage` tool to fetch downstream relationships.

• **Direction**: Set to `DOWNSTREAM`.
• **Search Parameters**: Use `max_depth = 10` and `max_process_per_link = 5`

as robust defaults.

4. Identify the Blast Radius

Traverse the returned lineage links to build the impact graph:

• **Affected Assets**: The `target` of each link represents a downstream asset

that depends on your source asset.

• **Transform Processes**: Inspect the `processes` field on each link. This

identifies the ETL pipelines, BigQuery Views, or Scheduled Queries that propagate the data.

• **Direct vs. Indirect Impact**:
• **Direct Impact (Depth 1)**: Assets directly consuming the source asset.

If a link has `dependency_type: EXACT_COPY`, mark the target as "Directly Stale / Identical Copy".

• **Indirect Impact (Depth > 1)**: Assets further down the stream that

will experience cascading stale data or failures.

5. Summarize and Format the Output

Present your findings clearly to the user using the following structure:

1. **Executive Summary**: State the total number of downstream assets affected and the maximum depth of the impact. 2. **Critical Path**: Highlight high-priority downstream assets (e.g., assets containing "prod", "dashboard", "reporting", or "master" in their names). 3. **Blast Radius Table**: A clean Markdown table listing the dependencies. You MUST include all columns:

| Downstream Asset | Transform Process | Depth | Impact Type | | :------------------------------- | :------------------------------------ | :---- | :---------- | | `bigquery:project.dataset.table` | `projects/p/locations/l/processes/proc` | 1 | Direct | | `bigquery:project.dataset.view` | `projects/p/locations/l/processes/view` | 2 | Indirect | 4. **Analysis Metadata**: Provide transparency on the parameters and boundaries of your search so the user can choose to expand them:

• **Locations Searched**: `{list_of_locations_queried}`
• **Parent Location**: `{parent_path}`
• **Depth Limit**: `{max_depth}`
• **Process per Link Limit**: `{max_process_per_link}`
• *Tip for User*: Let the user know they can request to rerun the analysis

with expanded locations or larger depth limits.

Crucial Constraints & Guardrails

1. **Interpret Empty Responses Correctly**:

• If the lineage response is empty, immediately assume that no

dependencies exist in the queried locations and report this to the user. 2. **Strictly Banned Bypasses**:

• Exclusively retrieve downstream rela