New to Claude Skills? Learn how to install them →

google on GitHub

BigQuery Asset Impact Analysis

Free

Assess downstream impacts of BigQuery assets efficiently.

by google17.6k stars on google/skills
1 views
Updated Aug 10, 2026
Get this skill

Free · Opens the source repo

What BigQuery Asset Impact Analysis does

The BigQuery Asset Impact Analysis skill is designed to help users evaluate the downstream consequences when a BigQuery table or view is modified, deleted, or marked as stale. It provides a systematic approach to performing a blast radius assessment, enabling users to understand which downstream assets—such as tables, dashboards, and processes—will be affected by changes to a specified BigQuery asset. This is particularly useful for data engineers and analysts who need to ensure data integrity and operational continuity when managing their BigQuery resources.

The skill leverages the Google Cloud Data Lineage API to discover relationships between assets, starting with the fully qualified name (FQN) of the asset in question. Users must ensure they have the correct FQN format and access to the Data Lineage MCP Server. The skill guides users through a structured workflow that includes resolving the asset's location, retrieving the downstream lineage graph, and identifying the blast radius of the asset. The output is presented in a clear format, including an executive summary, a critical path highlighting high-priority assets, and a detailed table of affected downstream dependencies.

This skill is essential for teams working within the Google Cloud ecosystem who regularly modify BigQuery assets and need to assess the impact of these changes on their data pipelines and reporting tools. By providing a clear view of dependencies, it helps prevent disruptions and ensures that stakeholders are informed about potential impacts before changes are made.

However, it is important to note that this skill is not intended for general BigQuery querying or data analysis. Users should not attempt to use it for non-BigQuery assets or for creating or modifying lineage links directly. Instead, it focuses exclusively on analyzing the impact of changes to BigQuery assets, making it a specialized tool for specific scenarios.

When to use it

Use this skill when planning changes to BigQuery tables or views and needing to assess the potential impact on downstream assets.

When not to use it

Do not use this skill for general data analysis or for assets outside the BigQuery environment.

What you can build with it

Assessing Changes Before Maintenance

Use this skill when planning maintenance on a BigQuery asset to understand its downstream impact.

Identifying Dependencies for Data Integrity

Employ this skill to identify all downstream processes and tables that may be affected by changes to a BigQuery asset.

Evaluating Risk of Deleting Assets

Before deleting a BigQuery table, use this skill to analyze which downstream assets will be impacted.

How to install BigQuery Asset Impact Analysis

View source

1. Install with the skills CLI

npx skills add google/skills/datalineage-bigquery-asset-impact-analysis --agent claude-code

2. Or install it manually

Download the skill folder and drop it into ~/.claude/skills/ for all projects, or .claude/skills/ to scope it to one repo. Restart Claude Code so it picks up the new skill.

Anthropic's agentic coding CLI, and the reference implementation of Agent Skills. Drop a skill folder into ~/.claude/skills and Claude Code loads it automatically whenever a task matches the skill's description. Claude Code docs

Inside SKILL.md

Written by google

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 Google Cloud Data Lineage (Knowledge Catalog) MCP Server to discover relationships between assets.

Prerequisites

This skill requires access to the Google Cloud Data Lineage API and an active client connection to the Data Lineage MCP Server. For detailed connection configurations and tool schemas, refer to MCP Usage.

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 AssetTransform ProcessDepthImpact Type
    bigquery:project.dataset.tableprojects/p/locations/l/processes/proc1Direct
    bigquery:project.dataset.viewprojects/p/locations/l/processes/view2Indirect
  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 relationships using the DataLineageServer:search_lineage tool.
  3. Verify Asset Existence First:
    • If bq show indicates the source table does not exist, stop and report this directly to the user. Do not attempt to guess alternative table names unless the user explicitly instructs you to do so.
  4. No Output Shortcutting or Hallucinated Artifacts:
    • Present the complete downstream blast radius table directly in your final response. Avoid telling the user you have created a separate Markdown file or artifact containing the details unless you have explicitly executed file-writing tools to create it.

Reference Directory

  • MCP Usage: Using the Google Cloud Data Lineage remote MCP server and tool preferences.

External Documentation

Frequently asked questions about BigQuery Asset Impact Analysis

Similar skills