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Data pipeline reviews
What breaking a table actually breaks. Every downstream user of a data asset, and the decisions that depend on it.
Why this is memory-only. Data lineage shows technical dependencies, but not every person, workflow, and decision that quietly relies on an asset. A persistent memory layer preserves those relationships through the conversations where they were established.
Side by side: token usage, with and without Paperbase
Memory onlyWithout Paperbase
Not recoverable from lineage artifacts alone
- Inspect the schema and technical lineage.
- Search repositories for references to the table.
- Ask data consumers what they rely on.
- Guess which dashboards or decisions will break.
With Paperbase
Persistent memory layer
- Pull the data Asset and its known Dependents.
- Pull Interactions and Decisions that rely on the asset.
- Surface downstream owners, dashboards, and commitments.
- Flag the decisions at risk if the asset changes.
Agent prompt
You are reviewing the impact of changing {{data_asset}}.
Using Paperbase:
1. Pull the asset, its dependents, owners, and downstream dashboards.
2. Pull Decisions and analyses that rely on {{data_asset}}.
3. Identify the teams and commitments affected by a schema or pipeline change.
4. Flag unknown consumers and decisions with no current owner.
Output:
- The dependency map, with owners.
- Decisions and workflows at risk.
- A migration checklist and the questions still unanswered.Placeholders in {double_braces} are inputs the agent will ask for at runtime. Give the prompt to any agent connected to Paperbase, and the rest grounds in your own memory graph.