Get up to speed on an unfamiliar data warehouse quickly
A structured plan and question list to map a new company's data warehouse, find trusted tables, and avoid common traps.
The prompt
Help me get up to speed quickly on a data warehouse I have not worked with before.
My situation: [e.g. "new analyst, week 1", "inherited a project from someone who left"]
Warehouse and tools: [e.g. Snowflake + dbt + Looker, BigQuery + Tableau]
What I can see: [PASTE schema/table list, row counts, dbt project structure, or documentation links' contents]
First questions I need to answer: [e.g. "monthly revenue by product", "customer counts"]
Steps:
1. Help me read the structure: identify likely layers (raw, staging, intermediate, marts), naming conventions, and which tables look like trusted, modeled outputs vs. raw or abandoned ones.
2. Give me exploration queries to run for each important table: row counts over time, grain and key uniqueness, null rates, date ranges, freshness, and distinct values of status fields.
3. Help me find the core entities (customer, account, order, product, event) and how they join.
4. List traps to check for: test and internal accounts, soft-deleted records, multiple sources of truth for the same metric, timezone handling, currency, and history or snapshot tables.
5. Write questions to ask the data owners or my team, prioritized.
6. Suggest a personal notes template so I build my own map as I learn.
Return:
- A 5-day learning plan.
- The exploration queries in [SQL DIALECT].
- A first-draft entity map based on what I shared, with guesses marked [CONFIRM].
- The question list for colleagues.
Highlighted [brackets] are blanks to replace with your own details.
Fill in the blanks and use this promptMore Data & Analysis prompts
What to fill in
- e.g. Snowflake + dbt + Looker, BigQuery + Tableau
- e.g. "monthly revenue by product", "customer counts"
- SQL DIALECT
- CONFIRM
How this prompt is built
This prompt follows the Few-Shot structure (Few-shot prompting). A few-shot prompt includes one or more worked examples of the input and the output you want, so the model can copy the pattern instead of guessing it.
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