Prompt

Data Analysis & SQLPromptIn a pack

Lakehouse Table Design on Spark — Delta Lake, Iceberg or Hudi, Partitioning, Merges and Maintenance

Designs lakehouse tables on Spark — choosing an open table format, partitioning and clustering, merge and upsert patterns, schema evolution, time travel and retention, compaction and clean-up jobs, concurrent writers and costs — for reliable analytics and machine learning data.

  • Role-Based
  • Step-by-Step
  • Constraints
  • Structured Output

Opening lines · ~296 words in full

ROLE: You are a lakehouse architect. Table format and layout choices decide query speed, storage cost and how safely multiple jobs can write, and neglected maintenance turns fast tables slow, so you design tables from access patterns and schedule maintenance from day one.
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How to use it

  1. Read it, then replace anything in [BRACKETS] with your details — the more concrete the context, the sharper the answer.
  2. Copy it (or download the .md) and paste it into the AI you already use — it knows your work, so that is where the prompt does the most.
  3. Want a sharper answer? Ask the AI to check its own draft against your brief before it replies — that is self-critique prompting.

Techniques in this prompt

Role-Based

Assigns the model an expert persona so it adopts the right vocabulary, depth, and standards for the task.

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Step-by-Step

Forces explicit intermediate reasoning instead of jumping to a conclusion, which improves accuracy on hard tasks.

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Constraints

Sets the rules and boundaries — tone, length, what to avoid — that keep the output on-target.

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Structured Output

Pins the response to a defined structure so it drops straight into your workflow.

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Works with

Any chat AI — ChatGPT, Claude, Gemini, Copilot, Grok, Mistral or a local model. The structure does the work, so you are not tied to one vendor or one model version.

New to structured prompts? Start with how to prompt AI, the RCTCO prompt framework this prompt is built on, and role prompting examples.

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