AI for Data Analysis

TL;DR

• AI is great at SQL generation, pipeline snippets, and exploratory suggestions.
• Always validate: schema, indexes, data types, and scale. AI will write syntactically correct, semantically wrong SQL.
• Use AI to speed up the mechanics. You own the analysis and interpretation.

Data work has a lot of boilerplate and pattern-matching. AI accelerates that. It doesn't replace your understanding of the data.

SQL Generation

Good use cases:

• "Write a query to get X from tables A, B, C with joins"
• "Optimize this query" (paste existing)
• "Convert this to Spark SQL / BigQuery / etc."

Critical checks:

• Schema accuracy — AI assumes column names and types. Wrong assumption = wrong query.
• Index usage — AI may not know your indexes. Verify execution plans.
• Scale — A query that works on 1K rows can explode on 1B. Add limits, consider partitions.
• Sensitives — Never paste real PII or production data. Use schema only or synthetic examples.

Workflow: Generate → review → run on small subset first → then scale.

Pipeline Design

Good use cases:

• "Design an ETL pipeline for [source] to [destination]"
• "Suggest a schema for event streaming with retention"
• "How do we handle late-arriving data in this flow?"

AI can suggest tools, patterns, and code. You verify compatibility with your stack and data governance.

Exploratory Data Analysis (EDA)

Good use cases:

• "What visualizations would help understand this dataset with columns X, Y, Z?"
• "Suggest statistical tests for comparing A and B"
• "What could explain this anomaly in the data?"

AI can propose approaches. You interpret. Correlation isn't causation. AI doesn't know your domain.

Data Documentation

Good use cases:

• "Document this table for a data catalog"
• "Write a data dictionary entry for this schema"
• "Create a README for this pipeline"

Useful for consistency. You add business context and ownership.

-- AI generated this. Always validate against YOUR schema.
-- Check: Do these columns exist? Correct types? Right table names?
SELECT u.id, u.email, o.total
FROM users u
JOIN orders o ON u.id = o.user_id
WHERE o.created_at > '2025-01-01';

-- Add limits when testing. A query that works on 1K rows
-- can explode on 1B. AI doesn't know your scale.
-- LIMIT 100  -- add when exploring
-- Then remove and verify with EXPLAIN before production.

Do This Next

1. Generate one SQL query with AI for a real task. Validate against your schema. Run it. Note what you had to fix.
2. Use AI to draft pipeline design for a small ETL. Compare to how you'd design it. See where AI helps and where it misses.