A Python script that queries your sales database, generates a revenue summary using SQL, and calls Claude to produce a written analysis — all in under 80 lines of code. This is what a data pipeline looks like in the real world.
Install dependencies
You need two packages: anthropic (Claude's Python library) and pandas (data manipulation). SQLite3 is built into Python — nothing to install.
pip install anthropic pandasGet your Anthropic API key from console.anthropic.com. Set it as an environment variable:
# Mac / Linux
export ANTHROPIC_API_KEY="sk-ant-..."
# Windows (Command Prompt)
set ANTHROPIC_API_KEY=sk-ant-...Python + SQLite3 — Querying Programmatically
Python's built-in sqlite3 module connects to any SQLite database file. You can run any SQL query and get results back as a list of tuples or a pandas DataFrame.
import sqlite3
import pandas as pd
# Connect to the database
conn = sqlite3.connect("sales.db")
# Run a SQL query — get results as a DataFrame
query = """
SELECT
c.name AS customer,
c.segment,
COUNT(*) AS orders,
SUM(o.amount) AS total_revenue,
AVG(o.amount) AS avg_order
FROM orders o
JOIN customers c ON o.customer_id = c.id
WHERE o.status = 'completed'
GROUP BY c.name, c.segment
ORDER BY total_revenue DESC
"""
df = pd.read_sql_query(query, conn)
conn.close()
print(df.to_string(index=False))Run it: python query_db.py. You'll see a formatted table of customer revenue data in your terminal.
Feed the Data to Claude
Now the interesting part — take the query results and ask Claude to analyze them. The pipeline: query → format as text → send to Claude → print the analysis.
import sqlite3
import pandas as pd
import anthropic
import os
# 1. Query the database
conn = sqlite3.connect("sales.db")
query = """
SELECT
c.name, c.segment,
COUNT(*) AS orders,
ROUND(SUM(o.amount), 2) AS total_revenue,
ROUND(AVG(o.amount), 2) AS avg_order
FROM orders o
JOIN customers c ON o.customer_id = c.id
WHERE o.status = 'completed'
GROUP BY c.name, c.segment
ORDER BY total_revenue DESC
"""
df = pd.read_sql_query(query, conn)
conn.close()
# 2. Format as a clean text table
data_text = df.to_string(index=False)
total = df['total_revenue'].sum()
# 3. Send to Claude for analysis
client = anthropic.Anthropic()
prompt = f"""Here is a sales revenue summary from our database:
{data_text}
Total revenue: ${total:,.2f}
Please analyze this data in 3-4 sentences. Identify:
- Who the top customer is and their share of revenue
- Any patterns in the segment data
- One actionable recommendation for the sales team"""
message = client.messages.create(
model="claude-3-5-haiku-20241022",
max_tokens=512,
messages=[{"role": "user", "content": prompt}]
)
# 4. Print the analysis
print("\n=== DATA ===\n")
print(data_text)
print(f"\nTotal revenue: ${total:,.2f}")
print("\n=== AI ANALYSIS ===\n")
print(message.content[0].text)claude-3-5-haiku is Claude's fastest and cheapest model — perfect for pipelines that run frequently. For deeper analysis, swap in claude-3-5-sonnet-20241022.
Wrap it in a function and schedule it
Production pipelines don't run once. They run on a schedule — daily, weekly, or triggered by an event. Here's a clean version you can extend:
def run_sales_report(db_path: str, query: str, prompt_template: str) -> str:
"""Query a SQLite DB, format results, get AI analysis. Returns the analysis."""
import sqlite3, pandas as pd, anthropic
# Query
conn = sqlite3.connect(db_path)
df = pd.read_sql_query(query, conn)
conn.close()
# Analyze
client = anthropic.Anthropic()
prompt = prompt_template.format(data=df.to_string(index=False))
msg = client.messages.create(
model="claude-3-5-haiku-20241022",
max_tokens=512,
messages=[{"role": "user", "content": prompt}]
)
return msg.content[0].text
# Use it
if __name__ == "__main__":
analysis = run_sales_report(
db_path="sales.db",
query="SELECT name, SUM(amount) AS total FROM orders o JOIN customers c ON o.customer_id=c.id GROUP BY name ORDER BY total DESC",
prompt_template="Analyze this sales data in 3 sentences:\n\n{data}"
)
print(analysis)To schedule this, use cron on Mac/Linux or Windows Task Scheduler. Or deploy it as a GitHub Action that runs on a schedule.
Day 5 Complete — What You Built
- Connected Python to SQLite with sqlite3 and pandas
- Ran parameterized SQL queries programmatically
- Fed query results to the Claude API for AI analysis
- Wrapped the pipeline in a reusable function
- A complete portfolio project: SQL + Python + AI
You finished SQL for AI and Data
You went from your first SELECT to building an AI-powered data pipeline. That's 5 days of real, usable SQL skill. Where you go next is up to you.