Tableau or Flask First: What Beginners Should Learn

// Tableau Calculated Field — Sales Growth %
// The kind of calculation every Tableau analyst writes in the first week.

(SUM([Sales]) - LOOKUP(SUM([Sales]), -1)) / LOOKUP(SUM([Sales]), -1)

// What this does:
// - Takes total sales in the current period.
// - Looks up the previous period's sales.
// - Computes the % difference.
// - Result can drive a KPI card, a trend line, or conditional formatting.
//
// What this teaches:
// - Tableau calculated fields behave like SQL window functions.
// - LOOKUP is essentially a "previous row" reference.
// - The same logic you would write in SQL works in Tableau.

// A second common calculation:
IF [Profit] < 0 THEN "Loss" ELSE "Profit" END

// Why Tableau analysts become valuable fast:
// - They turn raw data into business language.
// - The output is immediately usable by leadership.
// - The work is visible in every review meeting.

# app.py — a minimal Flask API that produces data
# The output of this file is what Tableau (or a frontend) consumes.

from flask import Flask, jsonify
import sqlite3

app = Flask(__name__)


def get_connection():
    return sqlite3.connect("sales.db")


@app.get("/health")
def health():
    return jsonify(status="ok")


@app.get("/api/sales/monthly")
def monthly_sales():
    with get_connection() as conn:
        rows = conn.execute("""
            SELECT
                strftime('%Y-%m', order_date) AS month,
                SUM(amount) AS revenue,
                COUNT(*) AS orders
            FROM orders
            GROUP BY month
            ORDER BY month
        """).fetchall()

    return jsonify([
        {"month": row[0], "revenue": row[1], "orders": row[2]}
        for row in rows
    ])


@app.get("/api/sales/by-region")
def sales_by_region():
    with get_connection() as conn:
        rows = conn.execute("""
            SELECT region, SUM(amount) AS revenue
            FROM orders
            GROUP BY region
            ORDER BY revenue DESC
        """).fetchall()

    return jsonify([
        {"region": row[0], "revenue": row[1]} for row in rows
    ])


# Why this matters:
# - Flask produces clean, structured data.
# - Tableau (or a web dashboard) can consume this API.
# - The separation is clean: Flask = logic, Tableau = visualisation.
#
# What this teaches:
# - How Flask routes and JSON responses work.
# - How SQL and Flask work together.
# - Why "data producers" are valuable in analytics products.

# Connect Tableau to Flask — the real "Tableau + Flask" workflow

# 1. Flask exposes a JSON endpoint (as in the previous example):
#    GET http://localhost:5000/api/sales/monthly

# 2. In Tableau:
#    - Data → New Data Source → Web Data Connector (WDC)
#    - Or use a JSON file / API connector
#    - Point it at the Flask endpoint
#    - Tableau loads the JSON as a table

# 3. Build the dashboard in Tableau:
#    - Line chart: revenue by month
#    - Bar chart: revenue by region
#    - KPI cards: total revenue, orders, AOV

# 4. Publish:
#    - Publish to Tableau Server or Tableau Public
#    - Share the dashboard URL with stakeholders

# 5. Automate refresh:
#    - Tableau Server can refresh from the Flask API on a schedule
#    - The Flask app queries the database on each request

# Why this combination matters:
# - Flask owns the logic and data.
# - Tableau owns the story and visuals.
# - The product is more powerful than either alone.
#
# Career implications:
# - Full stack + BI is one of the most valuable hybrid profiles.
# - You can build the app AND show its impact.