A Simple Data Engineering Project: Analyzing Daily Weather Data

Data engineering often sounds like a complex field full of big data, distributed systems, and massive pipelines. But the truth is you don’t need huge infrastructure to get started. Even a small project can teach you the core concepts of data engineering.

In this post, we’ll build a mini data pipeline that collects daily weather data, stores it in a database, processes it, and finally visualizes the results.

What We’re Building

Our pipeline will:

Collect weather data from an API.
Store the data in a lightweight database (SQLite).
Clean and process it using Python.
Analyze and visualize temperature trends.

Think of this as a scaled-down version of a real-world data pipeline:

Ingest → Store → Process → Analyze.

Tools We’ll Use

Here’s our simple tech stack:

Python 🐍 – main programming language
Requests – to fetch API data
SQLite – small database to store records
Pandas – for cleaning and analysis
Matplotlib – for visualization

Step 1: Collect Data from an API

We’ll use the free OpenWeatherMap APi (sign up for a free API key).

import requests

import sqlite3
import pandas as pd
from datetime import datetime
API_KEY = "your_api_key_here"
CITY = "Delhi"
URL = f"http://api.openweathermap.org/data/2.5/weather?q={CITY}&appid={API_KEY}&units=metric"
response = requests.get(URL).json()
print(response) # quick peek at the data

This will return live weather data in JSON format.

Step 2: Store Data in SQLite

Let’s save this data so we can analyze it later.

# Connect to SQLite database

conn = sqlite3.connect("weather_data.db")
cursor = conn.cursor()
# Create table if it doesn't exist
cursor.execute("""
CREATE TABLE IF NOT EXISTS weather (
city TEXT,
temperature REAL,
humidity REAL,
description TEXT,
date TEXT
)
""")
# Insert one record
data = (
CITY,
response["main"]["temp"],
response["main"]["humidity"],
response["weather"][0]["description"],
datetime.now().strftime("%Y-%m-%d %H:%M:%S")
)
cursor.execute("INSERT INTO weather VALUES (?, ?, ?, ?, ?)", data)
conn.commit()
conn.close()

Each time you run the script, a new record will be added with the latest weather data.

Step 3: Process & Clean Data

Now let’s load the stored data for analysis.

conn = sqlite3.connect("weather_data.db")

df = pd.read_sql("SELECT * FROM weather", conn)
conn.close()
print(df.head()) # see the first few rows

Step 4: Analyze & Visualize

We can calculate the average temperature and plot trends.

df["date"] = pd.to_datetime(df["date"])

df.set_index("date", inplace=True)
# Plot temperature over time
df["temperature"].plot(kind="line", title="Temperature Over Time")

This will generate a simple line chart showing how the temperature changes over time.

What We Learned

By completing this mini project, we covered the essentials of data engineering:

Data ingestion: pulling from an API
Data storage: saving into SQLite
Data processing: cleaning with Pandas
Data analysis: calculating averages
Data visualization: plotting with Matplotlib

This is the same workflow used in large-scale systems just on a smaller scale.

Next Steps

Want to make this project more powerful? Try:

Scheduling the script with Cron (Linux) or Task Scheduler (Windows) to collect data automatically every day.
Adding more cities to track multiple weather sources.
Exporting your final results to CSV or dashboards.

Conclusion

Congratulations you’ve just built your first data engineering project! 🎉

It may be small, but it teaches you the building blocks of every real-world pipeline: ingestion, storage, processing, and analysis. Keep practicing with different data sources, and you’ll be on your way to mastering data engineering.