Case Study 1.3: SQL vs Python and Pandas

Case Study 1.3 Reading Time: 6 mins

How does SQL compare with alternatives like Python and Pandas?

Goal: Learn about Python and Pandas alternatives to SQL

While SQL remains the dominant language for data at scale, modern developers often look to procedural languages for data tasks. Let’s review some alternatives to get a flavor for the tradeoffs.

Procedural Languages: Python, C++, and Java

These offer distinct approaches to big data processing:

Python: Simplicity makes it ideal for interactive data manipulation and rapid prototyping.
C++: Excels in performance-critical scenarios with low-level control, but at the cost of high development complexity.
Java: Offers great portability and a strong ecosystem for scalability and parallelism.

Trade-offs: You must consider Python's interpreted nature and memory consumption, C++'s complexity, and Java's performance overhead when building custom applications.

Python and the Pandas Library

Pandas offers a versatile approach to data analysis. Its intuitive DataFrame structure simplifies handling structured data, allowing you to use over 600 functions optimized for indexing, aggregation, and handling missing values. Its compatibility with various formats makes it invaluable for data scientists working on a single machine.

Custom SQL Derivatives

Industry players often bridge the gap with custom tools:

Apache Spark: Uses SparkSQL to allow SQL operations on massive, distributed datasets.
Cassandra: Uses CQL (Cassandra Query Language), which looks like SQL but is designed for its specific distributed architecture.

Example: Computing Popular Songs

Recall our three tables: Songs, Listens, and Users. To find the top 10 most popular songs (the most listens).

The Procedural Way (Pure Python)

In pure Python, you would represent these as arrays and use loops to calculate the result.

# Pure Python Logic
def get_popular_songs(songs, listens):
    counts = {}
    for listen in listens:
        song_id = listen['song_id']
        counts[song_id] = counts.get(song_id, 0) + 1

    # Merge titles and sort
    popular = []
    for s_id, count in counts.items():
        song = next(s for s in songs if s['id'] == s_id)
        popular.append({'title': song['title'], 'artist': song['artist'], 'count': count})

    return sorted(popular, key=lambda x: x['count'], reverse=True)[:10]

Logic: Iterate through every listen, manually count them in a dictionary, find the song title for each, and then sort at the end.

The Library Way (Pandas)

Pandas simplifies this by using high-level DataFrame operations that look more like SQL but are written in Python code.

import pandas as pd

# Pandas Logic
songs_df = pd.DataFrame(songs)
listens_df = pd.DataFrame(listens)

merged = pd.merge(listens_df, songs_df, on='id')
popular = merged.groupby(['title', 'artist']).size().reset_index(name='count')
top_10 = popular.sort_values('count', ascending=False).head(10)

Logic: Convert to DataFrames, merge on ID, group by title/artist, count, sort, and take the head.

When to use Pandas versus SQL?

The "RAM" Limit (Pandas Strengths)

Think about a Spotify dataset containing 10,000 Songs. All this data fits easily into the RAM of your laptop. Because Pandas has 600+ functions for visualization and complex math, data scientists find it more convenient to load data into RAM on a single machine for interactive exploration.

The "Scale" Limit (SQL Strengths)

Now, imagine the complete Spotify library: 50 million songs and billions of rows of user listens.

Pandas fails because you cannot fit billions of rows into the memory of one machine.
SQL succeeds because SQL engines are designed to split tasks across multiple machines (distributed processing) seamlessly. The app developer writes the same query, and the backend handles the massive scale.

Bridging the Gap: The Future

Startups and research projects are trying to give you the "Best of Both Worlds."

Modin (from Berkeley, acquired by Snowflake in 2024) rewrites your Pandas calls into SQL automatically.
This allows you to write the Python you love while the backend executes on a powerful SQL-based engine. In 2023, they showed this could scale up to a Terabyte of data, speeding up some workloads by 16x.