The Parquet Files: Why This File Format Is Your Data's Best Friend

Look, I know what you're thinking. Another article about file formats? Really? You'd rather be debugging that mysterious production issue or arguing about tabs versus spaces. But hear me out for a minute.

Last week, I was happily hunting through our logs data - you know, the usual terabytes of events that compliance keeps asking for - when our Head of Finance dropped by. "Hey, why is our logging bill so high?"

Narrator: And thus began our hero's journey into the world of file formats.

Remember the good old days when we could just dump everything into CSV files? They were simple, readable, and everyone understood them. Then your data grew. And grew. And suddenly, reading that one column you needed meant downloading 500GB of data you didn't. Your Athena queries started costing more than your coffee budget. That's when you know it's time to talk about Apache Parquet.

The "Why Should I Care?" Part

Here's a fun experiment. Take this piece of Python code:

import pandas as pd
import numpy as np

# Create some annoyingly repetitive data
df = pd.DataFrame({
    'user_id': range(1_000_000),
    'event': np.random.choice(['login', 'logout', 'purchase'], 1_000_000),
    'timestamp': pd.date_range('2024-01-01', periods=1_000_000, freq='1min'),
    'session_id': [f'sess_{i % 1000}' for i in range(1_000_000)]
})

# Save it both ways
df.to_csv('regrets.csv')
df.to_parquet('smart_choice.parquet')

CSV vs Parquet Comparison

Now check the file sizes. Go ahead, I'll wait.

Running this on my machine gave:

$ ls -lh regrets.csv smart_choice.parquet
-rw-r--r--@ 1 user staff 48M Nov 1 19:40 regrets.csv
-rw-r--r--@ 1 user staff 13M Nov 1 19:40 smart_choice.parquet

That's about a 73% reduction in size. "But storage is cheap!" I hear you say. Sure, until you're running queries on this data 50 times a day across hundreds of files, each time downloading the entire file because you just need to check the 'event' column. With the Parquet file format, you'd only download the columns you need (about 1/4 of the data in this case), and it's already compressed efficiently.

Let's do some quick math:

CSV: 48MB × 50 queries × 30 days = 72GB transferred per month
Parquet (querying just one specific column): 3.25MB × 50 queries × 30 days = 4.875GB transferred per month

Your cloud provider just got a lot less happy about their profit margins on AWS.

Understanding Apache Parquet Files

Apache Parquet is a columnar storage format designed for efficient data storage and retrieval in big data workloads. Having worked with petabyte-scale data pipelines for observability data, I can tell you that choosing the right file format can make or break your data engineering architecture.

What Makes Parquet Special?

Columnar Storage: Instead of storing data by rows like in traditional row-based formats (CSV, Excel), Parquet stores data by columns. This fundamental difference completely transforms how we interact with large datasets.
Schema-aware: Unlike CSV files that are just plain text, Parquet files include schema information that defines the data types and structure, making data validation more reliable.
Optimized for OLAP: Parquet was built specifically for analytical queries (OLAP) rather than transaction processing (OLTP), making it ideal for data lakes and data warehouses.
Open-source Ecosystem: Being part of the Apache ecosystem means Parquet has broad compatibility with frameworks like Apache Spark, Hadoop, and countless programming languages.

Why Choose Parquet?

1. Efficient Compression

Parquet uses column-level compression, which is more efficient than row-based compression. Here's a real-world example comparing different encoding schemes:

import pandas as pd
import numpy as np
import os
import pyarrow as pa
import pyarrow.parquet as pq

def compare_storage_efficiency():
    """
    Compare different compression methods for Parquet files
    using realistic data volumes
    """
    # First create the date range
    dates = pd.date_range('2023-01-01', '2023-12-31', freq='D')
    n_rows = len(dates)  # This will be 365
    
    # Create a dataset with repetitive values - matching the date length
    df = pd.DataFrame({
        'category': np.random.choice(['A', 'B', 'C'], n_rows),
        'value': np.random.randn(n_rows),
        'timestamp': dates,
        # Add some more columns to make it interesting
        'user_id': [f'user_{i%100}' for i in range(n_rows)],
        'score': np.random.uniform(0, 100, n_rows)
    })
    
    # Compare different compression methods
    compressions = ['snappy', 'gzip', 'brotli']
    results = {}
    
    print("\nOriginal DataFrame shape:", df.shape)
    print("Memory usage:", df.memory_usage(deep=True).sum() / 1024, "KB")
    
    for comp in compressions:
        output_file = f'data_{comp}.parquet'
        df.to_parquet(output_file, compression=comp)
        size = os.path.getsize(output_file)
        results[comp] = size
        print(f"\n{comp.upper()} compression:")
        print(f"File size: {size/1024:.2f} KB")
    
    # Also save as CSV for comparison
    df.to_csv('data.csv')
    results['csv'] = os.path.getsize('data.csv')
    print(f"\nCSV size: {results['csv']/1024:.2f} KB")
    
    # Calculate compression ratios compared to CSV
    csv_size = results['csv']
    print("\nCompression ratios compared to CSV:")
    for method, size in results.items():
        if method != 'csv':
            ratio = csv_size / size
            print(f"{method}: {ratio:.2f}x smaller")
    
    return results, df

# Run the comparison
compression_results, df = compare_storage_efficiency()

# Print a sample of the data to verify
print("\nSample of the data:")
print(df.head())

When I ran this, I got output like:

Original DataFrame shape: (365, 5)
Memory usage: 46.43 KB

SNAPPY compression:
File size: 10.94 KB

GZIP compression:
File size: 9.41 KB

BROTLI compression:
File size: 9.41 KB

CSV size: 22.27 KB

Compression ratios compared to CSV:
snappy: 2.04x smaller
gzip: 2.37x smaller
brotli: 2.37x smaller

Sample of the data:
  category     value  timestamp user_id      score
0        B  0.286035 2023-01-01  user_0  33.123630
1        A  0.767671 2023-01-02  user_1  73.225731
2        A  0.179130 2023-01-03  user_2  13.983122
3        B  1.942285 2023-01-04  user_3  57.281699
4        A  1.841576 2023-01-05  user_4  33.683535

As you can see, even with this relatively small dataset, Parquet with GZIP compression is 2.37x smaller than the CSV version. Now imagine scaling that to terabytes!

2. Schema Evolution

One of the most powerful features of the Parquet file format is schema evolution, which makes it perfect for evolving systems. The format allows you to add new columns to your datasets without breaking compatibility with older data.

import pyarrow as pa
import pyarrow.parquet as pq

def demonstrate_schema_evolution():
    # Original schema
    df1 = pd.DataFrame({
        'id': range(5),
        'value': range(5)
    })
    
    # Write with original schema
    table1 = pa.Table.from_pandas(df1)
    pq.write_table(table1, 'evolving_data.parquet')
    
    # New schema with additional column
    df2 = pd.DataFrame({
        'id': range(5, 10),
        'value': range(5, 10),
        'new_column': ['a', 'b', 'c', 'd', 'e']  # New column added!
    })
    
    # Append with new schema
    table2 = pa.Table.from_pandas(df2)
    pq.write_table(table2, 'evolving_data.parquet',
                   existing_data_behavior='delete_matching_partitions')
                   
    # Reading back works fine with both schemas
    read_back = pq.read_table('evolving_data.parquet').to_pandas()
    print(read_back)

This is something you simply can't do with CSV files without a lot of manual work or complex data processing pipelines.

3. Predicate Pushdown for Query Performance

When working with large datasets in Amazon S3 or other cloud storage, Parquet allows for predicate pushdown – a fancy term that means "filter before loading." This significantly improves query performance for analytical queries.

# Example using PyArrow to read only specific rows meeting a condition
import pyarrow.parquet as pq

# This only loads the filtered data, not the entire file
filtered_data = pq.read_table(
    'large_dataset.parquet',
    filters=[('timestamp', '>=', '2024-01-01'), ('level', '=', 'ERROR')]
)

How Parquet Actually Works

Think of CSV files like a library where every time you want to find something, you have to read every book from cover to cover. Parquet is more like having an index for each topic - you only read what you need.

Here's what makes it special:

1. Columnar Storage Structure

In row-based storage formats like CSV, each row is stored together. In columnar storage like Parquet, each column is stored together:

CSV (Row-based):
id,name,age
1,Alice,25
2,Bob,30
3,Charlie,22

Parquet (Conceptually):
Column 'id': [1, 2, 3]
Column 'name': ['Alice', 'Bob', 'Charlie']
Column 'age': [25, 30, 22]

This structure means when you're only interested in the 'age' column, you don't need to read the entire file – just that specific column.

2. Row Groups and Metadata

Parquet organizes data into row groups with built-in metadata that describes:

Schema information with data types
Statistics for each column (min/max values)
Encoding information

This metadata allows the query engine to skip entire chunks of data that don't match filtering criteria.

3. Complex Data Structures Support

Unlike CSV, Parquet can efficiently store nested data structures like arrays, maps, and structs, making it perfect for complex data:

# Creating a DataFrame with nested data
df = pd.DataFrame({
    'id': range(3),
    'nested': [
        {'a': 1, 'b': [1, 2, 3]},
        {'a': 2, 'b': [4, 5, 6]},
        {'a': 3, 'b': [7, 8, 9]}
    ]
})

# Save to Parquet (works fine!)
df.to_parquet('nested_data.parquet')

# Try with CSV (will not preserve structure)
df.to_csv('nested_data.csv')

When to Use Parquet

✅ Perfect for:

Analytics data and data warehouses
Log storage and event streams
Metrics data
Data lakes
Big data processing in Hadoop ecosystems
Optimizing storage space and query performance

❌ Not great for:

Small datasets (<100MB) where the overhead isn't worth it
Frequent small updates (OLTP workloads)
When you need direct file editing (like spreadsheets)
When compatibility with legacy systems is critical

Comparing Parquet with Other Data Formats

Format	Type	Strengths	Weaknesses	Best For
Parquet	Columnar	Compression, query perf	Not human-readable	Analytics
CSV	Row-based	Simplicity, compatibility	Size, no schema	Small datasets
Avro	Row-based	Schema evolution	Not columnar	Streaming
ORC	Columnar	Similar to Parquet	Less ecosystem support	Hive workloads

Best Practices We Learned the Hard Way

1. Don't Over-Partition

Good: Partition by year, month
Bad: Partition by year, month, day, hour, service, region
Why? Too many small files kill S3 performance and can overwhelm file system metadata

I once had a data pipeline that generated one file per minute per service (we had about 50 services). Within a week, we had over 500,000 tiny Parquet files that made listing operations painfully slow. We had to reprocess everything with more reasonable partitioning.

2. Choose Compression Wisely

Hot data: Snappy (fast compression/decompression)
Cold data: GZIP or ZSTD (better compression ratio)
Analytics: Depends on your query patterns

Our team found that for frequently queried log data, the slight increase in storage cost with Snappy was worth the improved query performance compared to GZIP.

3. Row Group Sizes Matter

Too small: More S3 requests
Too large: More memory pressure
Sweet spot: 100MB-200MB for analytics

We tune our row group sizes depending on the expected query patterns. For datasets where we typically scan entire columns, larger row groups work better. For datasets where we apply very selective filters, smaller row groups help with parallelism.

The Plot Twist

Here's the thing about Parquet that no one tells you in the technical docs: it's not just about saving money or making queries faster. It's about changing how you think about data.

Remember that time you had to add a new field to your logging, and everyone panicked about backward compatibility? With Parquet's schema evolution, that's just... not a problem anymore. Need to analyze just one field across five years of data? Go ahead, it won't bankrupt the company.

Real Talk: A Tale of Two Queries

Let me share something that happened last month. We had two identical queries running in production:

-- The query both teams ran:
SELECT date_trunc('hour', timestamp) as hour,
       COUNT(*) as errors 
FROM logs 
WHERE service = 'payment-api'
  AND level = 'ERROR'
GROUP BY 1
ORDER BY 1

Team A (running against CSVs on Amazon S3):

Query cost: $5.23
Data scanned: 1.2TB
Runtime: 3.5 minutes
Number of angry Slack messages: 7

Team B (running against Parquet on Amazon S3 with Athena):

Query cost: $0.89
Data scanned: 157GB
Runtime: 42 seconds
Number of coffee breaks taken while waiting: 0

The funny part? Both teams were looking at the exact same data. Team B just stopped reading columns they didn't need and filtering data they didn't want. Revolutionary, I know.

Practical Tips for Working with Parquet

Reading Parquet with Python

import pandas as pd

# Read an entire Parquet file
df = pd.read_parquet('mydata.parquet')

# Read only specific columns
df = pd.read_parquet('mydata.parquet', columns=['timestamp', 'user_id'])

# Filter data while reading (using PyArrow)
import pyarrow.parquet as pq
table = pq.read_table('mydata.parquet', filters=[('value', '>', 100)])
df = table.to_pandas()

Reading Parquet in SQL Engines

-- Amazon Athena, Presto, Spark SQL
SELECT * FROM parquet_table
WHERE date_column BETWEEN '2024-01-01' AND '2024-01-31'
  AND status = 'completed';

Converting from CSV to Parquet

import pandas as pd

# Simple conversion
df = pd.read_csv('legacy_data.csv')
df.to_parquet('optimized_data.parquet')

# Chunked conversion for large files
chunksize = 100_000
for i, chunk in enumerate(pd.read_csv('huge_file.csv', chunksize=chunksize)):
    chunk.to_parquet(f'output/part-{i:05d}.parquet')

Why This Actually Matters

Look, at the end of the day, this isn't just about file formats. It's about:

Being able to analyze problems without watching the AWS bill climb
Running queries without planning your coffee break around them
Adding new data without breaking old code
Making your Finance team actually like you

In my years of working with data engineering and big data, switching to Parquet for our data lake was one of those rare changes that immediately showed benefits. Query costs dropped by 60%, and data storage costs fell by around 40%. The entire data team suddenly had more budget for the things that actually mattered.

Getting Started

Ready to join the Parquet revolution? Here's how to get started:

For Python users:

pip install pandas pyarrow

For Spark users:

# Reading
df = spark.read.parquet("s3://your-bucket/path/to/data")

# Writing
df.write.parquet("s3://your-bucket/path/to/output")

For AWS Athena/Glue users:

-- Convert your data to Parquet
CREATE TABLE my_parquet_table
WITH (format = 'PARQUET')
AS SELECT * FROM my_csv_table;

Conclusion

If you're still dumping terabytes of data into CSV files, you're probably spending more time and money than you need to. Parquet's columnar storage format offers significant advantages in storage efficiency, query performance, and schema flexibility that are hard to ignore in today's data-driven world.

Give it a try – your cloud bill (and your colleagues waiting for those analytical queries to finish) will thank you.