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Parquet: The Big Data Gold Standard

Apache Parquet is an open-source, column-oriented data file format designed for efficient data storage and retrieval. Unlike CSV or JSON, which store data row-by-row, Parquet organizes data by columns. This single architectural shift makes it the industry standard for modern data lakes and ML feature stores.

1. Row-based vs. Columnar Storage​

To understand Parquet, you must understand the difference in how data is laid out on your hard drive.

  • Row-based (CSV/SQL): Stores all data for "User 1," then all data for "User 2."
  • Columnar (Parquet): Stores all "User IDs" together, then all "Ages" together, then all "Incomes" together.

2. Why Parquet is Superior for ML​

A. Column Projection (Selective Reading)​

In ML, you might have a dataset with 500 columns, but your specific model only needs 5 features.

  • CSV: You must read the entire file into memory to get those 5 columns.
  • Parquet: The system "jumps" directly to the 5 columns you need and skips the other 495. This reduces I/O by over 90%.

B. Drastic Compression​

Because Parquet stores similar data types together, it can use highly efficient compression algorithms (like Snappy or Gzip).

  • Example: In an "Age" column, numbers are similar. Parquet can store "30, 30, 30, 31" as "3x30, 1x31" (Run-Length Encoding).

C. Schema Preservation​

Parquet is a binary format that stores metadata. It "knows" that a column is a 64-bit float or a Timestamp. You never have to worry about a "Date" column being accidentally read as a string.

3. Parquet vs. CSV: The Benchmarks​

FeatureCSVParquet
Storage Size1.0x (Large)~0.2x (Small)
Query SpeedSlowVery Fast
Cost (Cloud)Expensive (S3 scans more data)Cheap (S3 scans less data)
ML ReadinessRequires manual type castingPlug-and-play

4. Using Parquet in Python​

Pandas and PyArrow make it easy to switch from CSV to Parquet.

import pandas as pd

# Saving a dataframe to Parquet
# Requires 'pyarrow' or 'fastparquet' installed
df.to_parquet('large_dataset.parquet', compression='snappy')

# Reading only specific columns (The magic of Parquet!)
df_subset = pd.read_parquet('large_dataset.parquet', columns=['feature_1', 'target'])

5. When to use Parquet​

  1. Production Pipelines: Always use Parquet for data passed between different stages of a pipeline.
  2. Large Datasets: If your data is MB, the speed gains become obvious.
  3. Cloud Storage: If storing data in AWS S3 or Google Cloud Storage, Parquet will save you significant money on data egress/scan costs.

References for More Details​

Parquet is the king of analytical data storage. However, some streaming applications require a format that is optimized for high-speed row writes rather than column reads.