A Parquet file is a columnar storage file format widely used in Data Engineering, Data Science, and Big Data systems.

🔹 What is a Parquet file?

Apache Parquet is:

• A binary

• Column-oriented

• Highly compressed

• Schema-aware file format

Designed for fast analytics and efficient storage.

🔹 Why Parquet instead of CSV / Excel?

🔹 How Columnar Storage Helps

Instead of storing data row by row, Parquet stores data column by column.

Example:

Name | Age | Salary
A    | 30  | 50000
B    | 25  | 40000

Parquet stores:

Names   → A, B
Ages    → 30, 25
Salary  → 50000, 40000

✅ Faster queries
✅ Better compression
✅ Read only required columns

🔹 Where Parquet is Used

• Apache Spark

• Hadoop

• AWS Athena

• Google BigQuery

• Databricks

• Snowflake

• Pandas / PyArrow

Perfect for:

• Analytics

• Machine Learning pipelines

• Data lakes

• ETL pipelines

🔹 How to Read & Write Parquet in Python

✅ Install

pip install pandas pyarrow fastparquet

✅ Write Parquet

import pandas as pd

df = pd.DataFrame({
    "name": ["A", "B"],
    "age": [30, 25],
    "salary": [50000, 40000]
})

df.to_parquet("data.parquet")

✅ Read Parquet

df = pd.read_parquet("data.parquet")
print(df)

🔹 Parquet vs ORC (Quick)

• Parquet → More popular, cross-platform

• ORC → Slightly faster in Hive ecosystems

🔹 When NOT to use Parquet

❌ Small files
❌ Frequently updated row-level data
❌ Simple human-readable storage

🔹 In Simple Words

Parquet = best format for storing large analytical data efficiently