Parquet

A Parquet file is a columnar storage file format specifically designed for storing large amounts of data efficiently. It is commonly used in Big Data ecosystems due to its optimised performance for both storage and querying.

Data Storage

Key Features of Parquet:

1. Columnar Storage Format:
   • Data is stored column by column instead of row by row.
   • This design is highly efficient for analytical queries that access only specific columns, reducing the amount of data read.
2. Optimised for Big Data:
   • Parquet is designed for distributed systems like Apache Hadoop, Apache Spark, and other big data processing tools.
3. Compression:
   • It supports multiple compression algorithms (e.g., Snappy, GZIP) for reducing file size while maintaining fast read and write performance.
4. Schema Evolution:
   • Parquet supports flexible schemas, allowing fields to be added or modified without breaking compatibility with older data.
5. Efficient Metadata Handling:
   • Metadata is stored along with the data, making it easier to retrieve and query information about the dataset without scanning the entire file.

Advantages of Parquet:

1. Improved Query Performance:

   • Since data is stored column-wise, queries that require only a few columns read less data compared to row-based formats like CSV.

2. Lower Storage Costs:

   • Built-in compression and columnar storage significantly reduce file size.

3. Compatibility:

   • Parquet is compatible with most big data processing tools, such as Hadoop, Spark, Hive, Presto, and AWS Athena.

4. Efficient I/O:

   • Parquet’s columnar format minimises disk I/O, making it faster to process large datasets.

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When to Use Parquet:

• Analytical Workloads: Ideal for scenarios where you need to perform aggregations, filtering, or processing large datasets.
• Big Data Processing: Frequently used with tools like Spark, Hive, and Presto in data lakes.
• Cloud Storage: Supported by Cloud Providers platforms.

Example Use Case:

Imagine a dataset with 10 million rows and 100 columns.

• If you query just 3 columns in a row-based format (e.g., CSV), you must read all 100 columns for every row.
• In Parquet, only the 3 relevant columns are read, significantly improving performance.

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File Structure:

• Row Group: Data is divided into chunks called row groups, enabling efficient data retrieval.
• Columns: Each column in a row group is stored together for fast access.
• Footer: Contains metadata, such as schema definitions and row group locations, allowing quick navigation of the file.

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How to Work with Parquet in Python:

You can use libraries like pandas or pyarrow:

import pandas as pd
 
# Write a DataFrame to a Parquet file
df = pd.DataFrame({'col1': [1, 2], 'col2': [3, 4]})
df.to_parquet('example.parquet', engine='pyarrow')
 
# Read a Parquet file into a DataFrame
df_read = pd.read_parquet('example.parquet')
print(df_read)

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In summary, Parquet is an efficient, compact, and scalable file format ideal for big data analytics and storage, providing faster performance and reduced costs.