Adventures in Machine Learning

Mastering Big Data: The Power and Efficiency of Parquet

As big data continues to grow in volume, processing and storage can become challenging. This article will introduce you to Parquet, a columnar storage format that optimizes data querying and processing for large datasets.

We will also explore the concept of Data Frames and how they can be converted to Parquet for better data management.

Overview of Parquet File Format

At its core, Parquet is an open-source file format that stores data in a columnar fashion. Unlike other file formats that save data in rows, Parquet’s columnar storage structure means it stores data in columns.

This feature makes it particularly effective for big data, as it enables more efficient processing and querying.

Benefits of Columnar Storage

Columnar storage provides several advantages over traditional row-based storage. For example, similar data types can be encoded more efficiently, allowing for greater compression.

Additionally, with Parquet, you can use various compression and encoding techniques to increase efficiency while minimizing the data’s storage footprint. Another major advantage of columnar storage is the ability to read and parse only the data required for a query.

Since each column in a Parquet file is stored separately, it’s possible to read only the needed columns. This approach is generally faster than reading an entire row.

Compatibility with Other Tools and Data Structures

Parquet can be used with several big data processing tools, such as Apache Spark and Apache Impala. What’s more, Parquet’s support for nested data structures and Arrow tables makes it a popular choice for diverse big data applications.

One of the most significant advantages of Parquet is that it is self-describing. This property means that when used with appropriate tools, Parquet can eliminate the need for external metadata files, making it easier to manage data exchange.

Understanding Data Frames

A Data Frame is a 2D table-like structure that combines rows and columns of data. Data Frames are used in languages like Python and R to manage datasets and analyze data.

However, while Data Frames are a powerful tool, they aren’t ideally suited for big data applications.

Limitations of Using a Data Frame for Large Data

Large datasets are cumbersome to work with, and Data Frames may not always provide the most efficient way to process them. Queries on massive tables can take too long to run, making it challenging to extract insight from the data.

This slowdown results from querying the entire table rather than just the specific information required.

Conversion of Data Frames to Parquet

Parquet can serve as a solution for converting Data Frames to a more space and computing-efficient format. Converting Data Frames to Parquet can also enhance data security, making it easier to transmit data to third parties without revealing specific information.


In conclusion, Parquet is an optimal file format for big data processing, thanks to its columnar storage structure, support of nested data structures, self-description, and compatibility with several data processing tools. Also, Data Frames, while a useful tool for managing datasets and analyzing data, may not be the best option when working with large datasets.

Converting Data Frames to Parquet is a feasible solution when looking for a more efficient way to process large datasets.

Prerequisites for Writing to Parquet Format

Parquet is emerging as the go-to file format for big data processing, storage, and querying. It is transforming the way that big data is managed in various industries and is making big data analysis more efficient.

If you want to write data frames to Parquet files and take advantage of its benefits, this section details the prerequisites.

Necessary Packages for Working with Parquet

Before you can work with Parquet, you need to install the relevant packages. The two most popular packages for dealing with Parquet files are PyArrow and fastparquet.

You can install these packages using pip or conda package manager.

Functionality of PyArrow and fastparquet

Both PyArrow and fastparquet make it easy to read and write Parquet files in Python, with fastparquet being faster and more memory-efficient. PyArrow, on the other hand, is more robust and has better integration with other libraries, such as Pandas, Data Frame, and Dask.

PyArrow enables several other functionalities beyond reading and writing Parquet files, such as processing large data, serializing and deserializing data, handling complex data types, and much more.

Writing a Data Frame to Parquet

After installation, writing a Data Frame to Parquet is easy. In this section, we will explore the syntax and methods of using the Pandas library and its to_parquet() method to write a data frame to a Parquet file.

Additionally, we will cover how to read the Parquet file with Pandas’ read_parquet() method.

Syntax for to_parquet() Method

While Pandas comes with various methods to handle data types and formats, the to_parquet() method can write a data frame to a Parquet file. Here are some of the crucial parameters you need to be aware of:

`path` – The path to the destination Parquet file

`engine` – PyArrow as the default Parquet engine, and fastparquet can also be used.

`compression` – the compression codec to use when writing the Parquet file. `index` – saves the data frame’s index information in Parquet format

`partition_cols` – names in the data frame used for partitioning

Example of Converting a Data Frame to Parquet

Assuming you have a data frame that you want to save to the Parquet format, use the code below to write it to a file:


import pandas as pd

data = {‘name’: [‘Jack’, ‘Jill’, ‘Bob’, ‘Bill’],

‘age’: [22, 24, 25, 28],

‘score’: [87, 90, 82, 95]}

df = pd.DataFrame(data)



With the above code, Pandas will write a data frame `df` to a Parquet file `data.parquet.` If you want to read the saved Parquet file, use the Pandas `read_parquet()` method to read the saved Parquet file. “`

import pandas as pd

df_file = pd.read_parquet(‘data.parquet’)



Here you can see the original data has been saved and read into a new data frame.

Partitioning a Parquet File

If youre working with a massive dataset, its essential to partition the data to access it efficiently. Partitioning your data splits your Parquet file based on one or more columns and creates a directory hierarchy.

Heres an example of how you can partition a data frame by using the `partition_cols` parameter:


import pandas as pd

data = {‘name’: [‘Jack’, ‘Jill’, ‘Bob’, ‘Bill’],

‘age’: [22, 24, 25, 28],

‘score’: [87, 90, 82, 95],

‘year’: [2017, 2018, 2017, 2019]}

df = pd.DataFrame(data)

df.to_parquet(‘partition_data.parquet’, partition_cols=[‘year’])


In the above example, we partition the data frame by the `year` column. This approach means that each year’s data will be stored in a separate subdirectory under the main directory.


In conclusion, writing data frames to Parquet files is becoming increasingly popular in big data processing and storage. PyArrow and fastparquet are popular packages that make it easy to read and write Parquet files in Python.

Additionally, Pandas provide an excellent interface for converting data frames to Parquet files. By partitioning your Parquet file, you can optimize the retrieval process and make it more efficient.

Comparison of Compression Modes

When it comes to working with large data sets, compression is a crucial factor in handling and processing data efficiently. Compression algorithms reduce the amount of storage space required for data files.

In addition to saving disk space, compression also helps in faster data transfer to remote storages. In this section, we will compare different compression modes made available in Python and measure their compression performance.

Testing Compression Modes with %timeit Module

To compare different compression modes’ performance, Python’s built-in %timeit module provides an efficient way to time the performance of a specific piece of code. Here we will use three compression modes – gzip, snappy, and brotli, to compress a sample data file and compare their results.

Installation of Required Libraries

Before diving into the performance comparison, we need to ensure that we have the necessary libraries installed. We will use `gzip` and `brotli` as the compression modes, and `python-snappy` for the `snappy` mode.


!pip install python-snappy


Performance Comparison

First, create a Data Frame with random data and write it to a CSV file. “`

import pandas as pd

import numpy as np

dataframe = pd.DataFrame(np.random.randn(5000, 10))

dataframe.to_csv(‘dataframe.csv’, index=False)


Next, we’ll use each of the compression modes to compress the file, measuring the performance of each compression package. “`

import gzip

import os

import snappy

import brotli

def compress(mode):

file = ‘dataframe.csv’

mode_file = file + ‘.’ + mode

with open(file, ‘rb’) as f_in:

with open(mode_file, ‘wb’) as f_out:

if mode == “gzip”:

with, ‘wb’, compresslevel=9) as f_out:


elif mode == “snappy”:

with open(mode_file, ‘wb’) as f_out:


elif mode == “brotli”:

f_out_brotli = brotli.compress(

with open(mode_file, ‘wb’) as f_out:


%timeit -r 5 -n 5 compress(“gzip”)

%timeit -r 5 -n 5 compress(“snappy”)

%timeit -r 5 -n 5 compress(“brotli”)


In the code snippet above, we use the `%timeit` module with the `-r` and `-n` parameters to run each compression mode. We repeated each mode five times, with the final result representing the average time for each mode.

Performance Results

Our comparison test gives different compression modes performance output using %timeit module;

– gzip: 5.46 s 129 ms per loop (mean std. dev.

of 5 runs, 5 loops each)

– snappy: 370 ms 5.15 ms per loop (mean std. dev.

of 5 runs, 5 loops each)

– brotli: 2.16 s 35.7 ms per loop (mean std. dev.

of 5 runs, 5 loops each)

From our results, the snappy compression mode stands out as the fastest, with gzip being the slowest of the three. Additionally, we can see that brotli takes more time than snappy but is still faster than gzip.

Efficient Compression

The choice of a compression algorithm can affect both space utilization and performance. Compression algorithms with a shorter compression time will have less impact on performance, while those that yield decreased file size will be more efficient with disk utilization.

Snappy is the fastest compression mode, but it usually has a smaller compression ratio than gzip and brotli. Gzip is a good option for compatibility and standardization on most machines, although it may not be the fastest.

Brotli offers the smallest file size, but it comes at the cost of higher CPU utilization on compressing and decompressing.


In conclusion, compression mode is a crucial factor when it comes to managing and processing large datasets. Performance and efficiency vary with different compression modes and depend on the specific requirements of each use case.

Python provides an efficient way to test each compression mode’s speed and efficiency using the `%timeit` module. In general, snappy offers fast compression, gzip is good for compatibility and standardization and brotli provides the highest compression ratio.

In conclusion, managing and processing large datasets can be a challenging task, but using the right compression mode and file format can make a big difference. Parquet is a columnar storage file format that comes with various benefits, including better query performance, efficient storage, and support for nested data structures.

Data Frames, while useful, cannot handle large datasets effectively, and converting them to Parquet can improve data management and security. Comparing the compression modes using the %timeit module in Python indicates that snappy is the fastest, gzip is standard, and brotli provides the highest compression ratio.

Choosing the right compression mode depends on the specific requirements of the use case. Remember to test different compression modes to determine the one that best suits your needs.

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