Dive into Apache Parquet: The Efficient File Format for Big Data

Working with big data can feel hard at first. Files grow fast, queries slow down, and systems use more space than expected. Apache Parquet helps solve these problems. It stores data in a way that makes reading and processing much faster. It also helps teams save storage space and lower compute costs.

Parquet works well when datasets are large and complex. So, instead of scanning full files, systems read required data only. This makes analysis quicker and more stable, even if data size increases. That is why many teams rely on Parquet for reporting, analytics, and data science work.

In this article, you will learn how much Parquet is useful and where it fits in real projects. We will explain how Parquet works, why it performs better than common formats, and how to use it in practice. You will also see how to work with Parquet files using Python.

By the end of this article, you will understand why many data professionals choose Parquet for large datasets. You will also know when it makes sense to use it and how to start using it with confidence.

What is Apache Parquet?

Apache Parquet is an open-source file format that stores data in columns instead of rows. This structure makes it faster to analyze large datasets and helps reduce storage costs compared to formats like CSV or JSON.

For example, we can target only relevant data instead of processing the entire database for finding just a single attribute. That’s the main reason why parquets are good for big data frameworks like Apache Hadoop, Hive, and Spark.

Not only frameworks, but Parquet is also widely used in analytical platforms. Also, developers are using parquet for Amazon S3, Azure Data Lake Storage, to store large-scale datasets.

Let’s understand and compare how parquet and other data platforms work with key differences.

Common Use Cases of Apache Parquet

• Data lakes
• Business intelligence and reporting
• Machine learning datasets
• Large-scale batch analytics
• Cloud-based analytics platforms

What Are the Characteristics of Parquet

Open-source and adopted globally: Apache Parquet is a free, open-source file format. And it is maintained by the Apache Software Foundation and is used in a big data ecosystem.

Language and platform independent: Parquet works with multiple programming languages and processing engines. That makes it suitable for many technology stacks.

Column-based storage design: Data is stored in columns instead of rows, which improves analytical performance and reduces storage usage.

Built for analytical workloads (OLAP): Parquet is commonly used for reporting, analytics, and data lake workloads.

Efficient compression and encoding: It uses advanced compression and encoding techniques to reduce file size alongside maintaining fast read performance.

Supports complex data structures: Parquet handles nested and complex data, making it suitable for modern data models.

Benefits of Apache Parquet

Reduced Storage Costs: Its efficient compression reduces storage space requirements.

Improved Query Performance: Speeds up analytical queries, making data processing more efficient.

Flexibility: Adapts to various use cases, supporting both complex and simple data structures.

We have seen Characteristics and Benefits of Parquet but that’s not enough to understand how all the things work behind the scenes.

How Apache Parquet Process Data

Apache Parquet seamlessly transforms raw data into an optimized format, and it improves both storage efficiency and query performance.

Let’s see how it processes data.

Data organization

When you write data in Parquet file, it splits the data into smaller chunks called row groups. Each row group works independently, which allows systems to process data in parallel and manage memory more efficiently.

Chucking Columns

Inside each row group, Parquet rearranges the data by columns instead of rows. It groups similar data types together, which allows the format to apply the best encoding for each column. For example, Parquet can store dates differently from numeric values to improve efficiency and performance.

Encoding and Compression

Paraquet uses two types of compression processes. First, it uses encoding schemes to represent repeated values and second, it applies compression algorithms like Snappy or Gzip.

Metadata Generation

This format creates complete metadata that includes file schema, data types, statistics for each column, row group locations, and structure.

Execution of Query

When systems read Parquet data, they first check the file metadata to see which columns are needed. Then they read only those columns from storage and decode the data as required. This approach reduces unnecessary reads and speeds up performance.

Apache Parquet vs Other File Formats

Parquet works differently compared to other traditional data formats. Basically, it is designed for analytics, while other formats focus on data exchange or event storage.

Parquet vs CSV and JSON

CSV and JSON are the two most popular data storage forms. And, it is easy to read and useful for sharing small datasets. However, they are slow and inefficient when you work with large amounts of data.

Parquet fixes this problem by storing data in columns structure. So, this allows systems to read only the required columns instead of scanning the entire file. For example, if you want to analyze one column in a very large dataset, Parquet reads only that column, while CSV must read the whole file. This makes Parquet faster and more efficient for analytics.

Parquet vs Avro

If you understand Avro and Parquet, they solve different problems. Avro uses a row-based format and works well for streaming data, like capturing events or transactions in real time. Meanwhile, Parquet focuses on analytical workloads. It performs best when you need to analyze specific columns from large datasets.

For example, an e-commerce company might use Avro to record live order events and then convert that data into Parquet for long-term storage and reporting.

Applications of Apache Parquet

Mostly Parquet is used in industries like finance, healthcare, and e-commerce. Developers rely on it for data analytics, machine learning, and large-scale data processing because it handles big datasets efficiently and performs well at scale.

Getting Started with Parquet

Modern data tools already support Parquet, so you don’t need extra setup. However, you can start reading and writing Parquet files using common Python libraries.

Below are two simple ways to work with Parquet files in Python.

Option 1: Using PyArrow and FSSpec

This approach works good for large datasets and supports cloud storage systems like AWS S3, Azure, and Google Cloud.

First, install the required libraries:

  
pip install pyarrow fsspec
   

Then import them:

  
import pyarrow.parquet as pq
import fsspec
   

Next, define the file location and configure access:

  
url = "paste your URL"
fs = fsspec.filesystem("your_provider", options={"key": "...", "secret": "..."})
    

Read the Parquet file and access the data:

  
table = pq.read_table(fs.open(url))
names = table["name"].to_numpy()
ages = table["age"].to_numpy()
   

This method is efficient and works well for cloud-based and large-scale data processing.

Option 2: Using Pandas

This approach is easier if you already use Pandas, and the file is publicly accessible. It is simpler but may not perform as well on very large datasets.

Install Pandas:

  
pip install pandas
   

Read the Parquet file:

Install Pandas:

  
import pandas as pd
url = "paste your URL"
df = pd.read_parquet(url)
names = df["name"]
ages = df["age"]
   

Use this method for quick analysis or smaller datasets.

Conclusion

Parquet files are a reliable source for any organization that needs to manage and process large amounts of data. Because Parquet files are columnar files, they allow organizations to use less storage space, execute queries faster, and reduce the costs associated with processing large amounts of data through tools like Spark and Hive, as well as most modern data lakes.

Companies dealing with vast amounts of data can benefit from the scalability of analytical solutions offered by Parquet. By giving analysts time to focus on generating insight rather than worrying about optimizing performance, Parquet facilitates the creation of more robust and repeatable analytical solutions.

Regardless of what kind of analytics you are performing (data analytics, machine learning or large scale/enterprise level reporting), Parquet is going to play an integral part in building out that data workflow in 2026 and beyond.

Do you need assistance for your next project? Hire Top Rated Data Analysts. At Lucent Innovation, our data analysis professionals understand how to design efficient data pipelines, use formats like Parquet effectively, and extract real value from large datasets.