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Difference between Apache parquet and arrow

27 September 2026 · 8 min read

Difference between Apache parquet and arrow

In the vast and ever-expanding landscape of big data, efficient data storage and processing are paramount. Two foundational technologies, Apache Parquet and Apache Arrow, frequently appear in discussions about optimizing data workflows. While often mentioned in the same breath, understanding the critical difference between Apache Parquet and Arrow is essential for any data engineer, analyst, or architect. Parquet excels as a durable, on-disk columnar storage format, designed for analytical queries and efficient compression. Arrow, on the other hand, provides a standardized, in-memory columnar data format, fundamentally changing how data moves between different systems without serialization overhead. This distinction, though subtle at first glance, dictates their respective roles and, more importantly, how they complement each other in modern data pipelines, accelerating everything from ETL to real-time analytics.

Understanding Apache Parquet: The On-Disk Standard

Apache Parquet is an open-source, columnar storage format designed for efficient data compression and encoding schemes. It’s built to handle complex nested data structures and is widely adopted across the big data ecosystem, especially within frameworks like Apache Spark, Apache Hadoop, and Impala. Its columnar nature means that instead of storing data row by row, it stores data column by column. This approach offers significant advantages for analytical queries, as only the necessary columns need to be read from disk, drastically reducing I/O operations.

The benefits of Parquet extend beyond just storage efficiency. For instance, according to the official Apache Parquet documentation, its sophisticated compression algorithms, such as Snappy, Gzip, and LZO, can lead to substantial reductions in storage space and network bandwidth. This makes Parquet an ideal choice for archiving large datasets and for long-term storage where cost and retrieval speed are critical. Furthermore, its ability to push down predicates (filters) directly to the storage layer means that data can be filtered before it’s even loaded into memory, further accelerating query performance.

Consider a scenario where you’re analyzing customer demographics. If your data is stored in a row-oriented format, retrieving just the ‘age’ and ‘country’ columns would still require reading entire rows, including irrelevant columns like ‘purchase history’ or ’email address’. With Parquet, only the ‘age’ and ‘country’ columns are accessed, leading to faster data retrieval and processing. This makes Parquet a cornerstone for data lakes and data warehouses where efficient batch processing and analytical workloads are common.

Introducing Apache Arrow: The In-Memory Accelerator

Apache Arrow is a cross-language development platform for in-memory data. It defines a language-agnostic, columnar memory format that allows different data processing systems to share data without the costly serialization and deserialization steps. Think of it as a universal data interchange format for analytics. Before Arrow, moving data between systems like Python (Pandas), Java (Spark), or R often involved converting data to a common format (like JSON or CSV), which then had to be parsed and reconstructed in the target system. This overhead significantly slowed down complex data workflows.

The core innovation of Arrow is its standardized in-memory layout. By defining a common way to represent columnar data in RAM, Arrow enables “zero-copy reads” across different programming languages and processing engines. This means that data can be accessed directly by multiple systems without needing to be copied or transformed, leading to dramatic performance improvements for analytical operations. For example, a dataset processed in Spark can be seamlessly passed to a Python Pandas DataFrame or a Dremio query engine without any data conversion, vastly improving data interoperability.

A recent study published in the Apache Arrow blog highlighted how Arrow Flight SQL, built on Arrow’s capabilities, can achieve significantly faster data transfers than traditional JDBC/ODBC protocols, especially for large analytical result sets. This demonstrates Arrow’s transformative impact on the speed and efficiency of modern data stacks. It’s not just about storage; it’s about the velocity of data through your entire analytical pipeline.

The Core Difference Between Apache Parquet and Arrow

The fundamental difference between Apache Parquet and Arrow lies in their primary function and where they operate within the data lifecycle. Apache Parquet is an on-disk, persistent storage format, optimized for efficient archival, compression, and retrieval of large datasets for analytical queries. It’s about how you save your data to storage (like HDFS or S3) and retrieve it efficiently. Arrow, conversely, is an in-memory data format, designed to accelerate data processing and transfer within and between different computational systems. It’s about how data is represented and manipulated while it resides in your computer’s RAM.

One way to conceptualize their roles is to think of Parquet as the highly efficient truck that transports your data on long journeys (from disk to memory), and Arrow as the standardized container that ensures the data can be quickly unloaded and processed by various machines (different applications or languages) once it arrives at its destination. Parquet focuses on minimizing I/O and storage footprint, while Arrow focuses on minimizing CPU cycles spent on serialization/deserialization and enabling direct memory access across diverse platforms.

For those seeking a quick answer: The primary difference between Apache Parquet and Apache Arrow is that Parquet is a columnar storage format optimized for disk I/O and compression, suitable for long-term data persistence and batch analytics, whereas Arrow is a columnar in-memory format designed for high-performance data interchange and processing within and across applications, enabling zero-copy data access. They are complementary technologies, with Parquet efficiently storing data and Arrow speeding up its in-memory manipulation.

Here’s a concise breakdown of their distinct characteristics:

  • Apache Parquet:
    • Purpose: Efficient on-disk columnar storage.
    • Optimization: Compression, I/O efficiency, long-term persistence.
    • Use Case: Data lakes, data warehousing, batch processing, archiving.
    • Output: Files on disk.
  • Apache Arrow:
    • Purpose: Standardized in-memory columnar data format.
    • Optimization: Zero-copy reads, inter-process communication, fast analytics.
    • Use Case: Real-time analytics, data interchange between systems (Pandas, Spark, Dremio), ETL acceleration.
    • Output: Data in RAM.

Synergy and Practical Applications: When and How They Work Together

The beauty of Apache Parquet and Arrow lies not in choosing one over the other, but in leveraging their synergy. In modern data architectures, they often work hand-in-hand to create highly optimized data pipelines. A typical workflow involves storing vast amounts of data efficiently in Parquet format on a distributed file system like HDFS or cloud storage like Amazon S3. When this data needs to be processed, an engine like Apache Spark or Dremio reads the Parquet files from disk, and then, crucially, converts Question & Answer :

I’m looking into a way to speed up my memory intensive frontend vis app. I saw some people recommend Apache Arrow, while I’m looking into it, I’m confused about the difference between Parquet and Arrow.

They are both columnized data structure. Originally I thought parquet is for disk, and arrow is for in-memory format. However, I just learned that you can save arrow into files at desk as well, like abc.arrow In that case, what’s the difference? Aren’t they doing the same thing?

Parquet is a columnar file format for data serialization. Reading a Parquet file requires decompressing and decoding its contents into some kind of in-memory data structure. It is designed to be space/IO-efficient at the expense of CPU utilization for decoding. It does not provide any data structures for in-memory computing. Parquet is a streaming format which must be decoded from start-to-end, while some “index page” facilities have been added to the storage format recently, in general random access operations are costly.

Arrow on the other hand is first and foremost a library providing columnar data structures for in-memory computing. When you read a Parquet file, you can decompress and decode the data into Arrow columnar data structures, so that you can then perform analytics in-memory on the decoded data. Arrow columnar format has some nice properties: random access is O(1) and each value cell is next to the previous and following one in memory, so it’s efficient to iterate over.

What about “Arrow files” then? Apache Arrow defines a binary “serialization” protocol for arranging a collection of Arrow columnar arrays (called a “record batch”) that can be used for messaging and interprocess communication. You can put the protocol anywhere, including on disk, which can later be memory-mapped or read into memory and sent elsewhere.

This Arrow protocol is designed so that you can “map” a blob of Arrow data without doing any deserialization, so performing analytics on Arrow protocol data on disk can use memory-mapping and pay effectively zero cost. The protocol is used for many things, such as streaming data between Spark SQL and Python for running pandas functions against chunks of Spark SQL data, these are called “pandas udfs”.

In some applications, Parquet and Arrow can be used interchangeably for on-disk data serialization. Some things to keep in mind:

  • Parquet is intended for “archival” purposes, meaning if you write a file today, we expect that any system that says they can “read Parquet” will be able to read the file in 5 years or 7 years. We are not yet making this assertion about long-term stability of the Arrow format (though we might in the future)
  • Parquet is generally a lot more expensive to read because it must be decoded into some other data structure. Arrow protocol data can simply be memory-mapped.
  • Parquet files are often much smaller than Arrow-protocol-on-disk because of the data encoding schemes that Parquet uses. If your disk storage or network is slow, Parquet is going to be a better choice

So, in summary, Parquet files are designed for disk storage, Arrow is designed for in-memory (but you can put it on disk, then memory-map later). They are intended to be compatible with each other and used together in applications.

For a memory-intensive frontend app I might suggest looking at the Arrow JavaScript (TypeScript) library.