Free Copy of Apache Iceberg the Definitive Guide


Free Apache Iceberg Crash Course


Iceberg Lakehouse Engineering Video Playlist Managing and processing large datasets efficiently is crucial for many organizations. One of the key factors in data efficiency is the format in which data is stored and retrieved. Among the numerous file formats available, Apache Parquet has emerged as a popular choice, particularly in big data and cloud-based environments. But what exactly is the Parquet file format, and why is it so widely adopted? In this post, we’ll introduce you to the key concepts behind Parquet, its structure, and why it has become a go-to solution for data engineers and analysts alike. What is Parquet? Parquet is an open-source, columnar storage file format designed for efficient data storage and retrieval. Unlike row-based formats (like CSV or JSON), Parquet organizes data by columns rather than rows, making it highly efficient for analytical workloads. However, Parquet is used with various processing engines such as Apache Spark, Dremio, and Presto, and it works seamlessly with cloud platforms like AWS S3, Google Cloud Storage, and Azure. Why Use Parquet? The design of Parquet provides several key benefits that make it ideal for large-scale data processing: Efficient CompressionParquet’s columnar format allows for highly efficient compression. Since data is stored by column, similar values are grouped together, making compression algorithms far more effective compared to row-based formats. This can significantly reduce the storage footprint of your datasets.

Faster QueriesColumnar storage enables faster query execution for analytical workloads. When executing a query, Parquet allows data processing engines to scan only the columns relevant to the query, rather than reading the entire dataset. This reduces the amount of data that needs to be read, resulting in faster query times.

Schema EvolutionParquet supports schema evolution, which means you can modify the structure of your data (e.g., adding or removing columns) without breaking existing applications. This flexibility is particularly useful in dynamic environments where data structures evolve over time.

Cross-Platform CompatibilityParquet is compatible with multiple languages and tools, including Python, Java, C++, and many data processing frameworks. This makes it an excellent choice for multi-tool environments where data needs to be processed by different systems. The Difference Between Row-Based and Columnar Formats To fully understand the benefits of Parquet, it's essential to grasp the distinction between row-based and columnar file formats. Row-based formats store all the fields of a record together in sequence. Formats like CSV or JSON are row-based. These are suitable for transactional systems where entire rows need to be read and written frequently.
Columnar formats, like Parquet, store each column of a dataset together. This approach is advantageous for analytical workloads, where operations like aggregations or filters are performed on individual columns. For example, in a dataset with millions of rows and many columns, if you only need to perform analysis on one or two columns, Parquet allows you to read just those columns, avoiding the need to scan the entire dataset. Key Features of Parquet Parquet is packed with features that make it well-suited for a wide range of data use cases: Columnar Storage: As mentioned, the format stores data column-wise, making it ideal for read-heavy, analytical queries.
Efficient Compression: Parquet supports multiple compression algorithms (Snappy, Gzip, Brotli) that significantly reduce data size.
Splittable Files: Parquet files are splittable, meaning large files can be divided into smaller chunks for parallel processing.
Rich Data Types: Parquet supports complex nested data types, such as arrays, structs, and maps, allowing for flexible schema designs. When to Use Parquet Parquet is an excellent choice for scenarios where: You have large datasets that need to be processed for analytics.
Your queries often target specific columns in a dataset rather than entire rows.
You need efficient compression to reduce storage costs.
You're working in a distributed data environment, such as Hadoop, Spark, or cloud-based data lakes. However, Parquet may not be ideal for small, frequent updates or transactional systems where row-based formats are more suitable. Conclusion The Apache Parquet file format is a powerful tool for efficiently storing and querying large datasets. With its columnar storage design, Parquet provides superior compression, faster query execution, and flexibility through schema evolution. These advantages make it a preferred choice for big data processing and cloud environments. In the upcoming parts of this blog series, we’ll dive deeper into Parquet’s architecture, how it handles compression, encoding, and how you can work with Parquet in various tools like Python, Spark, and Dremio. Stay tuned for the next post in this series: Parquet's Columnar Storage Model. Free Copy of Apache Iceberg the Definitive Guide Free Apache Iceberg Crash Course Iceberg Lakehouse Engineering Video Playlist Free Copy of Apache Iceberg the Definitive Guide Free Copy of Apache Iceberg the Definitive Guide Free Copy of Apache Iceberg the Definitive Guide Free Apache Iceberg Crash Course Free Apache Iceberg Crash Course Free Apache Iceberg Crash Course Iceberg Lakehouse Engineering Video Playlist Iceberg Lakehouse Engineering Video Playlist Iceberg Lakehouse Engineering Video Playlist Managing and processing large datasets efficiently is crucial for many organizations. One of the key factors in data efficiency is the format in which data is stored and retrieved. Among the numerous file formats available, Apache Parquet has emerged as a popular choice, particularly in big data and cloud-based environments. But what exactly is the Parquet file format, and why is it so widely adopted? In this post, we’ll introduce you to the key concepts behind Parquet, its structure, and why it has become a go-to solution for data engineers and analysts alike. Apache Parquet What is Parquet? Parquet is an open-source, columnar storage file format designed for efficient data storage and retrieval. Unlike row-based formats (like CSV or JSON), Parquet organizes data by columns rather than rows, making it highly efficient for analytical workloads. However, Parquet is used with various processing engines such as Apache Spark, Dremio, and Presto, and it works seamlessly with cloud platforms like AWS S3, Google Cloud Storage, and Azure. open-source, columnar storage file format Why Use Parquet? The design of Parquet provides several key benefits that make it ideal for large-scale data processing: Efficient CompressionParquet’s columnar format allows for highly efficient compression. Since data is stored by column, similar values are grouped together, making compression algorithms far more effective compared to row-based formats. This can significantly reduce the storage footprint of your datasets. Faster QueriesColumnar storage enables faster query execution for analytical workloads. When executing a query, Parquet allows data processing engines to scan only the columns relevant to the query, rather than reading the entire dataset. This reduces the amount of data that needs to be read, resulting in faster query times. Schema EvolutionParquet supports schema evolution, which means you can modify the structure of your data (e.g., adding or removing columns) without breaking existing applications. This flexibility is particularly useful in dynamic environments where data structures evolve over time. Cross-Platform CompatibilityParquet is compatible with multiple languages and tools, including Python, Java, C++, and many data processing frameworks. This makes it an excellent choice for multi-tool environments where data needs to be processed by different systems. Efficient CompressionParquet’s columnar format allows for highly efficient compression. Since data is stored by column, similar values are grouped together, making compression algorithms far more effective compared to row-based formats. This can significantly reduce the storage footprint of your datasets. Efficient Compression Parquet’s columnar format allows for highly efficient compression. Since data is stored by column, similar values are grouped together, making compression algorithms far more effective compared to row-based formats. This can significantly reduce the storage footprint of your datasets. Efficient Compression Faster QueriesColumnar storage enables faster query execution for analytical workloads. When executing a query, Parquet allows data processing engines to scan only the columns relevant to the query, rather than reading the entire dataset. This reduces the amount of data that needs to be read, resulting in faster query times. Faster Queries Columnar storage enables faster query execution for analytical workloads. When executing a query, Parquet allows data processing engines to scan only the columns relevant to the query, rather than reading the entire dataset. This reduces the amount of data that needs to be read, resulting in faster query times. Faster Queries Schema EvolutionParquet supports schema evolution, which means you can modify the structure of your data (e.g., adding or removing columns) without breaking existing applications. This flexibility is particularly useful in dynamic environments where data structures evolve over time. Schema Evolution Parquet supports schema evolution, which means you can modify the structure of your data (e.g., adding or removing columns) without breaking existing applications. This flexibility is particularly useful in dynamic environments where data structures evolve over time. Schema Evolution Cross-Platform CompatibilityParquet is compatible with multiple languages and tools, including Python, Java, C++, and many data processing frameworks. This makes it an excellent choice for multi-tool environments where data needs to be processed by different systems. Cross-Platform Compatibility Parquet is compatible with multiple languages and tools, including Python, Java, C++, and many data processing frameworks. This makes it an excellent choice for multi-tool environments where data needs to be processed by different systems. Cross-Platform Compatibility The Difference Between Row-Based and Columnar Formats To fully understand the benefits of Parquet, it's essential to grasp the distinction between row-based and columnar file formats. Row-based formats store all the fields of a record together in sequence. Formats like CSV or JSON are row-based. These are suitable for transactional systems where entire rows need to be read and written frequently. Columnar formats, like Parquet, store each column of a dataset together. This approach is advantageous for analytical workloads, where operations like aggregations or filters are performed on individual columns. Row-based formats store all the fields of a record together in sequence. Formats like CSV or JSON are row-based. These are suitable for transactional systems where entire rows need to be read and written frequently. Row-based formats Columnar formats , like Parquet, store each column of a dataset together. This approach is advantageous for analytical workloads, where operations like aggregations or filters are performed on individual columns. Columnar formats For example, in a dataset with millions of rows and many columns, if you only need to perform analysis on one or two columns, Parquet allows you to read just those columns, avoiding the need to scan the entire dataset. Key Features of Parquet Parquet is packed with features that make it well-suited for a wide range of data use cases: Columnar Storage: As mentioned, the format stores data column-wise, making it ideal for read-heavy, analytical queries. Efficient Compression: Parquet supports multiple compression algorithms (Snappy, Gzip, Brotli) that significantly reduce data size. Splittable Files: Parquet files are splittable, meaning large files can be divided into smaller chunks for parallel processing. Rich Data Types: Parquet supports complex nested data types, such as arrays, structs, and maps, allowing for flexible schema designs. Columnar Storage : As mentioned, the format stores data column-wise, making it ideal for read-heavy, analytical queries. Columnar Storage Efficient Compression : Parquet supports multiple compression algorithms (Snappy, Gzip, Brotli) that significantly reduce data size. Efficient Compression Splittable Files : Parquet files are splittable, meaning large files can be divided into smaller chunks for parallel processing. Splittable Files Rich Data Types : Parquet supports complex nested data types, such as arrays, structs, and maps, allowing for flexible schema designs. Rich Data Types When to Use Parquet Parquet is an excellent choice for scenarios where: You have large datasets that need to be processed for analytics. Your queries often target specific columns in a dataset rather than entire rows. You need efficient compression to reduce storage costs. You're working in a distributed data environment, such as Hadoop, Spark, or cloud-based data lakes. You have large datasets that need to be processed for analytics. Your queries often target specific columns in a dataset rather than entire rows. You need efficient compression to reduce storage costs. You're working in a distributed data environment, such as Hadoop, Spark, or cloud-based data lakes. However, Parquet may not be ideal for small, frequent updates or transactional systems where row-based formats are more suitable. Conclusion The Apache Parquet file format is a powerful tool for efficiently storing and querying large datasets. With its columnar storage design, Parquet provides superior compression, faster query execution, and flexibility through schema evolution. These advantages make it a preferred choice for big data processing and cloud environments. In the upcoming parts of this blog series, we’ll dive deeper into Parquet’s architecture, how it handles compression, encoding, and how you can work with Parquet in various tools like Python, Spark, and Dremio. Stay tuned for the next post in this series: Parquet's Columnar Storage Model . Parquet's Columnar Storage Model

Walkthroughs, tutorials, guides, and tips. This story will teach you how to do something new or how to do something better.

 All About Parquet Part 02 - Parquet's Columnar Storage Model

All About Parquet Part 01 - An Introduction

About Author

Comments

TOPICS

THIS ARTICLE WAS FEATURED IN

Related Stories

Untitled Story

10 Reasons Why Apache Iceberg Will Dominate Data Lakehouses in 2025

Branches & Tags: Comparing Iceberg, Hudi, and Delta Lake Tables

Creating a Data Lakehouse using Apache Iceberg and MinIO

Data Lakehouse Table Formats: Considerations, Reflection, and Perspective

What the Interoperability Trend in Open Table Formats Signify for Enterprise Data Architectures

10 Reasons Why Apache Iceberg Will Dominate Data Lakehouses in 2025

Branches & Tags: Comparing Iceberg, Hudi, and Delta Lake Tables

Creating a Data Lakehouse using Apache Iceberg and MinIO

Data Lakehouse Table Formats: Considerations, Reflection, and Perspective

What the Interoperability Trend in Open Table Formats Signify for Enterprise Data Architectures

Light-Mode

Classic

Newspaper

Dark-Mode

Neon Noir

Minty

HN StartUps