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What is Apache Parquet?

Parquet was created to offer the advantages of efficient and compressed columnar data formats across all initiatives within the Hadoop ecosystem. It takes into account complex nested data structures and utilizes the record shredding and assembly method described in the Dremel paper, which we consider to be a superior approach compared to just flattening nested namespaces. This format is specifically designed for maximum compression and encoding efficiency, with numerous projects demonstrating the substantial performance gains that can result from the effective use of these strategies. Parquet allows users to specify compression methods at the individual column level and is built to accommodate new encoding technologies as they arise and become accessible. Additionally, Parquet is crafted for widespread applicability, welcoming a broad spectrum of data processing frameworks within the Hadoop ecosystem without showing bias toward any particular one. By fostering interoperability and versatility, Parquet seeks to enable all users to fully harness its capabilities, enhancing their data processing tasks in various contexts. Ultimately, this commitment to inclusivity ensures that Parquet remains a valuable asset for a multitude of data-centric applications.

What is Apache Iceberg?

Iceberg is an advanced format tailored for high-performance large-scale analytics, merging the user-friendly nature of SQL tables with the robust demands of big data. It allows multiple engines, including Spark, Trino, Flink, Presto, Hive, and Impala, to access the same tables seamlessly, enhancing collaboration and efficiency. Users can execute a variety of SQL commands to incorporate new data, alter existing records, and perform selective deletions. Moreover, Iceberg has the capability to proactively optimize data files to boost read performance, or it can leverage delete deltas for faster updates. By expertly managing the often intricate and error-prone generation of partition values within tables, Iceberg minimizes unnecessary partitions and files, simplifying the query process. This optimization leads to a reduction in additional filtering, resulting in swifter query responses, while the table structure can be adjusted in real time to accommodate evolving data and query needs, ensuring peak performance and adaptability. Additionally, Iceberg’s architecture encourages effective data management practices that are responsive to shifting workloads, underscoring its significance for data engineers and analysts in a rapidly changing environment. This makes Iceberg not just a tool, but a critical asset in modern data processing strategies.

Media

Media

Integrations Supported

Amazon Data Firehose
PuppyGraph
Streamkap
Arroyo
Astera Dataprep
Ficstar
Gable
Hadoop
OpenObserve
PI.EXCHANGE
SDF
Sliq
Tad
Tictable
e6data

Integrations Supported

Amazon Data Firehose
PuppyGraph
Streamkap
Apache Spark
Dell AI-Ready Data Platform
Google Cloud Lakehouse
OpenMetadata
Salesforce Data 360

API Availability

API Availability

Pricing Information

Pricing not provided

Pricing Information

Free
Open source
Free Version

Supported Platforms

Windows
Mac
Linux

Supported Platforms

SaaS

Customer Service / Support

Web-Based Support

Customer Service / Support

Web-Based Support

Training Options

Documentation Hub
Webinars

Training Options

Documentation Hub

Company Facts

Organization Name

The Apache Software Foundation

Date Founded

1999

Company Location

United States

Company Website

parquet.apache.org

Company Facts

Organization Name

Apache Software Foundation

Date Founded

1999

Company Location

United States

Company Website

iceberg.apache.org

Categories and Features

Columnar Databases

Not specified

Categories and Features

Big Data

Not specified

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