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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.

What is Apache Hudi?

Hudi is a versatile framework designed for the development of streaming data lakes, which seamlessly integrates incremental data pipelines within a self-managing database context, while also catering to lake engines and traditional batch processing methods. This platform maintains a detailed historical timeline that captures all operations performed on the table, allowing for real-time data views and efficient retrieval based on the sequence of arrival. Each Hudi instant is comprised of several critical components that bolster its capabilities. Hudi stands out in executing effective upserts by maintaining a direct link between a specific hoodie key and a file ID through a sophisticated indexing framework. This connection between the record key and the file group or file ID remains intact after the original version of a record is written, ensuring a stable reference point. Essentially, the associated file group contains all iterations of a set of records, enabling effortless management and access to data over its lifespan. This consistent mapping not only boosts performance but also streamlines the overall data management process, making it considerably more efficient. Consequently, Hudi's design provides users with the tools necessary for both immediate data access and long-term data integrity.

Media

Media

Integrations Supported

Actian Data Observability
Apache Flink
Apache Hive
Apache Spark
CelerData Cloud
Onehouse
PuppyGraph
Amazon Data Firehose
Daft
Dremio
Google Cloud Lakehouse
OpenMetadata
SQL
Stackable
Trino

Integrations Supported

Actian Data Observability
Apache Flink
Apache Hive
Apache Spark
CelerData Cloud
Onehouse
PuppyGraph
Alluxio
Apache Kafka
MySQL
PostgreSQL
e6data

API Availability

API Availability

Pricing Information

Free
Open source
Free Version

Pricing Information

Pricing not provided

Supported Platforms

SaaS

Supported Platforms

SaaS

Customer Service / Support

Web-Based Support

Customer Service / Support

Web-Based Support

Training Options

Documentation Hub

Training Options

Documentation Hub

Company Facts

Organization Name

Apache Software Foundation

Date Founded

1999

Company Location

United States

Company Website

iceberg.apache.org

Company Facts

Organization Name

Apache Corporation

Date Founded

1954

Company Location

United States

Company Website

hudi.apache.org

Categories and Features

Big Data

Not specified

Categories and Features

Data Warehouse

Not specified

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