已使用

Uber

Hudi最初由Uber开发，用于实现低延迟、高效率的数据库摄取。 Hudi自2016年8月开始在生产环境上线，在Hadoop上驱动约100个非常关键的业务表，支撑约几百TB的数据规模(前10名包括行程、乘客、司机)。 Hudi还支持几个增量的Hive ETL管道，并且目前已集成到Uber的数据分发系统中。

EMIS Health

EMIS Health是英国最大的初级保健IT软件提供商，其数据集包括超过5000亿的医疗保健记录。HUDI用于管理生产中的分析数据集，并使其与上游源保持同步。Presto用于查询以HUDI格式写入的数据。

Yields.io

Yields.io是第一个使用AI在企业范围内进行自动模型验证和实时监控的金融科技平台。他们的数据湖由Hudi管理，他们还积极使用Hudi为增量式、跨语言/平台机器学习构建基础架构。

Yotpo

Hudi在Yotpo有不少用途。首先，在他们的开源ETL框架中集成了Hudi作为CDC管道的输出写入程序，即从数据库binlog生成的事件流到Kafka然后再写入S3。

演讲 & 报告

  1. “Hoodie: Incremental processing on Hadoop at Uber” - By Vinoth Chandar & Prasanna Rajaperumal Mar 2017, Strata + Hadoop World, San Jose, CA

  2. “Hoodie: An Open Source Incremental Processing Framework From Uber” - By Vinoth Chandar. Apr 2017, DataEngConf, San Francisco, CA Slides Video

  1. “Incremental Processing on Large Analytical Datasets” - By Prasanna Rajaperumal June 2017, Spark Summit 2017, San Francisco, CA. Slides Video

  2. “Hudi: Unifying storage and serving for batch and near-real-time analytics” - By Nishith Agarwal & Balaji Vardarajan September 2018, Strata Data Conference, New York, NY

  3. “Hudi: Large-Scale, Near Real-Time Pipelines at Uber” - By Vinoth Chandar & Nishith Agarwal October 2018, Spark+AI Summit Europe, London, UK

  4. “Powering Uber’s global network analytics pipelines in real-time with Apache Hudi” - By Ethan Guo & Nishith Agarwal, April 2019, Data Council SF19, San Francisco, CA.

  5. “Building highly efficient data lakes using Apache Hudi (Incubating)” - By Vinoth Chandar June 2019, SF Big Analytics Meetup, San Mateo, CA

  6. “Apache Hudi (Incubating) - The Past, Present and Future Of Efficient Data Lake Architectures” - By Vinoth Chandar & Balaji Varadarajan September 2019, ApacheCon NA 19, Las Vegas, NV, USA

文章

  1. “The Case for incremental processing on Hadoop” - O’reilly Ideas article by Vinoth Chandar
  2. “Hoodie: Uber Engineering’s Incremental Processing Framework on Hadoop” - Engineering Blog By Prasanna Rajaperumal