博客來Overcome challenges in building transactional guarantees on rapidly changing data by using Apache Hudi. With this practical guide, data engineers, data architects, and software architects will discover how to seamlessly build an interoperable lakehouse from disparate data sources and deliver faster insights using your query engine of choice.
Authors Shiyan Xu, Prashant Wason, Bhavani Sudha Saktheeswaran, and Rebecca Bilbro provide practical examples and insights to help you unlock the full potential of data lakehouses for different levels of analytics, from batch to interactive to streaming. You’ll also learn how to evaluate storage choices and leverage built-in automated table optimizations to build, maintain, and operate production data applications.
- Understand the need for transactional data lakehouses and the challenges associated with building them
- Explore data ecosystem support provided by Apache Hudi for popular data sources and query engines
- Perform different write and read operations on Apache Hudi tables and effectively use them for various use cases, including batch and stream applications
- Apply different storage techniques and considerations such as indexing and clustering to maximize your lakehouse performance
- Build end-to-end incremental data pipelines using Apache Hudi for faster ingestion and fresher analytics
