Hudi introduced a fundamentally new way of building a serverless yet massively distributed database abstraction over lake storage that other projects have since adopted. Apache Hudi provides transactional updates, deletes and incremental change streams from tables on lake storage. Over the past several years, the Hudi community has also steadily built a rich set of platform components that enable users to go to production quickly with end solutions to common problems like data ingestion, compaction, CDC, or incremental ETLs.
What is Hudi?
Hudi (Hadoop Upserts Deletes and Incrementals) is one of the open-source technologies supported by Apache, used to manage the input and storage of large analytical datasets on Hadoop-compatible file systems, including HDFS and cloud-based object storage services. It is designed to provide efficient data entry and preparation with low latency.
Hudi allows users to gradually extract only modified data, which significantly improves the efficiency of requests. It scales horizontally and can be used from any Spark task. The main advantage is Hudi`s efficient incremental data processing capabilities.
What tasks does Hudi solve?
Apache Hudi is one of the best formats explicitly designed for data lakes. It solves the following problems:
Data integrity. Atomic transactions ensure that update and add operations in the lake do not fail halfway through. It avoids data corruption.
Consistency of updates. They prevent reading from failing or incomplete results from being returned during writing. They also solve the task of possible simultaneous writing.
Scalability of data and metadata. They eliminate bottlenecks with object storage APIs and associated metadata when tables grow to thousands of batches and billions of files.
• Scalability, overcome limitations of HDFS.
• Fast data view in Hadoop,
• Support for updating and deleting existing data.
• Fast ETL and modeling.
Data Lakehouse Valuable component
Hudi is used as a component in building a Data Lakehouse architecture.
It is designed to handle large-scale data sets and provides capabilities for managing incremental updates and deletes in a Data Lake environment. It enables efficient data ingestion, data management, and query capabilities on top of existing data lakes. Hudi achieves this by leveraging Apache Hadoop ecosystem technologies such as Apache Spark, Apache Parquet, and Apache Avro.
Using Hudi, organizations can achieve features like data versioning, change data capture, and near real-time data processing in their Data Lakehouse architecture. Hudi provides batch and streaming data processing capabilities, making it suitable for a wide range of use cases.
Hudi fills a vast data processing gap in HDFS to coexist well with some big data technologies. Hudi is best used to perform insert/update operations in parquet format over HDFS.