Get Started Free
No time limit - totally free - just the way you like it.Sign Up Now
Apache Kudu is an open-source storage engine that enables fast analytics on fast and changing data. It was developed to fill the gap between Hadoop's HDFS (Hadoop Distributed File System) and HBase key-value store. Kudu stores data in a columnar format, which makes data retrieval much faster compared to traditional row-based storage. It is designed to support real-time analytic workloads and offers excellent performance for random access and columnar scans.
Under the hood, Apache Kudu stores data in a columnar format and uses a write-ahead log for crash recovery. The data is organized into tablets, which can be partitioned and replicated across a cluster. Kudu also supports predicate push-down, which allows user queries to be optimized and distributed across the cluster for maximum performance. Kudu's architecture allows it to work seamlessly with Hadoop's ecosystem.
Apache Kudu is an important tool for businesses that require real-time analytics and data processing. Traditional data storage engines, such as HDFS and HBase, have limitations when it comes to accessing and processing fast-changing data. Kudu's columnar storage and efficient write-ahead log designed for high-speed data ingestion and updates enable real-time analytics on fast-moving data, making it an ideal solution for many use cases.
Some of the benefits of Apache Kudu include:
Apache Kudu is used in a variety of industries for real-time analytics and data processing. Some of the most common use cases for Apache Kudu include:
Some technologies or terms that are closely related to Apache Kudu include:
Dremio users would be interested in Apache Kudu because it offers an efficient and fast way to store and retrieve data for real-time analytics and data processing. Dremio's self-service data platform integrates with Apache Kudu, allowing users to query and analyze data in real-time without the need for complex ETL pipelines. Additionally, Apache Kudu's seamless integration with Hadoop's ecosystem makes it an ideal storage engine for Dremio's platform.