Hadoopcompatible

Hadoop-compatible refers to software, storage, or data formats that can integrate with the Apache Hadoop ecosystem by implementing or exposing Hadoop-compatible interfaces. Such components can participate in Hadoop workloads, operate with Hadoop tooling, and exchange data via the Hadoop FileSystem API and related standards.

It can include storage systems that present a Hadoop-compatible file system (HCFS), enabling Hadoop clients to read and write data as if to HDFS. It also covers processing engines and runtimes that can submit tasks to YARN or run MapReduce jobs, either directly or through compatible APIs. Data formats commonly used in Hadoop workflows, such as Parquet, ORC, Avro, and SequenceFile, are typically described as Hadoop-compatible when they preserve schema, compression, and compatibility across components.

Cloud-based storage and object stores may be marketed as Hadoop-compatible when they provide connectors or adapters (for example, s3a or gcs-connector) that implement the Hadoop FileSystem interface. Similarly, non-traditional compute engines may claim Hadoop compatibility if they can read/write Hadoop data, support Hadoop data formats, or participate in the Hadoop scheduling and resource management model (via YARN or a compatible runtime).

Compatibility is often version-specific and can be partial. Some features may not be fully supported, and performance, security, and consistency characteristics may differ from a native Hadoop deployment. Users should verify compatibility matrices and test workloads when integrating third-party components.

See also: Apache Hadoop, HDFS, MapReduce, YARN, Hadoop ecosystem, Hadoop-compatible storage, Hadoop-compatible file system.