StackHDFSbased

StackHDFSbased is a data processing and storage stack designed to operate on top of the Hadoop Distributed File System. It integrates storage, processing engines, and governance components into a cohesive platform for large-scale data workloads.

The architecture comprises a distributed storage layer using HDFS, a resource management layer (such as YARN or a compatible substitute), pluggable processing engines (MapReduce, Spark, Flink), and a metadata or data catalog for lineage and discovery. The design emphasizes data locality and fault tolerance provided by HDFS, with modular adapters to support various compute engines.

Functions include batch and streaming processing, efficient data access via HDFS caches, and support for common data formats such as Parquet, ORC, and Avro. Security features encompass Kerberos-based authentication, access control lists, and optional encryption, while governance features cover data lineage, schema evolution, and data quality checks.

Deployment options range from on-premises clusters to hybrid environments; the stack can be deployed as managed services in cloud contexts with HDFS-compatible storage. It relies on the Hadoop ecosystem tooling for monitoring and administration and integrates with existing data pipelines and scheduling mechanisms.

Advantages of StackHDFSbased include tight integration with mature HDFS storage, strong fault tolerance, and scalable batch and streaming capabilities. Limitations include operational complexity, dependency on Hadoop infrastructure, and potential divergence from newer cloud-native data lake architectures.

See also Hadoop, HDFS, YARN, Apache Spark, Apache Flink, Parquet, ORC, Avro.