HadoopSpark

HadoopSpark is a term used to describe an integration approach that combines Apache Hadoop's storage and cluster management capabilities with Apache Spark's in-memory processing engine. It is not an official Apache project, but a concept used in literature and vendor documentation to indicate running Spark workloads on a Hadoop-based data architecture, leveraging components such as HDFS for storage and YARN for resource management.

In a HadoopSpark configuration, data typically resides in HDFS or a compatible data lake, while Spark executes on the cluster using YARN or a standalone Spark deployment. Spark can access data through Hadoop input formats and the Hive Metastore, and it supports common Hadoop data formats such as Parquet and ORC. Security and governance may be provided by Kerberos, Ranger, or Sentry, and additional Hadoop ecosystem components like Hive, HBase, or Kafka may be integrated for metadata management and streaming.

The combination enables batch processing, iterative analytics, and data transformation pipelines, with Spark handling in-memory computation to accelerate workloads. Structured Streaming or Spark Streaming can be used for real-time or near-real-time analytics, while MLlib supports machine learning tasks within the same ecosystem. The approach is often used for data lake modernization, blending robust storage with flexible, scalable processing across large datasets.

Limitations and considerations include operational complexity, version and compatibility management between Hadoop and Spark components, potential resource contention, and the need for careful memory configuration. Organizations must plan for data governance, security posture, and upgrade paths when adopting a HadoopSpark-style architecture, balancing the benefits of in-memory analytics with the overhead of maintaining multiple Hadoop and Spark layers.