Distx

Distx is a distributed data processing framework designed to simplify the construction of scalable, data-intensive applications. It provides a unified API for both batch and streaming workloads, with built-in support for task orchestration, failure recovery, and data movement across a cluster. Distx emphasizes portability across cloud, on-premises, and hybrid environments and aims to interoperate with common data formats and messaging systems.

Its architecture typically comprises a control plane, a pool of worker processes, and a metadata store. A distributed scheduler translates a user-defined DAG into executable tasks, assigns work to workers, and coordinates data transfers. Workers perform computation, manage intermediate data, and handle retries. The system supports data locality optimization, checkpointing, and lineage tracking to aid reproducibility and auditing.

Key features include a DAG-based execution model, pluggable backends for storage and compute, streaming and batch support within a single framework, and observability through metrics and tracing. Security and access control are commonly implemented via authentication, TLS encryption, and role-based permissions. The API is designed to be language-agnostic or provide bindings for several popular languages.

Typical use cases include Extract-Transform-Load pipelines, feature-engineering workflows for machine learning, real-time analytics, and event-driven processing across distributed data systems.

Distx is an open-source project maintained by a community of contributors and organizations. Development follows public collaboration practices, with releases and documentation produced in public repositories. The project continues to evolve, with ongoing work on performance, reliability, and ecosystem integrations.