Search Results for "event processing"

Bytewax is a Python framework that simplifies event and stream processing. Because Bytewax couples the stream and event processing capabilities of Flink, Spark, and Kafka Streams with the friendly and familiar interface of Python, you can re-use the Python libraries you already know and love. Connect data sources, run stateful transformations, and write to various downstream systems with built-in connectors or existing Python libraries.

Lithops is an open-source serverless computing framework that enables transparent execution of Python functions across multiple cloud providers and on-prem infrastructure. It abstracts cloud providers like IBM Cloud, AWS, Azure, and Google Cloud into a unified interface and turns your Python functions into scalable, event-driven workloads. Lithops is ideal for data processing, ML inference, and embarrassingly parallel workloads, giving you the power of FaaS (Function-as-a-Service) without vendor lock-in. It also supports hybrid cloud setups, object storage access, and simple integration with Jupyter notebooks.

Originally a reimplementation of OpenGroupware's ZideStore. While compatible with legacy ZideStore Coils provides a sophisticated workflow system with ETL and integration capabilities and superior WebDAV/CalDAV features and compatibility. The workflow engine suppports processes described in BPML and provides integration with a variety of services include SSH, LPD, LDAP, and relation databases.

Atgen A2 is a radically simple IT automation solution that automates application builds, continuous testing, data parsing & processing, report generation, batch processing, job scheduling, and many other IT needs. Avoid running repetitive batch jobs and managing access to deploy and update your applications — automate in a language that approaches plain English, using SSH or WinRM, with no agents to install on remote systems. https://www.atgensoft.com/ You can install a released version...

StreamMine is a distributed event processing (streaming) infrastructure. You can create low-latency, fault-tolerant stream processing functionality with any stream-oriented operators that can be implemented in Python.