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Source channel @githubtrending · Post #15049 · Aug 12

#java#distributed_systems#durable_execution#grpc#java#javascript#microservice_orchestration#orchestration_engine#orchestrator#reactjs#spring_boot#workflow_automation#workflow_engine#workflow_management#workflows Conductor is an open-source tool that helps you manage and automate complex workflows involving many microservices and systems. It makes your workflows flexible, reliable, and scalable by handling retries, errors, and monitoring automatically. You can define workflows as code in JSON, use various task types, and manage workflows dynamically without tightly coupling services. It offers an easy-to-use web interface and supports multiple databases like Redis and MySQL. This helps you build, run, and monitor workflows efficiently, saving time and reducing errors in managing distributed applications. It also has SDKs for Java, Python, JavaScript, Go, and C# to integrate easily with your projects. https://github.com/conductor-oss/conductor

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#java #distributed_systems #durable_execution #grpc #javascript #microservice_orchestration #orchestration_engine #orchestrator #reactjs #spring_boot #workflow_automation #workflow_engine #workflow_management #workflows

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djangoproject

@djangoproject · Post #423 · 08/26/2017, 08:39 AM

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http://scitools.org.uk/iris/docs/latest/userguide/index.html Iris seeks to provide a powerful, easy to use, and community-driven Python library for analysing and visualising #meteorological and #oceanographic data sets. With Iris you can: Use a single #API to work on your data, irrespective of its original format. Read and write (CF-)netCDF, GRIB, and PP files. Easily produce graphs and maps via integration with #matplotlib and #cartopy.

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#meteorological #oceanographic #api #matplotlib #cartopy

djangoproject

@djangoproject · Post #424 · 08/26/2017, 08:43 AM

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http://scitools.org.uk/cartopy/docs/latest/index.html Cartopy is a Python package designed to make drawing maps for data analysis and visualisation as easy as possible. #Cartopy makes use of the powerful #PROJ.4, #numpy and #shapely libraries and has a simple and intuitive drawing interface to #matplotlib for creating publication quality maps. Some of the key features of cartopy are: object oriented projection definitions point, line, vector, polygon and image transformations between projections integration to expose advanced mapping in matplotlib with a simple and intuitive interface powerful vector data handling by integrating shapefile reading with Shapely capabilities

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#cartopy #proj #numpy #shapely #matplotlib