#python #docker #docker_compose #orchestration

https://github.com/docker/compose

#go #cloud_native #cluster_management #distributed_cloud #edge_cloud #hybrid_cloud #kubernetes #multicloud #orchestration #out_of_the_box #public_cloud

https://github.com/clusternet/clusternet

#java #data #data_engineering #data_orchestration #data_orchestrator #data_pipeline #dataflow #elt #etl #kestra #orchestration #pipeline #scheduler #workflow #workflow_automation #workflow_engine

https://github.com/kestra-io/kestra

#java #cloud #cloudstack #iaas #infrastructure #kubernetes #kvm #libvirt #orchestration #python #virtual_machine #virtualization #vmware #vsphere #xcp_ng #xenserver

https://github.com/apache/cloudstack

#typescript #agent #ai #anthropic #backend_as_a_service #chatbot #gemini #genai #gpt #gpt_4 #llama3 #llm #llmops #nextjs #openai #orchestration #python #rag #workflow #workflows

Dify is an open-source platform for developing AI applications, especially those using Large Language Models (LLMs). It offers a user-friendly interface to build and test AI workflows, integrate various LLMs, and manage models. Key features include a visual workflow builder, comprehensive model support (including GPT, Mistral, and more), a prompt IDE for crafting and testing prompts, RAG pipeline capabilities for document ingestion and retrieval, and agent capabilities with pre-built tools like Google Search and DALL·E.

Using Dify, you can quickly move from prototyping to production with features like observability to monitor application performance and backend-as-a-service for easy integration into your business logic. You can deploy Dify via their cloud service or self-host it in your environment. This makes it highly versatile and beneficial for developers looking to leverage AI efficiently in their projects.

https://github.com/langgenius/dify

#go #docker #docker_compose #go #golang #orchestration

Docker Compose is a tool that helps you run multiple containers for your application easily. You define how your containers should be set up using a Compose file, and then you can start your entire application with just one command: `docker compose up`. This makes it simple to manage and run complex applications. You can get Docker Compose through Docker Desktop on Windows and macOS, or download it manually for Linux. Using Docker Compose saves time and effort by automating the setup and execution of your multi-container applications.

https://github.com/docker/compose

#java #airflow #azkaban #cloud_native #data_pipelines #job_scheduler #orchestration #powerful_data_pipelines #task_scheduler #workflow #workflow_orchestration #workflow_schedule

Apache DolphinScheduler is a powerful tool for managing data workflows. It makes it easy to create and manage complex tasks with a user-friendly interface and low-code options. You can deploy it in several ways, including standalone, cluster, Docker, and Kubernetes, making it flexible for different environments. It's highly reliable, scalable, and performs much faster than other platforms, supporting millions of tasks daily. The tool also offers features like versioning, state control of workflows, multi-tenancy support, and permission control. This helps you manage your data pipelines efficiently and reliably, saving time and effort.

https://github.com/apache/dolphinscheduler

#python #analytics #dagster #data_engineering #data_integration #data_orchestrator #data_pipelines #data_science #etl #metadata #mlops #orchestration #python #scheduler #workflow #workflow_automation

Dagster is a tool that helps you manage and automate your data workflows. You can define your data assets, like tables or machine learning models, using Python functions. Dagster then runs these functions at the right time and keeps your data up-to-date. It offers features like integrated lineage and observability, making it easier to track and manage your data. This tool is useful for every stage of data development, from local testing to production, and it integrates well with other popular data tools. Using Dagster, you can build reusable components, spot data quality issues early, and scale your data pipelines efficiently. This makes your work more productive and helps maintain control over complex data systems.

https://github.com/dagster-io/dagster

#python #airflow #apache #apache_airflow #automation #dag #data_engineering #data_integration #data_orchestrator #data_pipelines #data_science #elt #etl #machine_learning #mlops #orchestration #python #scheduler #workflow #workflow_engine #workflow_orchestration

Apache Airflow is a tool that helps you manage and automate workflows. You can write your workflows as code, making them easier to maintain, version, test, and collaborate on. Airflow lets you schedule tasks and monitor their progress through a user-friendly interface. It supports dynamic pipeline generation, is highly extensible, and scalable, allowing you to define your own operators and executors.

Using Airflow benefits you by making your workflows more organized, efficient, and reliable. It simplifies the process of managing complex tasks and provides clear visualizations of your workflow's performance, helping you identify and troubleshoot issues quickly. This makes it easier to manage data processing and other automated tasks effectively.

https://github.com/apache/airflow

#python #automation #data #data_engineering #data_ops #data_science #infrastructure #ml_ops #observability #orchestration #pipeline #prefect #python #workflow #workflow_engine

Prefect is a tool that helps you automate and manage data workflows in Python. It makes it easy to turn your scripts into reliable and flexible workflows that can handle unexpected changes. With Prefect, you can schedule tasks, retry failed operations, and monitor your workflows. You can install it using `pip install -U prefect` and start creating workflows with just a few lines of code. This helps data teams work more efficiently, reduce errors, and save time. You can also use Prefect Cloud for more advanced features and support.

https://github.com/PrefectHQ/prefect