MinIO’s Post

Tailscale has been a game-changer in the world of #dataprocessing, enabling users to easily connect to their #hybridcloud VPN from a GitHub Actions workflow. The new Tailscale GitHub action simplifies this process immensely. Learn how to set up a comprehensive data processing pipeline that highlights the use of the Tailscale GitHub Action for secure networking. This setup will also incorporate a GitHub Actions workflow and the MinIO Python SDK. https://hubs.li/Q02DQVxg0

Tailscale has been a game-changer in the world of #dataprocessing, enabling users to easily connect to their #hybridcloud VPN from a GitHub Actions workflow. The new Tailscale GitHub action simplifies this process immensely. In this article, David Cannan explores how to set up a comprehensive data processing pipeline that highlights the use of the Tailscale GitHub Action for secure networking. This setup will also incorporate a GitHub Actions workflow and the MinIO Python SDK. https://hubs.li/Q02CvWwq0

Data processing is a fundamental practice in modern #softwaredevelopment. It enables teams to automate the collection, processing and storage of data, ensuring high-quality #data and efficient handling. Tailscale has been a game-changer, enabling users to easily connect to their #hybridcloud VPN from a GitHub Actions workflow. The Tailscale GitHub action simplifies this process immensely. In this article, David Cannan explores how to set up a comprehensive #dataprocessing pipeline that highlights the use of the Tailscale GitHub Action for secure networking. This setup will also incorporate a GitHub Actions workflow and the MinIO Python SDK. This setup unlocks a new level of security, scalability and flexibility that will take your software development workflow to new heights. https://hubs.li/Q02zMvzt0

What is Apache Airflow? Airflow™ is a platform created by the community to programmatically author, schedule and monitor workflows. Airflow™ is an open-source workflow management platform for data engineering pipelines. It started at Airbnb in October 2014[2] as a solution to manage the company's increasingly complex workflows. Creating Airflow allowed Airbnb to programmatically author and schedule their workflows and monitor them via the built-in Airflow user interface.[3][4] From the beginning, the project was made open source, becoming an Apache Incubator project in March 2016 and a top-level Apache Software Foundation project in January 2019. Scalable Airflow™ has a modular architecture and uses a message queue to orchestrate an arbitrary number of workers. Airflow™ is ready to scale to infinity. Dynamic Airflow™ pipelines are defined in Python, allowing for dynamic pipeline generation. This allows for writing code that instantiates pipelines dynamically. Extensible Easily define your own operators and extend libraries to fit the level of abstraction that suits your environment. Elegant Airflow™ pipelines are lean and explicit. Parametrization is built into its core using the powerful Jinja templating engine. Pure Python No more command-line or XML black-magic! Use standard Python features to create your workflows, including date time formats for scheduling and loops to dynamically generate tasks. This allows you to maintain full flexibility when building your workflows. Useful UI Monitor, schedule and manage your workflows via a robust and modern web application. No need to learn old, cron-like interfaces. You always have full insight into the status and logs of completed and ongoing tasks. Robust Integrations Airflow™ provides many plug-and-play operators that are ready to execute your tasks on Google Cloud Platform, Amazon Web Services, Microsoft Azure and many other third-party services. This makes Airflow easy to apply to current infrastructure and extend to next-gen technologies. Easy to Use Anyone with Python knowledge can deploy a workflow. Apache Airflow™ does not limit the scope of your pipelines; you can use it to build ML models, transfer data, manage your infrastructure, and more. Open Source Wherever you want to share your improvement you can do this by opening a PR. It’s simple as that, no barriers, no prolonged procedures. Airflow has many active users who willingly share their experiences.

🚀 I'm thrilled to share my latest Medium article! As part of my ongoing work in the Data group, I have developed a Python script that significantly improves the tenant offboarding process in DynamoDB's multi-tenant environments. This script is my individual contribution to the realm of cloud data management, automating tenant data deletion while upholding the highest standards of data consistency and security. This innovation is set to redefine efficiency and reliability in managing tenant data. For those immersed in cloud data management or dealing with the complexities of DynamoDB in multi-tenant architectures, this article is a crucial read. Dive in for an in-depth exploration of this pioneering approach to tenant data management, complete with a detailed guide and insights into the script's capabilities. See how my script can enhance your DynamoDB processes, making them more streamlined and secure. Check out the full article for a comprehensive look at this unique solution! #DataManagement #DynamoDB #CloudComputing #PythonProgramming #DevOps #CloudSecurity #TechInnovation #DataEngineering #pyhton #aws #multitenancy

Hey Devs, Have you ever found yourself in the struggle of finding a clear resources on implementing a feature or using a particular tech stack? 🤔 You know how it feels right? 😖 Yeah! That was the position I found myself late last year while building an MVP for an idea with this awesome tech stack (Python, FastAPI, AWS). That's why I decided to write this comprehensive technical guide. 📖 https://lnkd.in/dJvMWN_P It's perfect for fellow developers who want to get started quickly with secure authentication or try out the technologies. 👨💻 Let's empower one another to build secure, reliable applications. Check out the article, share with a techie who might need it, and let me know your thoughts! 👇 #backendengineering #apidevelopment #fastapi #python #authentication #aws #technicalarticle

FastAPI has emerged as a popular framework for building high-performance APIs in Python. Its focus on simplicity, speed, and automatic data validation makes it ideal for modern web applications. Leveraging serverless architectures with AWS Lambda and API Gateway can further enhance your FastAPI app by offering scalability, cost-efficiency, and a pay-per-use model. This article will explain the seamless deployment process of a FastAPI application using AWS Lambda and API Gateway, empowering you to create robust and efficient APIs. Read More: https://lnkd.in/dpinTVE6 #aws #cloudcomputing #awslambda #fastapi #applications #deployment #apigateway #devops

I’m excited to announce that, with the help of Abdulrahman Hassan , Mohamed Abdelmaksoud , and Hania Wael, we successfully completed our Spring Distributed Systems course project, “Distributed Image Processing”!🎉 Throughout this journey, we used a variety of cutting-edge technologies to build a highly scalable and fault-tolerant system. Starting with AWS services, we used EC2 for compute power, EFS for shared storage among nodes, S3 to store processed images, and Elastic Load Balancers to ensure high availability and fault tolerance. Additionally, RabbitMQ was utilized for efficient task queuing and distribution. At the same time, Docker and Docker-Compose were essential for containerizing components and applications, enabling lightweight processors that can be spawned on-demand from any machine, and potentially facilitating a faster CI/CD flow. MPI was used for parallel processing of large images across multiple nodes, and FASTAPI was used for developing an asynchronous, robust web server to handle requests. Linux Administration involved writing configuration scripts for various components and establishing services with restart policies to ensure high availability. In this project, various actions were taken to ensure scalability and fault tolerance. By using Docker and Docker-Compose, we ensured our services could be easily scaled. Along with AWS Elastic Load Balancers, we distributed incoming traffic, making the system highly available and fault-tolerant. For larger images, we utilized MPI to distribute the processing workload across several nodes, significantly improving processing times. Smaller images are handled by single lightweight workers to avoid network delays. Automation was achieved by employing systemd and cron jobs to automate the deployment and maintenance of services, ensuring they remain operational and efficient. Parallel processing was accomplished by utilizing RabbitMQ and MPI, which efficiently queued and processed tasks in parallel, reducing overall processing time. We designed a decoupled architecture to ensure that each component could function independently, improving resilience, reducing bottlenecks, and increasing fault-tolerance. This project marks a significant milestone in our journey, combining advanced technologies to create a scalable, resilient, and high-performance system. Special thanks to the incredible team and mentors, Prof. Ayman Bahaa-Eldin, and Eng. Mostafa Ashraf, who supported us throughout this project. Check out the project on GitHub: https://lnkd.in/dhYGxhDQ Watch the project demo here: https://lnkd.in/dHzffS4U Looking forward to the future developments and innovations that this project will inspire! 🌟 #DistributedSystems #AWS #Docker #RabbitMQ #MPI #FASTAPI #CloudComputing #LinuxAdministration #Scalability #FaultTolerance

Best Answers: Most Frequently Asked Questions About Docker I remember the chaos like it was yesterday: “It works on my machine!” was the battle cry as our new feature failed on the staging server. I had meticulously set everything up locally, but deployment felt like a different beast altogether—dependencies clashed, configurations went awry, and nothing behaved as expected. That’s when I discovered Docker. The moment I spun up my first Docker container, everything changed. I packaged my application with all its dependencies into a neat, portable container. Running it locally, then on the staging server, was a revelation—it just worked, no surprises. It felt like I had bottled my local environment, complete with all its quirks and settings, and could deploy it anywhere without a hitch. Docker eliminated the "works on my machine" problem by ensuring consistency across environments. No more late-night debugging or frantic calls to figure out what went wrong. With Docker, what I developed locally was precisely what ran in production. Now, I could focus on building features, confident that Docker would handle the rest. What is Docker? Docker is a tool that automates the deployment of applications in lightweight containers, allowing them to run efficiently in different environments while maintaining isolation. Example: A simple Dockerfile to run a Python application. Dockerfile Copy code # Use an official Python runtime as a parent image FROM python:3.11-slim # Set the working directory in the container WORKDIR /usr/src/app # Copy the current directory contents into the container COPY . . # Install the required packages RUN pip install --no-cache-dir -r requirements.txt # Run the application CMD ["python", "./your-script.py"] 2. How does Docker improve the development lifecycle? Docker eliminates inconsistencies across development, testing, and production environments by packaging applications and their dependencies into standardized containers. This enhances portability and efficiency throughout the development lifecycle. 3. What are the key advantages of using Docker? Docker offers several advantages, including improved application portability, simplified dependency management, efficient resource utilization, and consistent environments across different stages of development. 4. How does Docker differ from traditional virtualization? Unlike traditional virtualization, which involves creating entire virtual machines, Docker uses containers to package applications and their dependencies. This approach is more lightweight and efficient, as containers share the host system's kernel. Example: Docker vs. Virtual Machine Architecture. # Docker Container Architecture - Application 1   - Libraries/Binaries - Application 2   - Libraries/Binaries - Shared OS Kernel # Virtual Machine Architecture - Application 1   - Libraries/Binaries   - Guest OS - Application 2   - Libraries/Binaries   - Guest OS - Host OS #docker #dockercompose #dockerization

🎨 Need to customize your deployed Python code? Look no further. A request for customization is often met with a groan 😫 - rightfully so, because it can create tech debt if not done properly. With Prefect, programmatically customize your Python workflow runs using deployment parameters, which allow updating variables to be passed into your deployed Python functions setup from the UI or terminal. 🏛️ Consider one function that you've already deployed into a flow that updates a variable - and another that uses the variable but runs on a completely different schedule. Coordinate the two by using this variable as a parameter in the deployment itself. Specify a pointer to this variable in the CLI or via the UI. 📆 That's not all - need a flow to run every 12 hours and make sure it runs Monday by 9am? No problem. Specify multiple schedules for a deployment without needing to contort to one single cron string. Read the in depth guide here: https://vist.ly/riay