#containerized applications

[rajaniesh]

Mastering Azure Container Apps: From Configuration to Deployment

Thank you for following our Azure Container Apps series! We hope you're gaining valuable insights to scale and secure your applications. Stay tuned for more tips, and feel free to share your thoughts or questions. Together, let's unlock the Azure's Power.

[lucid-outsourcing-solutions]

What Is the Best Way to Containerize ColdFusion Applications Using Docker?

[simple-logic]

#TechKnowledge Have you heard of Containerization?

Swipe to discover what it is and how it can impact your digital security! 🚀

👉 Stay tuned for more simple and insightful tech tips by following us.

🌐 Learn more: https://simplelogic-it.com/

💻 Explore the latest in #technology on our Blog Page: https://simplelogic-it.com/blogs/

✨ Looking for your next career opportunity? Check out our #Careers page for exciting roles: https://simplelogic-it.com/careers/

[jcmarchi]

Karthik Ranganathan, Co-Founder and Co-CEO of Yugabyte – Interview Series

New Post has been published on https://thedigitalinsider.com/karthik-ranganathan-co-founder-and-co-ceo-of-yugabyte-interview-series/

Karthik Ranganathan, Co-Founder and Co-CEO of Yugabyte – Interview Series

Karthik Ranganathan is co-founder and co-CEO of Yugabyte, the company behind YugabyteDB, the open-source, high-performance distributed PostgreSQL database. Karthik is a seasoned data expert and former Facebook engineer who founded Yugabyte alongside two of his Facebook colleagues to revolutionize distributed databases.

What inspired you to co-found Yugabyte, and what gaps in the market did you see that led you to create YugabyteDB?

My co-founders, Kannan Muthukkaruppan, Mikhail Bautin, and I, founded Yugabyte in 2016. As former engineers at Meta (then called Facebook), we helped build popular databases including Apache Cassandra, HBase, and RocksDB – as well as running some of these databases as managed services for internal workloads.

We created YugabyteDB because we saw a gap in the market for cloud-native transactional databases for business-critical applications. We built YugabyteDB to cater to the needs of organizations transitioning from on-premises to cloud-native operations and combined the strengths of non-relational databases with the scalability and resilience of cloud-native architectures. While building Cassandra and HBase at Facebook (which was instrumental in addressing Facebook’s significant scaling needs), we saw the rise of microservices, containerization, high availability, geographic distribution, and Application Programming Interfaces (API). We also recognized the impact that open-source technologies have in advancing the industry.

People often think of the transactional database market as crowded. While this has traditionally been true, today Postgres has become the default API for cloud-native transactional databases. Increasingly, cloud-native databases are choosing to support the Postgres protocol, which has been ingrained into the fabric of YugabyteDB, making it the most Postgres-compatible database on the market. YugabyteDB retains the power and familiarity of PostgreSQL while evolving it to an enterprise-grade distributed database suitable for modern cloud-native applications. YugabyteDB allows enterprises to efficiently build and scale systems using familiar SQL models.

How did your experiences at Facebook influence your vision for the company?

In 2007, I was considering whether to join a small but growing company–Facebook. At the time, the site had about 30 to 40 million users. I thought it might double in size, but I couldn’t have been more wrong! During my over five years at Facebook, the user base grew to 2 billion. What attracted me to the company was its culture of innovation and boldness, encouraging people to “fail fast” to catalyze innovation.

Facebook grew so large that the technical and intellectual challenges I craved were no longer present. For many years I had aspired to start my own company and tackle problems facing the common user–this led me to co-create Yugabyte.

Our mission is to simplify cloud-native applications, focusing on three essential features crucial for modern development:

First, applications must be continuously available, ensuring uptime regardless of backups or failures, especially when running on commodity hardware in the cloud.

Second, the ability to scale on demand is crucial, allowing developers to build and release quickly without the delay of ordering hardware.

Third, with numerous data centers now easily accessible, replicating data across regions becomes vital for reliability and performance.

These three elements empower developers by providing the agility and freedom they need to innovate, without being constrained by infrastructure limitations.

Could you share the journey from Yugabyte’s inception in 2016 to its current status as a leader in distributed SQL databases? What were some key milestones?

At Facebook, I often talked with developers who needed specific features, like secondary indexes on SQL databases or occasional multi-node transactions. Unfortunately, the answer was usually “no,” because existing systems weren’t designed for those requirements.

Today, we are experiencing a shift towards cloud-native transactional applications that need to address scale and availability. Traditional databases simply can’t meet these needs. Modern businesses require relational databases that operate in the cloud and offer the three essential features: high availability, scalability, and geographic distribution, while still supporting SQL capabilities. These are the pillars on which we built YugabyteDB and the database challenges we’re focused on solving.

In February 2016, the founders began developing YugabyteDB, a global-scale distributed SQL database designed for cloud-native transactional applications. In July 2019, we made an unprecedented announcement and released our previously commercial features as open source. This reaffirmed our commitment to open-source principles and officially launched YugabyteDB as a fully open-source relational database management system (RDBMS) under an Apache 2.0 license.

The latest version of YugabyteDB (unveiled in September) features enhanced Postgres compatibility. It includes an Adaptive Cost-Based Optimizer (CBO) that optimizes query plans for large-scale, multi-region applications, and Smart Data Distribution that automatically determines whether to store tables together for lower latency, or to shard and distribute data for greater scalability. These enhancements allow developers to run their PostgreSQL applications on YugabyteDB efficiently and scale without the need for trade-offs or complex migrations.

YugabyteDB is known for its compatibility with PostgreSQL and its Cassandra-inspired API. How does this multi-API approach benefit developers and enterprises?

YugabyteDB’s multi-API approach benefits developers and enterprises by combining the strengths of a high-performance SQL database with the flexibility needed for global, internet-scale applications.

It supports scale-out RDBMS and high-volume Online Transaction Processing (OLTP) workloads, while maintaining low query latency and exceptional resilience. Compatibility with PostgreSQL allows for seamless lift-and-shift modernization of existing Postgres applications, requiring minimal changes.

In the latest version of the distributed database platform, released in September 2024, features like the Adaptive CBO and Smart Data Distribution enhance performance by optimizing query plans and automatically managing data placement. This allows developers to achieve low latency and high scalability without compromise, making YugabyteDB ideal for rapidly growing, cloud-native applications that require reliable data management.

AI is increasingly being integrated into database systems. How is Yugabyte leveraging AI to enhance the performance, scalability, and security of its SQL systems?

We are leveraging AI to enhance our distributed SQL database by addressing performance and migration challenges. Our upcoming Performance Copilot, an enhancement to our Performance Advisor, will simplify troubleshooting by analyzing query patterns, detecting anomalies, and providing real-time recommendations to troubleshoot database performance issues.

We are also integrating AI into YugabyteDB Voyager, our database migration tool that simplifies migrations from PostgreSQL, MySQL, Oracle, and other cloud databases to YugabyteDB. We aim to streamline transitions from legacy systems by automating schema conversion, SQL translation, and data transformation, with proactive compatibility checks. These innovations focus on making YugabyteDB smarter, more efficient, and easier for modern, distributed applications to use.

What are the key advantages of using an open-source SQL system like YugabyteDB in cloud-native applications compared to traditional proprietary databases?

Transparency, flexibility, and robust community support are key advantages when using an open-source SQL system like YugabyteDB in cloud-native applications. When we launched YugabyteDB, we recognized the skepticism surrounding open-source models. We engaged with users, who expressed a strong preference for a fully open database to trust with their critical data.

We initially ran on an open-core model, but rapidly realized it needed to be a completely open solution. Developers increasingly turn to PostgreSQL as a logical Oracle alternative, but PostgreSQL was not built for dynamic cloud platforms. YugabyteDB fills this gap by supporting PostgreSQL’s feature depth for modern cloud infrastructures. By being 100% open source, we remove roadblocks to adoption.

This makes us very attractive to developers building business-critical applications and to operations engineers running them on cloud-native platforms. Our focus is on creating a database that is not only open, but also easy to use and compatible with PostgreSQL, which remains a developer favorite due to its mature feature set and powerful extensions.

The demand for scalable and adaptable SQL solutions is growing. What trends are you observing in the enterprise database market, and how is Yugabyte positioned to meet these demands?

Larger scale in enterprise databases often leads to increased failure rates, especially as organizations deal with expanded footprints and higher data volumes. Key trends shaping the database landscape include the adoption of DBaaS, and a shift back from public cloud to private cloud environments. Additionally, the integration of generative AI brings opportunities and challenges, requiring automation and performance optimization to manage the growing data load.

Organizations are increasingly turning to DBaaS to streamline operations, despite initial concerns about control and security. This approach improves efficiency across various infrastructures, while the focus on private cloud solutions helps businesses reduce costs and enhance scalability for their workloads.

YugabyteDB addresses these evolving demands by combining the strengths of relational databases with the scalability of cloud-native architectures. Features like Smart Data Distribution and an Adaptive CBO, enhance performance and support a large number of database objects. This makes it a competitive choice for running a wide range of applications.

Furthermore, YugabyteDB allows enterprises to migrate their PostgreSQL applications while maintaining similar performance levels, crucial for modern workloads. Our commitment to open-source development encourages community involvement and provides flexibility for customers who want to avoid vendor lock-in.

With the rise of edge computing and IoT, how does YugabyteDB address the challenges posed by these technologies, particularly regarding data distribution and latency?

YugabyteDB’s distributed SQL architecture is designed to meet the challenges posed by the rise of edge computing and IoT by providing a scalable and resilient data layer that can operate seamlessly in both cloud and edge contexts. Its ability to automatically shard and replicate data ensures efficient distribution, enabling quick access and real-time processing. This minimizes latency, allowing applications to respond swiftly to user interactions and data changes.

By offering the flexibility to adapt configurations based on specific application requirements, YugabyteDB ensures that enterprises can effectively manage their data needs as they evolve in an increasingly decentralized landscape.

As Co-CEO, how do you balance the dual roles of leading technological innovation and managing company growth?

Our company aims to simplify cloud-native applications, compelling me to stay on top of technology trends, such as generative AI and context switches. Following innovation demands curiosity, a desire to make an impact, and a commitment to continuous learning.

Balancing technological innovation and company growth is fundamentally about scaling–whether it’s scaling systems or scaling impact. In distributed databases, we focus on building technologies that scale performance, handle massive workloads, and ensure high availability across a global infrastructure. Similarly, scaling Yugabyte means growing our customer base, enhancing community engagement, and expanding our ecosystem–while maintaining operational excellence.

All this requires a disciplined approach to performance and efficiency.

Technically, we optimize query execution, reduce latency, and improve system throughput; organizationally, we streamline processes, scale teams, and enhance cross-functional collaboration. In both cases, success comes from empowering teams with the right tools, insights, and processes to make smart, data-driven decisions.

How do you see the role of distributed SQL databases evolving in the next 5-10 years, particularly in the context of AI and machine learning?

In the next few years, distributed SQL databases will evolve to handle complex data analysis, enabling users to make predictions and detect anomalies with minimal technical expertise. There is an immense amount of database specialization in the context of AI and machine learning, but that is not sustainable. Databases will need to evolve to meet the demands of AI. This is why we’re iterating and enhancing capabilities on top of pgvector, ensuring developers can use Yugabyte for their AI database needs.

Additionally, we can expect an ongoing commitment to open source in AI development. Five years ago, we made YugabyteDB fully open source under the Apache 2.0 license, reinforcing our dedication to an open-source framework and proactively building our open-source community.

Thank you for all of your detailed responses, readers who wish to learn more should visit YugabyteDB.

[agiledock]

Harnessing Containerization in Web Development: A Path to Scalability

Explore the transformative impact of containerization in web development. This article delves into the benefits of containerization, microservices architecture, and how Docker for web apps facilitates scalable and efficient applications in today’s cloud-native environment.

[robomad]

Using Docker with Node.js Applications

Learn how to use Docker with Node.js applications. This guide covers setting up Docker, creating Dockerfiles, managing dependencies, and using Docker Compose.

Introduction Docker has revolutionized the way we build, ship, and run applications. By containerizing your Node.js applications, you can ensure consistency across different environments, simplify dependencies management, and streamline deployment processes. This guide will walk you through the essentials of using Docker with Node.js applications, from setting up a Dockerfile to running your…

[virtualizationhowto]

Best Kubernetes Management Tools in 2023

Best Kubernetes Management Tools in 2023 #homelab #vmwarecommunities #Kubernetesmanagementtools2023 #bestKubernetescommandlinetools #managingKubernetesclusters #Kubernetesdashboardinterfaces #kubernetesmanagementtools #Kubernetesdashboard

Kubernetes is everywhere these days. It is used in the enterprise and even in many home labs. It’s a skill that’s sought after, especially with today’s push for app modernization. Many tools help you manage things in Kubernetes, like clusters, pods, services, and apps. Here’s my list of the best Kubernetes management tools in 2023. Table of contentsWhat is Kubernetes?Understanding Kubernetes and…

[andmaybegayer]

Any recommendations/cautions about using Alpine Linux on the desktop? It's always intrigued me and you're the only person I've seen post about it

Alpine is pretty good for desktop, very stable, good security practice, professional development philosophy, broad package availability. You will run into some very obvious pitfalls, although they can mostly be obviated by using some modern applications.

The Alpine wiki is a little sparse and at times can be weirdly focussed, like spending a lot of the installation page talking about the very specific usecase of a diskless install. Nonetheless, it's quite good and should be your first port of call. A lot of the things I'm mentioning here are well covered in the article on Daily Driving for Desktop use. I'm basically just editorializing here.

The installation procedure is command-line only, but pretty straightforward, you run setup-alpine and follow the prompts, assuming you want a basic system. If you need special disk partitioning, you'll usually have to do it yourself. There's a whole whackload of helpers to get you set up, like setup-desktop which will help you install any of 'gnome', 'plasma', 'xfce', 'mate', 'sway', or 'lxqt'. Most of these are called by setup-alpine for you, but not the desktop one. You can call it at any time though.

Most obviously, musl libc, no glibc. Packaged software will work fine. There's a compatibility shim called gcompat that will usually work, but might fall apart on more complicated software expecting glibc, for example I've had no luck running glibc AppImages. For more complex software, Flatpaks are a good option, e.g. Steam runs great on Alpine as a Flatpak, I run the Homestuck Companion Flatpak. Your last ditch is containerization and chroots, which are fortunately really easy to handle, just install podman and Distrobox and you can run anything that won't run on Alpine inside a Fedora or Debian or Whatever container seamlessly with your desktop.

Less obviously: no systemd. Systemd underpins some really common features of modern Linux and not having it around means you have to use a few different tools that are anywhere from comparable to a little worse for some tasks. Packaged applications will work smoothly, just learn the OpenRC invocations, Alpine has a really great wiki. For writing your own services, it's a lot more limited than SystemD, you're not going to have full access to like, udev functionality, instead you get the good but kind of weird eudev system.

If you're mainly installing things from the repos you'll barely notice the difference, other than that every package is split up into three, <package>, <package>-docs, and <package>-dev. This is a container-y thing, to allow Alpine container images to install the smallest possible packageset. If you need man pages you'll have to install them specifically.

Alpine has a very solid main repo, and a community repo that's plenty good, and worth enabling on any desktop system. It'll generally be automatically enabled when you set up a desktop anyway, but just a notice if you're going manual. You can run Stable alpine, which updates every six months, or if you want you can run Edge, which is a rolling release of packages as they get added. Lots of very up-to-date software, and pretty stable as these go. You can go from Stable->Edge pretty easily, going back not so much.

There's also the Testing repo, only available on Edge, which I don't really recommend, especially since apkbuild files are so easy to run if you just need one thing that has most of its dependencies met.

Package management is with APK, which is fast and easy to work with. The wiki page will cover you.

Side note: if you want something more batteries-included, you could look at Postmarket, an Alpine derivative mainly focussed on running on smartphones but that is a pretty capable desktop OS, and which has a fairly friendly setup process. I run this on an ARM Chromebook and it's solid. Installation requires some reading between the lines because it's intended for the weird world of phones, so you'll probably want to follow the PMBootstrap route.

[marq-lynch]

If you think I've gotten less insane about StrickPage, no, I've just hit a minor depressive episode and been heads down lately but I still am working on The Project in the background and it continues to grow out of control I am just being Good and biting my tongue and staring at the wall when I get new info. But yeah the string wall is getting insane too I have no idea how I'll even make this a video essay series I might need a more experienced video editor's help legitimately. I have developed a deep and abiding loathing for the Genius phone App and a gnashing frustration at the fact one cannot forensically track changes to a Spotify playlist.

Incomplete Primary Sources now cover:

Five AEW Shows and [x] PPVs, a span of 350+ BTE episodes, footage from at least 6 other promotions, five plus albums and singles, Swerve's socials that he cleaned and/or recreated when he joined AEW, NOT Hangman's socials except Bsky skeets cuz I was slow, socials of others involved, at least three books, and miscellaneous promos, interviews, podcasts, documentaries, posts by fans about socials as secondary sources, playlists, and attempted CSI reconstructions of Spotify Playlist timelines.

I have fucked around with basic python for the first time to download containerized application 3rd party amazing open source software and had a meltdown when it then updated six weeks later and broke. I have learned so much about rap and r&b history my Spotify algorithm is permanently changed for the better, the same way getting in wrestling made my news apps send me push notifications about the local football team. I have paid for subscriptions for early 2010s indie wrestling archive footage and 'download everything from a public Instagram immediately with details' tools that make me both so happy and a little uncomfortable still.

Useful pre-existing knowledge so far: knowledge of Opera, Victorian Floriography, Horror Movies, some birding knowledge, your basic art history and queer history, and a strong layman's knowledge of Dolly Parton lore. Also just basic history, lit, and film nerd shit I think.

So yeah I am no longer raving about Gold in them hills in the streets, but I am collecting things in my cave of wonders like a little goblin and realizing I have dwelled too deep already and there's no way out but through the mountains and who knows how much I can carry out with me.

[utopicwork]

Wasm will change the game of client side/offline web apps, I find the server side applications to be not that exciting because there's tons of ways to containerize applications already, the real value in my opinion is putting the browser back into people's hands as an interface but this time with more power/utility

[chiragqlanceblogs]

How Python Powers Scalable and Cost-Effective Cloud Solutions

Explore the role of Python in developing scalable and cost-effective cloud solutions. This guide covers Python's advantages in cloud computing, addresses potential challenges, and highlights real-world applications, providing insights into leveraging Python for efficient cloud development.

Introduction

In today's rapidly evolving digital landscape, businesses are increasingly leveraging cloud computing to enhance scalability, optimize costs, and drive innovation. Among the myriad of programming languages available, Python has emerged as a preferred choice for developing robust cloud solutions. Its simplicity, versatility, and extensive library support make it an ideal candidate for cloud-based applications.

In this comprehensive guide, we will delve into how Python empowers scalable and cost-effective cloud solutions, explore its advantages, address potential challenges, and highlight real-world applications.

Why Python is the Preferred Choice for Cloud Computing?

Python's popularity in cloud computing is driven by several factors, making it the preferred language for developing and managing cloud solutions. Here are some key reasons why Python stands out:

Simplicity and Readability: Python's clean and straightforward syntax allows developers to write and maintain code efficiently, reducing development time and costs.

Extensive Library Support: Python offers a rich set of libraries and frameworks like Django, Flask, and FastAPI for building cloud applications.

Seamless Integration with Cloud Services: Python is well-supported across major cloud platforms like AWS, Azure, and Google Cloud.

Automation and DevOps Friendly: Python supports infrastructure automation with tools like Ansible, Terraform, and Boto3.

Strong Community and Enterprise Adoption: Python has a massive global community that continuously improves and innovates cloud-related solutions.

How Python Enables Scalable Cloud Solutions?

Scalability is a critical factor in cloud computing, and Python provides multiple ways to achieve it:

1. Automation of Cloud Infrastructure

Python's compatibility with cloud service provider SDKs, such as AWS Boto3, Azure SDK for Python, and Google Cloud Client Library, enables developers to automate the provisioning and management of cloud resources efficiently.

2. Containerization and Orchestration

Python integrates seamlessly with Docker and Kubernetes, enabling businesses to deploy scalable containerized applications efficiently.

3. Cloud-Native Development

Frameworks like Flask, Django, and FastAPI support microservices architecture, allowing businesses to develop lightweight, scalable cloud applications.

4. Serverless Computing

Python's support for serverless platforms, including AWS Lambda, Azure Functions, and Google Cloud Functions, allows developers to build applications that automatically scale in response to demand, optimizing resource utilization and cost.

5. AI and Big Data Scalability

Python’s dominance in AI and data science makes it an ideal choice for cloud-based AI/ML services like AWS SageMaker, Google AI, and Azure Machine Learning.

Looking for expert Python developers to build scalable cloud solutions? Hire Python Developers now!

Advantages of Using Python for Cloud Computing

Cost Efficiency: Python’s compatibility with serverless computing and auto-scaling strategies minimizes cloud costs.

Faster Development: Python’s simplicity accelerates cloud application development, reducing time-to-market.

Cross-Platform Compatibility: Python runs seamlessly across different cloud platforms.

Security and Reliability: Python-based security tools help in encryption, authentication, and cloud monitoring.

Strong Community Support: Python developers worldwide contribute to continuous improvements, making it future-proof.

Challenges and Considerations

While Python offers many benefits, there are some challenges to consider:

Performance Limitations: Python is an interpreted language, which may not be as fast as compiled languages like Java or C++.

Memory Consumption: Python applications might require optimization to handle large-scale cloud workloads efficiently.

Learning Curve for Beginners: Though Python is simple, mastering cloud-specific frameworks requires time and expertise.

Python Libraries and Tools for Cloud Computing

Python’s ecosystem includes powerful libraries and tools tailored for cloud computing, such as:

Boto3: AWS SDK for Python, used for cloud automation.

Google Cloud Client Library: Helps interact with Google Cloud services.

Azure SDK for Python: Enables seamless integration with Microsoft Azure.

Apache Libcloud: Provides a unified interface for multiple cloud providers.

PyCaret: Simplifies machine learning deployment in cloud environments.

Real-World Applications of Python in Cloud Computing

1. Netflix - Scalable Streaming with Python

Netflix extensively uses Python for automation, data analysis, and managing cloud infrastructure, enabling seamless content delivery to millions of users.

2. Spotify - Cloud-Based Music Streaming

Spotify leverages Python for big data processing, recommendation algorithms, and cloud automation, ensuring high availability and scalability.

3. Reddit - Handling Massive Traffic

Reddit uses Python and AWS cloud solutions to manage heavy traffic while optimizing server costs efficiently.

Future of Python in Cloud Computing

The future of Python in cloud computing looks promising with emerging trends such as:

AI-Driven Cloud Automation: Python-powered AI and machine learning will drive intelligent cloud automation.

Edge Computing: Python will play a crucial role in processing data at the edge for IoT and real-time applications.

Hybrid and Multi-Cloud Strategies: Python’s flexibility will enable seamless integration across multiple cloud platforms.

Increased Adoption of Serverless Computing: More enterprises will adopt Python for cost-effective serverless applications.

Conclusion

Python's simplicity, versatility, and robust ecosystem make it a powerful tool for developing scalable and cost-effective cloud solutions. By leveraging Python's capabilities, businesses can enhance their cloud applications' performance, flexibility, and efficiency.

Ready to harness the power of Python for your cloud solutions? Explore our Python Development Services to discover how we can assist you in building scalable and efficient cloud applications.

FAQs

1. Why is Python used in cloud computing?

Python is widely used in cloud computing due to its simplicity, extensive libraries, and seamless integration with cloud platforms like AWS, Google Cloud, and Azure.

2. Is Python good for serverless computing?

Yes! Python works efficiently in serverless environments like AWS Lambda, Azure Functions, and Google Cloud Functions, making it an ideal choice for cost-effective, auto-scaling applications.

3. Which companies use Python for cloud solutions?

Major companies like Netflix, Spotify, Dropbox, and Reddit use Python for cloud automation, AI, and scalable infrastructure management.

4. How does Python help with cloud security?

Python offers robust security libraries like PyCryptodome and OpenSSL, enabling encryption, authentication, and cloud monitoring for secure cloud applications.

5. Can Python handle big data in the cloud?

Yes! Python supports big data processing with tools like Apache Spark, Pandas, and NumPy, making it suitable for data-driven cloud applications.

[rajaniesh]

Skyrocket Your Efficiency: Dive into Azure Cloud-Native solutions

Join our blog series on Azure Container Apps and unlock unstoppable innovation! Discover foundational concepts, advanced deployment strategies, microservices, serverless computing, best practices, and real-world examples. Transform your operations!!

[bjsmall]

22.02.25

A quick look at the free, open source Openshot Video Editor, running in Linux Mint Cinnamon 22.1 on Distrosea!

On Mint, Openshot is installed via the Software Manager as a .deb file.

Here are some of the packages (dependencies) that are installed alongside it. The version featured in the manager (Openshot 3.1.1) is a few versions behind the web version (3.3.0) for stability reasons.

OpenShot was released in August 2008. It is built using Python, C++ (libopenshot) and the Qt5 framework. It is available for Linux, Chrome OS, Windows and Mac OS operating systems.

https://www.openshot.org/

It can handle most modern and classic video formats and can export videos in up to 8K resolution. It comes with a customisable interface, built-in tutorial, various transitions, effects, 3D text and animation effects with Blender Animation support and emoji emblems that can be used in video shots through the time line.

Openshot comes with a default dark theme and a light theme.

https://en.wikipedia.org/wiki/OpenShot

An interesting look at the OpenShot User interface. The window size of the VM is shown in the 'Display Settings' window. The actual display resolution is 2560x1440.

Online video tutorials can be watched here:

https://www.youtube.com/playlist?list=PLymupH2aoNQNezYzv2lhSwvoyZgLp1Q0T

However I still prefer the simplicity of Clipchamp, as it includes ready made templates, a clean UI and is a browser based web-app that can run on any operating system.

https://clipchamp.com/en/

Other popular open-source video editing applications include:

Kdenlive (released 2002)

https://kdenlive.org/en/

Shortcut (released 2011)

https://www.shotcut.org/

Flowblade Movie Editor (released 2009)

https://jliljebl.github.io/flowblade/

Blender Animation Program (released 1994)

https://www.blender.org/

All of the video editors mentioned here have their pros and cons and many can be installed as a .deb (System Package) or .flatpak (Containerized App) on Linux Mint.

[orbitwebtech]

Are you struggling with deployment issues for your Node.js applications? 😫 Tired of hearing "but it works on my machine"? 🤯 Docker is the game-changer you need!

With Docker, you can containerize your Node.js app, ensuring a smooth, consistent, and scalable deployment across all environments. 🌍

🔥 Why Use Docker for Node.js Deployment? ✅ Eliminates Environment Issues – Package dependencies, runtime, and configurations into a single container for a "works everywhere" experience! ✅ Faster & Seamless Deployment – Reduce deployment time with pre-configured images and lightweight containers! ✅ Improved Scalability – Easily scale your app using Docker Swarm or Kubernetes! ✅ CI/CD Integration – Automate and streamline your deployment pipeline with Docker + Jenkins/GitHub Actions! ✅ Better Resource Utilization – Docker uses less memory and boots faster than traditional virtual machines!

💡 Whether you're a DevOps engineer, developer, or tech enthusiast, understanding Docker for Node.js deployment is a must!

📌 Want to master seamless deployment? Read the full article now!

[jcmarchi]

Platform Engineering: Streamlining Modern Software Development

New Post has been published on https://thedigitalinsider.com/platform-engineering-streamlining-modern-software-development/

Platform Engineering: Streamlining Modern Software Development

As we accelerate ahead of Industry 4.0, digital transformation reshapes businesses at an unprecedented level.  Today, organizations face high pressures to deliver software faster, more reliably, and at scale.

The growing complexities of the cloud environment and heightened demand for frictionless customer experiences have further complicated software development. Since delivering a seamless customer experience is an organization’s top priority, continuous software development runs parallel with other operations.

Platform engineering has emerged to address these challenges. It is based upon integrating product names as an internal developer’s platform designed to streamline the software development process.

According to a research by Gartner, “45% of large software engineering organizations were already utilizing platform engineering platforms in 2022, and the number is expected to rise by 80% by 2026.”.

This article will explain platform engineering and its benefits and see how it boosts the entire software development cycle.

What is Platform Engineering?

With ever-increasing functionalities, cloud environments are becoming more complex every day. Developing new tools and software isn’t easy now. Sometimes, software development-related complexities, the underlying infrastructure that must be managed, and routine tasks become hard to tackle.

Platform engineering focuses on designing, developing, and optimizing Internal Developers Platforms (IDPs).

IDPs work as an added layer and bridge the gap between developers and underlying infrastructure. Adopting an IDP enables workflow standardizations, self-services in software development, and improved observability in development.

With all these workflow enhancements, developers can work in more automated environments. By enabling automation at every level, the internal data platforms streamline the software development lifecycle (SDCL) while adhering to the governance and compliance standards.

Core Concepts of Platform Engineering

As cloud computing, microservices, and containerization grew, organizations opted for more advanced platforms to manage their complexities. An engineering platform in software development creates a cohesive and efficient development environment that enhances productivity while maintaining operational stability.

Here are some of the core concepts of platform engineering:

1. Internal Developer Platform (IDP)

Internal Developer Platforms (IDPs) are designed to help organizations optimize their development processes. As development processes become more iterative, cloud complexities become an added burden.

Source

IDPs act as added layers in the development process, simplifying operations and enabling teams to leverage existing development technologies. It is an ecosystem that empowers developers by providing tools and automation and autonomously managing the end-to-end lifecycle, from development to deployment.

Some of the most demanded IDPs are:

Qovery

Platform.sh

Backstage by Spotify

Humanitec

Coherence

However, if not implemented strategically, IDPs can introduce further complexities downstream.

2. Self-Service

One of platform engineering’s most powerful features is its capability to empower developers through self-service. This means developers can work independently, provision, manage, and deploy applications without relying on operations teams. It empowers the workflow, enabling developers to iterate and deploy efficiently.

3. Workflow Automation

Automation is another reason to adopt an engineering platform.  It automates routine tasks and reduces human error while ensuring scalability and consistency across the development lifecycle.

4. Standardization

Engineering platforms promote end-to-end standardization of best practices across the development workflow. They usually have built-in quality standards to meet compliance requirements. So, both organizations and development teams automatically follow compliance best practices.

5. Security & Governance

As security and governance are core concerns in any SDLC, integrating a robust engineering platform ensures robust governance to minimize vulnerabilities and risks.

6. Infrastructure as Code (IaC)

Platform engineering enables development teams to provision and manage infrastructure resources in repeatable and automated ways. It also promotes version control for easier management and collaboration and faster time-to-market.

At its core, it manages the development process more efficiently by reducing development complexities and ensuring compliance and quality.

Why is Platform Engineering Important to Adapt?

Platform engineering has gained importance due to the growing complexity of software and cloud-based development environments. Managing multiple development environments, multifaceted cloud environments, and diverse technology loads obstructs developers.

To avoid this, internal development platforms streamline the process by creating scalable, reusable platforms to automate tasks and simplify processes.

An IDP allows developers to stay ahead and be more productive at high-value tasks rather than tackling underlying infrastructure. Moreover, platform engineering enables security measures in the development process, diminishing risks and ensuring compliance.

Benefits of Platform Engineering

Internal development platforms offer several critical benefits that boost the overall software development process.

Accelerates Development Process: The cross-functional and automated collaborative environment leads to faster time-to-market and rationalized development costs.

Improves Developer Experience: Platform engineering offers self-service capabilities, enabling developers to be efficient and creative and focus on innovation instead of infrastructural complexities.

Enhances Scalability: Engineering platforms empower organizations to scale their applications and infrastructure to meet growing demands.

Establishes Governance: Platform engineering promotes governance, which is critical for all industries. It ensures that all activities are compliant and aligned with best practices.

Is Platform Engineering the Future of Software Development?

As businesses transform digitally, platform engineering provides a unified infrastructure to empower the build, test, and deployment processes. Integrating artificial intelligence (AI) will lead to more efficient internal platform services in the future.

Moreover, early adoption will provide a competitive advantage for seamless software development workflows.

For more resources on software development, cloud computing, and AI, visit Unite.ai.

[cyberanalyst023]

Exploring the Azure Technology Stack: A Solution Architect’s Journey

Kavin

As a solution architect, my career revolves around solving complex problems and designing systems that are scalable, secure, and efficient. The rise of cloud computing has transformed the way we think about technology, and Microsoft Azure has been at the forefront of this evolution. With its diverse and powerful technology stack, Azure offers endless possibilities for businesses and developers alike. My journey with Azure began with Microsoft Azure training online, which not only deepened my understanding of cloud concepts but also helped me unlock the potential of Azure’s ecosystem.

In this blog, I will share my experience working with a specific Azure technology stack that has proven to be transformative in various projects. This stack primarily focuses on serverless computing, container orchestration, DevOps integration, and globally distributed data management. Let’s dive into how these components come together to create robust solutions for modern business challenges.

Understanding the Azure Ecosystem

Azure’s ecosystem is vast, encompassing services that cater to infrastructure, application development, analytics, machine learning, and more. For this blog, I will focus on a specific stack that includes:

Azure Functions for serverless computing.

Azure Kubernetes Service (AKS) for container orchestration.

Azure DevOps for streamlined development and deployment.

Azure Cosmos DB for globally distributed, scalable data storage.

Each of these services has unique strengths, and when used together, they form a powerful foundation for building modern, cloud-native applications.

1. Azure Functions: Embracing Serverless Architecture

Serverless computing has redefined how we build and deploy applications. With Azure Functions, developers can focus on writing code without worrying about managing infrastructure. Azure Functions supports multiple programming languages and offers seamless integration with other Azure services.

Real-World Application

In one of my projects, we needed to process real-time data from IoT devices deployed across multiple locations. Azure Functions was the perfect choice for this task. By integrating Azure Functions with Azure Event Hubs, we were able to create an event-driven architecture that processed millions of events daily. The serverless nature of Azure Functions allowed us to scale dynamically based on workload, ensuring cost-efficiency and high performance.

Key Benefits:

Auto-scaling: Automatically adjusts to handle workload variations.

Cost-effective: Pay only for the resources consumed during function execution.

Integration-ready: Easily connects with services like Logic Apps, Event Grid, and API Management.

2. Azure Kubernetes Service (AKS): The Power of Containers

Containers have become the backbone of modern application development, and Azure Kubernetes Service (AKS) simplifies container orchestration. AKS provides a managed Kubernetes environment, making it easier to deploy, manage, and scale containerized applications.

Real-World Application

In a project for a healthcare client, we built a microservices architecture using AKS. Each service—such as patient records, appointment scheduling, and billing—was containerized and deployed on AKS. This approach provided several advantages:

Isolation: Each service operated independently, improving fault tolerance.

Scalability: AKS scaled specific services based on demand, optimizing resource usage.

Observability: Using Azure Monitor, we gained deep insights into application performance and quickly resolved issues.

The integration of AKS with Azure DevOps further streamlined our CI/CD pipelines, enabling rapid deployment and updates without downtime.

Key Benefits:

Managed Kubernetes: Reduces operational overhead with automated updates and patching.

Multi-region support: Enables global application deployments.

Built-in security: Integrates with Azure Active Directory and offers role-based access control (RBAC).

3. Azure DevOps: Streamlining Development Workflows

Azure DevOps is an all-in-one platform for managing development workflows, from planning to deployment. It includes tools like Azure Repos, Azure Pipelines, and Azure Artifacts, which support collaboration and automation.

Real-World Application

For an e-commerce client, we used Azure DevOps to establish an efficient CI/CD pipeline. The project involved multiple teams working on front-end, back-end, and database components. Azure DevOps provided:

Version control: Using Azure Repos for centralized code management.

Automated pipelines: Azure Pipelines for building, testing, and deploying code.

Artifact management: Storing dependencies in Azure Artifacts for seamless integration.

The result? Deployment cycles that previously took weeks were reduced to just a few hours, enabling faster time-to-market and improved customer satisfaction.

Key Benefits:

End-to-end integration: Unifies tools for seamless development and deployment.

Scalability: Supports projects of all sizes, from startups to enterprises.

Collaboration: Facilitates team communication with built-in dashboards and tracking.

4. Azure Cosmos DB: Global Data at Scale

Azure Cosmos DB is a globally distributed, multi-model database service designed for mission-critical applications. It guarantees low latency, high availability, and scalability, making it ideal for applications requiring real-time data access across multiple regions.

Real-World Application

In a project for a financial services company, we used Azure Cosmos DB to manage transaction data across multiple continents. The database’s multi-region replication ensure data consistency and availability, even during regional outages. Additionally, Cosmos DB’s support for multiple APIs (SQL, MongoDB, Cassandra, etc.) allowed us to integrate seamlessly with existing systems.

Key Benefits:

Global distribution: Data is replicated across regions with minimal latency.

Flexibility: Supports various data models, including key-value, document, and graph.

SLAs: Offers industry-leading SLAs for availability, throughput, and latency.

Building a Cohesive Solution

Combining these Azure services creates a technology stack that is flexible, scalable, and efficient. Here’s how they work together in a hypothetical solution:

Data Ingestion: IoT devices send data to Azure Event Hubs.

Processing: Azure Functions processes the data in real-time.

Storage: Processed data is stored in Azure Cosmos DB for global access.

Application Logic: Containerized microservices run on AKS, providing APIs for accessing and manipulating data.

Deployment: Azure DevOps manages the CI/CD pipeline, ensuring seamless updates to the application.

This architecture demonstrates how Azure’s technology stack can address modern business challenges while maintaining high performance and reliability.

Final Thoughts

My journey with Azure has been both rewarding and transformative. The training I received at ACTE Institute provided me with a strong foundation to explore Azure’s capabilities and apply them effectively in real-world scenarios. For those new to cloud computing, I recommend starting with a solid training program that offers hands-on experience and practical insights.

As the demand for cloud professionals continues to grow, specializing in Azure’s technology stack can open doors to exciting opportunities. If you’re based in Hyderabad or prefer online learning, consider enrolling in Microsoft Azure training in Hyderabad to kickstart your journey.

Azure’s ecosystem is continuously evolving, offering new tools and features to address emerging challenges. By staying committed to learning and experimenting, we can harness the full potential of this powerful platform and drive innovation in every project we undertake.