Making use of containerization in a development & production environment has been more and more popular in the last two decades. But before diving into the technology, let’s remember life before it.
“You’re going to test using Python 2.7, and then it’s going to run on Python 3 in production and something weird will happen. Or you’ll rely on the behavior of a certain version of an SSL library and another one will be installed. You’ll run your tests on Debian and production is on Red Hat and all sorts of weird things happen.” — Solomon Hykes, founder of Docker
Within the software engineering world, an all too common mistake occurs when certain devs use different versioning of dependencies in their environment. When their code is pushed into the organization branch, other engineers working off that branch often find themselves with versioning errors the moment they pull. When we use containerization, we house all of the dependencies for our application in one location — so that we no longer have issues with the development team using different OS and installation versions. Containerization goes further than this, by also ensuring that the network topologies, security policies and the storage for that application is centralized and isolated from any one developer.
In this lesson you will learn:
#docker #containerization #virtualization
Most of the business applications today are enabled by the cloud with a lot of them residing as containerized workloads. Digital transformation is being powered by concepts encompassing containers, Kubernetes, and microservices and has become indispensable parts of how applications are developed & deployed.
If we take containers particularly in consideration, they are modernizing applications like never before and helping in creating scalable & agile cloud-native applications. Even though companies are adopting containers at a fast pace, operating them in production will need a steep learning curve.
As per Gartner, by 2022 a whopping 75% of the organizations will be using containerized applications in production, up from 30% in 2019.
But this is not the entire story. Companies still need to mature in their adoption of container and one of the key areas of concern is security, especially that of the data and that will be the focal point of this blog.
Container technologies like Docker and orchestration frameworks like Kubernetes administer a standardized way to package applications along with the code, runtime and libraries so that they can run consistently across the software development lifecycle.
The biggest advantage – You can run your application reliably when moved from one computing environment to another. The environment could be anything from a developer’s laptop, test environment or from staging to production. You can even move the application from a physical machine in a data center to a virtual machine in the cloud.
For instance, think about Google Maps! The moment you search for a fresh location on your mobile application, the cloud service constructs a new container to manage the workload. Now imagine the number of times people search for locations on Google Maps on a particular day – that’s a lot of containers!
**So why containers? **Traditionally workloads and applications had to be built from scratch if there was a need to migrate to another environment. Containers solved this problem with the concept of “isolation”. They are lightweight software components that package the application along with its dependencies & configurations in an isolated environment on a traditional OS, traditional server.
“Isolation” is important here. Isolation delivers-
The reason why the industry has been so excited about containerization is the flexibility they offer and the faster pace of application development that comes with it. Containers and orchestration engines like Kubernetes are paving the way for a new era of application development where modern concepts like microservices and continuous development and delivery are the new normal.
The flexibility we just spoke about also leaves containers susceptible to security risks. On one hand, containers have transformed the way applications are built & scaled and on the other, this has given rise to challenges around security, storage, and networking.
You can’t possibly wait until you go into production to integrate a container security solution. It will nullify the advantages gained from DevOps processes when deployments are delayed because of security matters popping up at the end of the development cycle.
Let’s take a look at some of the findings in the State of Container and Kubernetes Security Report, 2020.
Container technology is one of the major drivers of IT innovation & digital transformation. However, the fact that 44% of respondents agreed that they had to delay application deployments into production because of a container security issue indicates an aching fact – organizations are unable to tap into its biggest benefit i.e. faster app delivery.
#business insights #devops #container security #containerization #encryption #what are containers
This is our second post on cloud deployment with containers. Looking for more? Join our upcoming GitHub Actions webcast with Sarah, Solutions Engineer Benedict Oleforo, and Senior Product Manager Kayla Ngan on October 22.
In the past few years, businesses have moved towards cloud-native operating models to help streamline operations and move away from costly infrastructure. When running applications in dynamic environments with Docker, Kubernetes, and other tooling, a container becomes the tool of choice as a consistent, atomic unit of packaging, deployment, and application management. This sounds straightforward: build a new application, package it into containers, and scale elastically across the infrastructure of your choice. Then you can automatically update with new images as needed and focus more on solving problems for your end users and customers.
However, organizations don’t work in vacuums. They’re part of a larger ecosystem of customers, partners, and open source communities, with unique cultures, existing processes, applications, and tooling investments in place. This adds new challenges and complexity for adopting cloud native tools such as containers, Kubernetes, and other container schedulers.
At GitHub, we’re fortunate to work with many customers on their container and DevOps strategy. When it comes to adopting containers, there are a few consistent challenges we see across organizations.
Despite the few challenges of adopting containers and leveraging Kubernetes, more and more organizations continue to use them. Stepping over those hurdles allows enterprises to automate and streamline their operations, here with a few examples of how enterprises make it work successfully with support from package managers and CI/CD tools. At GitHub, we’ve introduced container support in GitHub Packages, CI/CD through GitHub Actions, and partnered within the ecosystem to simplify cloud-native workflows. Finding the right container tools should mean less work, not more—easily integrating alongside other tools, projects, and processes your organization already uses.
#engineering #actions #containerized #deployment #devops #github actions #github packages #packages
One of the few remaining challenges of deploying applications as microservices, running in containers, is complexity. A cloud environment may start as a simple ecosystem for microservices, but it doesn’t take much for that simplicity to be replaced by a complex web of containers.
Modern container orchestration systems like Kubernetes don’t really help with simplicity either. In fact, it is too easy to end up with a complex network of pods across multiple clusters whenyou don’t incorporate Kubernetes deployment best practices correctly.
When it was first made popular back in late 2019, the Cloud Native Application Bundles, or CNAB, is designed to combat the complex nature of containerization. Today, CNAB has become the go-to standard for simplifying the process of bundling, installing, and managing apps in containers.
CNAB was first introduced in 2018, but it wasn’t widely adopted until late in 2019. As a standard, one of the biggest advantages offered by CNAB is its support for any cloud computing environment. Yes, it is a cloud-agnostic approach that can be implemented in any environment.
In fact, the primary goal of CNAB is to make containerization easier to incorporate across different cloud environments. Moving a bundled app from one cloud cluster to another should involve nothing but the standardized process.
CNAB itself is a tool that was initially developed by Microsoft and Docker. Considering that Docker is still used on top of existing container orchestration systems, it is easy to see how CNAB immediately became the universal tool to use.
Before we get to the technical side of CNAB, there are several additional advantages to acknowledge first, starting with the fact that CNAB makes deploying applications easy. Since packages are standardized, you can scale up the distribution of pre-made apps.
That level of standardization is perfect for developers offering on-premise or self-hosted solutions. Rather than having to manually install and configure everything, applications can be delivered as packages or bundles, and any cloud administrator can install them easily.
CNAB also highlights security as an important factor. The signing and verification runtimes that are part of CNAB makes securing bundled apps easy. In fact, administrators can always check the crypto signature of bundled apps to make sure that they come from legitimate sources.
#cloud #kubernetes #cloud native #containerization #container adoption #cnab
In this article, we will not discuss developing Machine Learning model but rather containerizing our ready to deploy ML API (Flask) with the help of Docker so that there is no hassle that our model is not working in the production environment or while deploying it on cloud or simply sharing the working model to friends and colleagues.
Note: Docker is kind of useful as well as in trend nowadays so better start containerizing your models.
Firstly, I assume that if you are reading this article you may be already familiar with the Docker. If not, don’t worry I’m still going to define that in my own layman’s terms.
#tutorial #machine learning #docker #artificial intelligence #deep learning #mlops #containerize