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Getting Started with GitLab

GitLab is a DevOps platform that provides version control, CI/CD pipelines, and collaborative tools for software development.
Important GitLab terminologies include repository, branch, merge request, pipeline, runner, jobs, artifacts, tags, groups, and issues.
To get started with GitLab, you can create an account, create a new project, clone the repository, add files and push them.
An example GitLab workflow involves creating a feature branch, making changes, opening a merge request, and setting up a CI/CD pipeline.

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The New Stack

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Honeycomb.io’s Austin Parker: OpenTelemetry In-Depth

OpenTelemetry is a framework and toolkit designed to create and manage telemetry data such as traces, metrics, and logs.
OpenTelemetry is currently the second most active project of the Cloud Native Computing Foundation, surpassed only by Kubernetes.
Observability for Kubernetes is fully supported by OpenTelemetry (OTel).
Most of the challenges in integrating OpenTelemetry with Kubernetes aren’t necessarily specific to Kubernetes, although they are exacerbated in some ways. The biggest challenge is, easily, data management.
Your goal is to balance these, while making tradeoffs between telemetry resolution, telemetry availability, and query performance.
Generally, OpenTelemetry integrates well with other observability tooling, especially if those tools support OpenTelemetry Protocol (OTLP).
In OpenTelemetry, the former is any of our zero-code instrumentation agents, and the latter is the OpenTelemetry Collector.
Effective alerting on Kubernetes services can dramatically reduce the amount of time needed in order to discover the contributing factors to an incident.
OpenTelemetry has emerged as a clear and unambiguous standard for multi-modal observability data in cloud-native systems.
In the future, I believe we’ll see OpenTelemetry further expand into new domains — user analytics, business events, developer productivity, carbon emissions and resource cost, to name a few.

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Docker

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How to Set Up a Kubernetes Cluster on Docker Desktop

Kubernetes is an open source platform for managing containerized applications in production environments.
Docker Desktop lets you run Kubernetes directly on your local machine, making it easy to test microservices and containerized apps without needing a remote cluster.
Setting up a Kubernetes cluster can be complex for developing and testing local environments, but with Docker Desktop, it is simpler.
Docker Desktop’s Kubernetes cluster is designed for local development and testing and not for production use.
Running Kubernetes via Docker Desktop offers several benefits like easy local Kubernetes cluster set up, developer convenience, and testing of Kubernetes-based applications locally and CI/CD pipelines.
The article provides step-by-step instructions on how to start a Kubernetes cluster on Docker Desktop, along with troubleshooting tips and resources.
Docker bundles Kubernetes but does not provide official Kubernetes support.
Troubleshooting Kubernetes issues can include generating a diagnostics file and checking logs.
You can search for known Kubernetes issues on GitHub and try rebooting or resetting your Kubernetes cluster as a last resort.
Running Kubernetes on Docker Desktop is an easy setup that allows developers to test Kubernetes-based applications locally and improve their local Kubernetes workflow.

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Dev

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OpenSearch metrics challenge: can you spot the performance flaw?

An analysis of metrics can identify hidden performance issues to improve system efficiency.
This article is about a real-world case involving an error for a high-performance OpenSearch cluster.
The OpenSearch cluster was configured with 12 nodes to meet growing performance requirements, but performance couldn't improve anymore.
The article focuses on three high-level metrics - Indexing Data Rate, HTTP requests by response code and Search rate - to identify the flaw.
It was found that the root cause of the anomaly was due to how queries were executed by a component leveraging a shared library that was lacking the index pattern parameter.
The impact of the error was significant and led to an unintended consumption of resources.
Correcting the code to always specify the index pattern in queries led to a significant improvement in performance and resource utilization.
Horizontal scaling-in of the cluster reduced cloud costs, highlighting the importance of monitoring performance metrics.
This experience highlights the need to pay attention to detail in analysis, monitor metrics and avoid simple coding errors.
OpenSearch's good performances can hide issues that might go unnoticed and have severe consequences.

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Alvinashcraft

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Dew Drop – January 7, 2025 (#4336)

Microsoft AI resources developer home has been refreshed
Visual Studio’s Meter Histogram to unlock insights
Metalama 2025.0 generally available with C# 13 and .NET 9 support
Introducing the new Angular chat UI component
The year 2025: CES 2025 will be the year of the Windows 11 PC refresh, says Yusuf Mehdi
Upcoming CVE for end-of-life Node.js versions
Critter Stack Roadmap for 2025 announced by Jeremy D. Miller
Top 5 JavaScript Gantt Chart Libraries for 2025
Customizing and integrating Telerik slider control in .NET MAUI projects
The importance of investing in soft skills in the age of AI

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Dev

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DevOps vs NoOps: What is the future of operations?

DevOps is a culture and practice that combines development and operations to automate software delivery.
NoOps is a concept where infrastructure and operational tasks are completely automated, eliminating the need for a dedicated operations team.
NoOps and DevOps can coexist and satisfy different needs based on the complexity and control required by the organization.
Artificial intelligence is playing a key role in reshaping both DevOps and NoOps, reducing manual effort and improving decision-making.

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Dev

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Docker Install In Ubuntu or on Cloud (AWS provisioning)

To install Docker on Ubuntu:
1. Update Package: sudo apt update
2. Install Required Packages: sudo apt-get install ca-certificates curl
3. Add Docker's official GPG key
4. Add Docker Repository
To install Docker on AWS by provisioning:
1. Provision EC2 Instance with desired specifications
2. Paste the provided script in user data during instance setup
3. Launch the instance and wait for it to be ready
4. Verify Docker installation by running 'docker --version'

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Dev

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Mastering AWS DevOps: Tips, Tools, and Best Practices for 2025

The demand for seamless CI/CD pipelines, automated workflows, and scalable cloud solutions is rising in AWS DevOps.
Tips for mastering AWS DevOps include starting with core AWS services, leveraging tools like CloudFormation for infrastructure as code, using monitoring and logging tools like CloudWatch and X-Ray, automating everything with services like CodeBuild and CodeDeploy, and working on hands-on projects.
To learn AWS DevOps in 2025, choose practical and industry-aligned learning resources such as online courses, certifications, and training from reputed institutes.
AWS DevOps requires embracing automation, staying curious about new tools, and continuously experimenting.

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Dev

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Can Cloud Data Be Hacked

Cloud computing has become an integral part of our daily lives, but it is not immune to security risks.
Understanding the concept of cloud computing is crucial before addressing data security.
Robust encryption and access control measures are necessary to protect data in the cloud.
Cloud computing offers advantages such as flexibility, cost reduction, and dynamic IT resource adjustment.

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Dev

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Bringing Together Containers & SQL

The mini project is a combination of some tools and technologies that are new to my knowledge and tools such as SQL and Python that I have used in the past.
The application builds visualisations based on the queries it asks the users to input on the dashboard; a dashboard for data stored on SQL databases.
The connection to the SQL database is established using the mysql.connector written within a function based on updated credentials.
Flask API is the heart of the application which works with the endpoint /run_query and handles both GET and POST requests for sending and retrieving the input from the users.
A key feature integrated involves converting the outputs as graphs and creating charts using Matplotlib. The charts can vary as per the data being fetched.
The frontend is basic but does the job; the form collects the queries and displays the chart depending upon whether the query was executed or not.
The 'Dockerfile' - serves as an image for the Flask app and the 'compose file' - specifies the services that contain the multiple containers present in the application, two containers have been specified - Flask and SQL. They have been linked for the application to run.
While integrating containers and SQL, it became clear that using SQL while containerising the application was slightly different and needed some more setups to be done manually to copy the data.
The project lays a foundation for building more robust and feature-rich applications in the future, with opportunities for scalability, improved frontend design, and enhanced user engagement.

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Dev

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Build a CI/CD Pipeline Using AWS Glue, AWS CDK and GitHub

AWS Glue has evolved into a powerful tool for building scalable ETL solutions, but managing Glue jobs manually can be a pain point.
Using AWS CDK in combination with GitHub and CI/CD workflows has made deploying Glue jobs faster, more reliable, and far less error-prone.
To ensure consistency and collaboration across the team, a structured development workflow is followed integrating tools like Jira for task tracking and integrated with GitHub to map tickets to git branches.
Each component folder contains sub-folders for configuration files and Python-based Glue scripts. AWS CDK stacks are used to define Glue jobs for each component.
Separation of default and custom configurations gives flexibility to manage AWS Glue jobs efficiently.
Using a CI/CD-driven workflow for deploying AWS Glue jobs has significantly improved deployment process and reduces the chances of errors.
Every change is traceable, reviewable and deployed consistently across the environments.
The workflow works well for the author, but the workflows evolve with the project goals and requirements.
All views and opinions expressed in the blog are personal to the author and not necessarily those of the employer.

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Dev

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Back-End Engineer with Expertise in Cryptocurrency, Blockchain, and Social Media

INCARNATE is seeking a Back-End Engineer with expertise in cryptocurrency, blockchain, and social media.
Responsibilities include system architecture, cryptocurrency integration, API development, cybersecurity, algorithm development, data management, collaboration, performance optimization, and staying updated on emerging technologies.
Qualifications include a degree in Computer Science or related field, proven back-end development experience, and technical expertise in programming languages, blockchain technologies, cloud platforms, and databases.
Preferred skills include experience with decentralized applications, Web3 technologies, social media algorithms, and previous experience at a leading social media or blockchain company.

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Dev

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What Isn't A PaaS

PaaS is a developer self-service platform that automates as much of the infrastructure deployment as possible allowing developers to simply build an app and deploy it without requiring additional infrastructure configuration.
Here are a few characteristics of offerings which disqualify an application as a PaaS: If you can’t supply application configuration at deployment time, if you have to configure an ingress (or proxy), if it doesn’t give you a DNS name, if it doesn’t generate SSL certificates and if it doesn’t provide a developer workflow.
An app developer should not have to deal with low-level infrastructural changes that impact the PaaS, the underlying platform, and the security of the broader network.
If an app developer receives just an IP address after deploying an app, that developer will immediately become an operations engineer dealing with DNS.
SSL is a tables stakes feature for any HTTP-based app and if the system doesn’t issue certificates at app deployment, it is not a PaaS.
Steps 1 and 2 seem to be ignored by some tools that claim to be PaaSes, while the absence of step 4, which handles upgrades to an application, forces the developer to do infrastructure finagling that is unnecessary.
Fermyon Cloud is a PaaS that does not violate any of these terms, automating networking, DNS, SSL, configuration, and deployment management.
As an industry, we should strive to maintain standards and not play fast and loose with our terminology, lest we revert to a less developer-centric age.
The promise of PaaS is that it can automate much of the infrastructure deployment process and allow developers to focus on building apps, not on configuring infrastructure.
Let’s work towards building better developer self-service platforms, leveraging PaaSes’ v1’s example to ensure that new PaaS offerings do even better.

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Dev

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The Ripple Effect: How a Single Push Notification Brought Down Our Kubernetes Cluster

A simple push notification exposed the fragility of Kubernetes infrastructure when it sent a notification to the user base. This resulted in traffic exploding by 12x on some services, nodes CPU utilization went from 45% to 95%, and pods were evicted faster than they could be stabilized.
The team targeting rapid scaling capability, resource efficiency, reliability and cost optimization pushed the infrastructure to new levels by redesigning their infrastructure and initial platform setup.
The team redesigned EKS control plane architecture and implemented a robust Multi-AZ Configuration along with creating a dedicated VPC for cluster operations, implementing private API endpoints, optimizing CNI settings, and implementing security measures.
By tackling bottlenecks and issues with CNI configurations, suboptimal route tables, DNS resolution and analyzing kubelet startup procedures, container runtime configurations, and node initialization scripts, dramatic improvements were seen in node boot times, CNI setup, image pull times and pod scheduling times.
Karpenter and KEDA implementations further accelerated the node provisioning time, scale-up decisions and resource utilization.
Today, the team's platform runs with newfound confidence with average node provisioning time, p95 pod scheduling latency, resource utilization, and platform availability metrics reflecting transformation.
In Kubernetes, every setting, limit, and policy creates its own ripple effect. Understanding and harnessing them is key to success.
Future directions include exploring component-level analysis, performance optimization techniques, and testing methodologies to catch problems before production.
Team seeks to learn from others' hard lessons by sharing their experiences in the comments section of the post.

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Dev

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The Unsung Hero of DevOps AWS X-Ray

AWS X-Ray is a powerful tool for analyzing and debugging distributed applications in a DevOps environment.
It provides complete visibility into application services, helping to identify latency issues and system failures.
X-Ray allows tracking of application performance and identifies bottlenecks for optimization.
By implementing X-Ray, DevOps teams can troubleshoot quickly, optimize resources, and improve user experience.

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