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How to Jenkins on a Local Windows Machine

Jenkins helps developers automate tasks, facilitating continuous integration and continuous delivery (CI/CD), and improving collaboration among development teams.
Installing Jenkins on your local Windows machine allows you to experiment with Jenkins features & develop and test CI/CD pipelines locally.
This guide walks you through two methods to install Jenkins on Windows - 1)Using Docker, 2)Using Windows Subsystem for Linux (WSL).
Docker involves less configuration, offers lightweight containerization, better isolation, consistency, easy maintenance, and portability compared to running full Linux distribution in WSL.
Using Docker also allows users to interact with Docker on the host machine to build images and manage containers from within Jenkins jobs.
Security considerations while granting access to Docker daemon include - use proper authentication and authorization within Jenkins, consider using Docker-in-Docker or Docker agents with limited permission for additional isolation.
Alternatively, plugins like the Docker Pipeline plugin provide DSL for building and running containers that abstracts Docker operations and can improve security.
Integration with Jenkins helps improve collaboration between development and operations teams, accelerates software delivery, and improves code quality through continuous integration and testing.
Happy Building with Jenkins!

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Exploring the Use of Amazon Web Services for Email Address Management

Amazon Web Services (AWS) offers a range of solutions for email address management, including Amazon WorkMail and Amazon SES.
Amazon WorkMail is a secure, managed business email and calendaring service from AWS that offers seamless integration with existing corporate directories and native support for standard email protocols.
Amazon SES provides robust email authentication mechanisms, including DKIM and SPF, to ensure the integrity and authenticity of email messages, with DMARC policy to help in monitoring and enforcing email authentication practices.
AI and machine learning are reshaping customer engagement strategies, automating complex tasks, improving efficiency, and enabling businesses to personalize email content and anticipate customer needs.
Neo leverages AI to enhance user engagement and streamline email marketing strategies, with features like personalization of email content and automated decision-making.
Choosing the right email solution is crucial for businesses, and it's important to consider the total cost of ownership, integration with other business tools, and the unique features and benefits of each solution.
Implementing multi-tenancy with Amazon Pinpoint allows businesses to scale efficiently by managing multiple customer segments and product lines within a single AWS account.
The use of Amazon Web Services for email address management offers a robust and secure solution, with a growing focus on AI, integrated marketing tools, and data protection.
Organizations can rely on Amazon WorkMail and Neo to maintain a secure and compliant email management system.
When choosing between Amazon WorkMail and Neo, businesses should consider factors such as the need for a domain name, cost-benefit analysis, integration with other Amazon services, and the right email solution that aligns with their communication and collaboration needs.

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Dev

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🔋⚡ Ensuring High Availability with Two-Server Setup Using Keepalived

Ensuring high availability with limited resources can be challenging.
The author demonstrates achieving high availability using Keepalived and two servers.
The process involves creating a Vagrantfile, spinning up the VMs, configuring Keepalived, testing the setup, and simulating failover.
Lessons learned include the simplicity and power of Keepalived, achieving high availability on a budget, and the importance of practical testing.

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Devopsonline

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Winners Announced for the North American Software Testing Awards 2024

The winners of the North American Software Testing Awards 2024 have been officially announced.
The awards ceremony took place on November 27, 2024, in Toronto, Canada.
The program included 16 categories to showcase innovation, technical skills, and commitment to quality.
The upcoming Asia Pacific Software Testing Awards are now open for participation.

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Dev

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Decoding the Design: The Evolution of the Amazon Web Services Logo

The AWS logo has undergone several transformations since its inception, each reflecting the company's growth and its expanding suite of cloud services, which has helped solidify its position as a leader in cloud computing.
The AWS current logo presents a refined visual identity that encapsulates the brand's evolution and commitment to innovation, symbolizing the company's forward-thinking approach and its role in empowering businesses and developers around the world.
AWS offers scalable, efficient, and cost-effective cloud solutions for businesses, with key features like automatic scaling and diverse cloud services.
The AWS logo reflects the company's commitment to innovation, customer-centricity, and operational excellence. The clean and modern design signifies AWS's commitment to providing cutting-edge cloud solutions.
Future iterations of the AWS logo are expected to balance brand heritage with innovation and represent the company's foray into advanced areas such as Generative AI and Machine Learning.
The AWS logo is not just a brand mark; it's a symbol of technological advancement and a testament to the company's enduring vision to be Earth's most customer-centric company.
The AWS logo's design elements symbolize cloud computing, innovation, and reliability.
The AWS logo plays a pivotal role in establishing the brand's market presence. Consistency in branding, especially through the logo, has enabled AWS to maintain a strong identity across various platforms and services.
The AWS logo is a cornerstone of the brand's marketing and promotional strategies, serving as a visual anchor across various platforms.
The AWS logo's adaptability is crucial for integrating it with a multitude of new services and technologies.

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Amazon

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Leverage powerful generative-AI capabilities for Java development in the Eclipse IDE public preview

Amazon Q Developer in the Eclipse IDE brings the power of AI-driven development directly into one of the most popular development environments.
This integration is set to become an indispensable generative AI-assistant that accelerates tasks across the software development lifecycle including coding.
During the public preview, Eclipse developers will be able to chat with Amazon Q Developer about their project and code faster with inline code suggestions.
Amazon Q Developer analyzes your code, comments, and naming conventions to provide context-aware suggestions which enhances productivity across the entire software development lifecycle.
Amazon Q Developer chat interface serves as a versatile tool for various development needs, users can seek guidance on implementing specific functionalities.
With customizations, Amazon Q Developer can assist with software development in ways that conform to your team’s internal libraries, proprietary algorithmic techniques, and enterprise code style.
The preview represents a significant step forward in enhancing the development experience within this trusted platform.
The integration of cutting-edge AI can supercharge your development tasks across the software development lifecycle.
Download the Amazon Q Developer plugin into your Eclipse IDE to get started.
Amazon Q Developer plugin for Eclipse IDE is set to revolutionize the familiar comfort of Eclipse with cutting-edge capabilities, making Java developers more efficient and productive.

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Siliconangle

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AI-driven observability: Tackling the five C’s of modern workflows

The complexities of Kubernetes, containerization, and serverless workflows have posed challenges to observability.
Kloudfuse, an AI-enabled observability company, aims to address observability challenges.
Challenges include cardinality, control over data, cost efficiency, and identifying root causes of operational issues.
Consolidating data into a single observability data lake can reduce costs and improve efficiency.

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Siliconangle

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Agentic AI and Kubernetes: Vultr’s vision for scalable cloud solutions

Vultr is leading the charge in scalable cloud solutions, particularly in the deployment of Kubernetes AI.
Kubernetes AI deployment revolutionizes the integration of artificial intelligence into organizations' operations, enhancing performance and prioritizing security in cloud environments.
Vultr's infrastructure enables rapid delivery to 90% of the global population in under 40 milliseconds, making it ideal for web applications and AI model deployment.
Vultr leverages Kubernetes and cloud infrastructure to enable scalable, secure, and efficient deployment of AI-driven applications globally.

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Siliconangle

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Closing the skills gap: Andela and CNCF train Africa’s future technologists

Andela Inc. has developed skilling programs with the Cloud Native Computing Foundation to enhance Kubernetes mastery in Africa.
90% of the roles created by companies with Andela require Kubernetes skills, leading to a strategic learning partnership.
Andela connects African talent to jobs in the tech industry and has trained 15% of the African developer population.
Learning programs with companies like Nvidia, Microsoft, AWS, and Meta have been initiated to strengthen Kubernetes skills in African developers.

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Hashicorp

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Terraform 1.10 improves handling secrets in state with ephemeral values

Terraform 1.10 introduces ephemeral values for secure secrets handling.
Ephemeral values are not stored in any artifacts like plan or state files.
Ephemeral values include ephemeral input variables and output variables, ephemeral resources, and managed resources' write-only attribute.
Terraform 1.10 also includes performance improvements and other enhancements.

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Siliconangle

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Streaming data infrastructure: Scaling AI with cloud-native innovation

Efficient streaming data infrastructure is crucial for scaling AI training and operational insights in the cloud-native era.
Challenges of scaling technologies like Apache Kafka include escalating costs, operational complexities, and inefficiencies of legacy architectures.
Buf Technologies focuses on modernizing streaming data infrastructure by addressing inefficiencies in traditional systems and emphasizing cost efficiency and scalability.
They emphasize the importance of schema-driven development and the role of schemas like Protobufs in ensuring data integrity and compatibility.

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Dev

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Join us for the DevCycle Feature Flag Challenge: $1,000 in Prizes!

The DevCycle Feature Flag Challenge is a collaboration with DevCycle to explore creative uses of feature flags.
Participants are encouraged to build an app showcasing unique and imaginative ways to use feature flags.
There are three ways to win: overall prompt winner, OpenFeature Aficionado, and API All-Star.
Prizes include cash, DEV++ Membership, exclusive badges, and gifts from the DEV Shop.

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Javacodegeeks

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Kafka Consumer Offset Example

In Kafka, a consumer offset represents the position of a consumer within a partition of a topic.
Managing offsets is critical for preventing the reprocessing of messages, recovering from failures or restarts, and enabling independent message processing.
Offsets can be automatically managed or manually handled by the consumer application for greater control.
Proper offset management is fundamental to leveraging Kafka’s reliability and scalability in distributed systems.
Consumer offsets play a vital role in managing the flow of messages and maintaining system resilience.
Understanding consumer offsets is crucial for ensuring the efficient and reliable operation of Kafka-based systems.
For scenarios where finer control over offsets is required, auto-commit can be disabled, and offsets can be handled manually.
Leveraging Kafka monitoring tools to visualize consumer group lag is a best practice for offset management.
Kafka stores consumer offsets in a special internal topic named __consumer_offsets.
Understanding and managing Kafka consumer offsets effectively involves several best practices.

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Dev

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API Testing: Ensuring Reliable and Robust Software Applications

API testing focuses on validating the functionality, performance, and security of APIs.
API testing ensures stability, improves user satisfaction, and safeguards the reputation of businesses.
Types of API testing include functionality, performance, security, and interoperability testing.
API testing challenges include complex API structures, incomplete documentation, and testing edge cases.

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Dev

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What We Learned From Analyzing 20.2 Million CI Jobs In Trunk Flaky Tests - Part 2

Flaky tests pose a significant challenge for most teams that write automation tests.
In this part, we discuss various technical and product challenges involved in building Trunk Flaky Tests.
One of the major challenges is scaling. Production test suites can be huge, and letflaky tests accumulate will become debilitating.
Processing and managing test data across different circumstances, such as different branches, can also be challenging.
Moreover, different types of UI require precomputing metrics and caching query data, which is again time-consuming.
Building solutions that operate on imperfect, non-standard, or low-quality data from a variety of testing stacks with legacy code is also a significant challenge.
Quarantining, which isolates failures for known flaky tests, is tedious, highly specific, and must operate on custom platforms and environments.
Trunk Flaky Tests Public Beta has three distinct screens to condense the massive amount of information for different use-cases.
Even during a limited private beta, having access to many diverse technology stacks and millions of test runs gives Trunk Flaky Tests an edge in refining algorithms continuously.
Trunk Flaky Tests aims to reduce the cost of trial and error when dealing with flaky tests by providing a more significant and effective solution.

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