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In the digital era, where online interactions and transactions have become the norm, the importance of identity verification cannot be overstated. For WordPress site owners, implementing ID verification is a…
The post Enhancing Security and Trust: The Importance of ID Verification in Your WordPress Site first appeared on WPArena and is written by Editorial Staff.
I want to write an SEO load more button that Google crawls and click on. I have read somewhere in the documents that Google will only click on anchor tags with href value. My current website loads content using a button and I have noticed after changing some, not load more, links Google started to index my website.
Is there a best practice method to do this?
Will it index more pages on my site?
I am not sure if I should post this in SEO or Web Development section :)
Much has changed since I wrote the article An Introduction to BentoML: A Unified AI Application Framework, both in the general AI landscape and BentoML. Generative AI, Large Language Models, diffusion models, ChatGPT (Sora), and Gemma: these are probably the most mentioned terms over the past several months in AI and the pace of change is overwhelming. Amid these brilliant AI breakthroughs, the quest for AI deployment tools that are not only powerful but also user-friendly and cost-effective remains unchanged. For BentoML, it comes with a major update 1.2, which moves towards the very same goal.
In this blog post, let’s revisit BentoML and use a simple example to see how we can leverage some of the new tools and functionalities provided by BentoML to build an AI application in production.
In the rapidly evolving landscape of container orchestration, Kubernetes has emerged as the de facto standard, offering a robust framework for deploying, managing, and scaling containerized applications. One of the cornerstone features of Kubernetes is its powerful and flexible scheduling system, which efficiently allocates workloads across a cluster of machines, known as nodes. This article delves deep into the mechanics of Kubernetes scheduling, focusing on the pivotal roles of pods and nodes, to equip technology professionals with the knowledge to harness the full potential of Kubernetes in their projects.
A pod is the smallest deployable unit in Kubernetes and serves as a wrapper for one or more containers that share the same context and resources. Pods encapsulate application containers, storage resources, a unique network IP, and options that govern how the container(s) should run. A key concept to grasp is that pods are ephemeral by nature; they are created and destroyed to match the state of your application as defined in deployments.
Building scalable systems using microservices architecture is a strategic approach to developing complex applications. Microservices allow teams to deploy and scale parts of their application independently, improving agility and reducing the complexity of updates and scaling. This step-by-step guide outlines the process of creating a microservices-based system, complete with detailed examples.
Identify the distinct functionalities within your system that can be broken down into separate, smaller services.
Salesforce CRM Analytics is a cloud-based Business Intelligence (BI) and visualization framework seamlessly integrated into the Salesforce platform, designed to enable business insights, predictive analytics, and recommendations by integrating the Salesforce data with external big data sources.
CRM Dataflows and recipes are effective Data Transformation tools providing extensive ETL (Extract, Transform, and Load) capabilities. Dataflows can be used to Extract Data from Salesforce Local objects or External Big Data sources, Transform the Input data by performing data operations like Filter, Modify, Group, Cleanse, Append, Slice, and Transform, and Load the transformed Data into the datasets and make it available for deriving the Business Insights in the CRM Analytics Dashboards and Lens.
Cross-Origin Resource Sharing (CORS) often becomes a stumbling block for developers attempting to interact with APIs hosted on different domains. The challenge intensifies when direct server configuration isn't an option, pushing developers towards alternative solutions like the widely-used cors-anywhere. However, less known is the capability of NGINX's proxy_pass directive to handle not only local domains and upstreams but also external sources, for example:
This is how the idea was born to write a universal (with some reservations) NIGNX config that supports any given domain.
Distributed databases partition the data across several nodes, spreading across regions depending on the database configuration. Such partitioning is fundamental to achieving scalability. All such cloud-native databases have some sort of a session management layer. A session, in plain terms, is the span of communication between a database client and server. It can span multiple transactions. I.e., in a given session, a client can do many writes and reads. The session management layer is usually responsible to guarantee “read your own writes”. I.e., data written by a user must be available for reading in the same session.
In the distributed database world, with many regions serving the database, reads can happen from anywhere. There is fundamentally a need to distinguish between “Not Found” and “Not Available” scenarios. I.e. in the former case data does not exist while in the latter case data is yet to be seen by the region. This is important to provide “read your own write” guarantee. For example, let’s look at the time steps that happen in the below picture.
Cloud computing has revolutionized software organizations' operations, offering unprecedented scalability, flexibility, and cost-efficiency in managing digital resources. This transformative technology enables businesses to rapidly deploy and scale services, adapt to changing market demands, and reduce operational costs. However, the transition to cloud infrastructure is challenging. The inherently dynamic nature of cloud environments and the escalating sophistication of cyber threats have made traditional security measures insufficient. In this rapidly evolving landscape, proactive and preventative strategies have become paramount to safeguard sensitive data and maintain operational integrity.
Against this backdrop, integrating security practices within the development and operational workflows—DevSecOps—has emerged as a critical approach to fortifying cloud environments. At the heart of this paradigm shift is Continuous Security Testing (CST), a practice designed to embed security seamlessly into the fabric of cloud computing. CST facilitates the early detection and remediation of vulnerabilities and ensures that security considerations keep pace with rapid deployment cycles, thus enabling a more resilient and agile response to potential threats.
Behave, a Python-based behavior-driven development (BDD) framework for writing human-readable tests that describe the expected behavior of software systems. On the other hand, Terraform is an infrastructure as code (IaC) tool that streamlines the management of infrastructure by enabling developers to define resources and configurations in a declarative manner. By combining Behave's BDD approach with Terraform, you can ensure that infrastructure behaves as expected under various conditions. This integration facilitates early detection of issues and the reliability of infrastructure code.
Testing Terraform configurations with Behave involves a series of structured steps:
Every time you start developing your app, you’re left with the initial drag of setting up your development environment.
It carefully requires you to connect the different technologies, follow conventions, and ensure everything is set up properly before writing the first business logic code.
Editor's Note: The following is an article written for and published in DZone's 2024 Trend Report, The Modern DevOps Lifecycle: Shifting CI/CD and Application Architectures.
Forbes estimates that cloud budgets will break all previous records as businesses will spend over $1 trillion on cloud computing infrastructure in 2024. Since most application releases depend on cloud infrastructure, having good continuous integration and continuous delivery (CI/CD) pipelines and end-to-end observability becomes essential for ensuring highly available systems. By integrating observability tools in CI/CD pipelines, organizations can increase deployment frequency, minimize risks, and build highly available systems. Complementing these practices is site reliability engineering (SRE), a discipline ensuring system reliability, performance, and scalability.
News of companies laying off people in Agile roles are circulating frequently. What is going on?
For years now, I’ve been coaching companies and leaders to see the Scrum Master and similar roles like the SAFe RTE as accountabilities relevant leaders take on (it can be formal managers/leaders or natural leaders from within teams that are passionate about agility and gravitate to this). See the Scrum Guide for Leaders.
Docker Swarm is a popular container orchestration technology that makes containerized application administration easier. While Docker Swarm provides strong capabilities for deploying and scaling applications, it’s also critical to monitor and report the performance and health of your Swarm clusters. In this post, we will look at logging and monitoring in a Docker Swarm environment, as well as best practices, tools, and tactics for keeping your cluster working smoothly.
Before we delve into the technical aspects of logging and monitoring in a Docker Swarm environment, let’s understand why these activities are crucial in a containerized setup.