Large language models (LLMs) have taken the tech industry by storm in recent years, unleashing new frontiers of innovation and disrupting everything from search to customer service. Underpinning this revolution in artificial intelligence are open ecosystems like GitHub and Hugging Face, which enable developers and companies to build, deploy and scale LLMs rapidly. Just as GitHub has become the go-to platform for software development and collaboration, Hugging Face is now emerging as the de facto hub for all things related to LLMs. The Rise of Large Language Models LLMs like GPT-3, BERT and PaLM have captured the imagination of the tech world with their ability to generate human-like text, answer questions, summarize documents and even write code based on simple text prompts. According to a McKinsey report, investments in natural language processing startups focusing on LLMs ballooned from $100 million in 2020 to over $1.5 billion in 2021.

Your approach to DevOps is likely to be influenced by the methods and practices that came before. For organizations that gave teams autonomy to adapt their process, DevOps would have been a natural progression. Where an organization has been more prescriptive in the past, people will look for familiar tools to run a DevOps implementation, such as maturity models. In this article, I explain why a maturity model isn't appropriate and what you should use instead.

In the rapidly advancing world of technology, the continuous search for efficient platforms to streamline Machine Learning Projects is ever-persistent. It is undeniable that GitHub has paved a smooth path for developers around the globe. However, we comprehend the necessity of diversity and innovation in this field. Hence, we bring to your notice the best GitHub-like alternatives that can revolutionize your approach to machine learning projects. Let's delve into some of these platforms that offer robust features and functionalities, which can easily give GitHub a fight. Popular GitHub Alternatives for Machine Learning Projects 1. DVC (dvc.org) Data Version Control (DVC) is a potent tool facilitating streamlined project management and collaboration. At its core, it simplifies data management by integrating closely with Git, which enables tracking changes in data and models meticulously, akin to how Git tracks code variations. This fosters a more organized approach to handling large datasets and brings in a higher degree of reproducibility, as team members can effortlessly roll back to previous versions if required.

This blog post explores the state of data streaming in 2023 for digital natives born in the cloud. The evolution of digital services and new business models requires real-time end-to-end visibility, fancy mobile apps, and integration with pioneering technologies like fully managed cloud services for fast time-to-market, 5G for low latency, or augmented reality for innovation. Data streaming allows integrating and correlating data in real-time at any scale to improve the most innovative applications leveraging Apache Kafka. I look at trends for digital natives to explore how data streaming helps as a business enabler, including customer stories from New Relic, Wix, Expedia, Apna, Grab, and more. A complete slide deck and on-demand video recording are included.

TIBCO recently held its annual TIBCO NEXT conference, outlining its product roadmap and strategy for modernizing its pioneering integration and analytics platform. As a trusted integration anchor for over 25 years, TIBCO aims to simplify connecting systems and data across today's complex hybrid technology landscapes. Several key themes indicate how TIBCO is adapting to emerging needs:

IBM App Connect Enterprise (ACE) is a powerful and widely used integration tool. Developers create integration flows by defining an entry point that receives a message, then processing that message, and finishing by sending or placing the transformed message. Flows consist of a series of nodes and logical constructs. ACE is powerful and flexible — there are many nodes provided specifically to interact with the systems being integrated, however there are also nodes that can run a script or Java code. Because of this, ACE can do pretty much anything, and as such could be considered (although this is not its intended purpose) as an application runtime environment.  An ACE flow is a deployable unit that is inherently stateless, although it can manage its own state. In a traditional server environment, many flows are deployed on an integration server and their execution can be managed and scaled using the workload management features. This makes ACE a natural fit for a Kubernetes environment.

IBM App Connect Enterprise (ACE) is a powerful and widely used integration tool. Developers create integration flows by defining an entry point that receives a message, then processing that message, and finishing by sending or placing the transformed message. Flows consist of a series of nodes and logical constructs. ACE is powerful and flexible — there are many nodes provided specifically to interact with the systems being integrated, however there are also nodes that can run a script or Java code. Because of this, ACE can do pretty much anything, and as such could be considered (although this is not its intended purpose) as an application runtime environment.  An ACE flow is a deployable unit that is inherently stateless, although it can manage its own state. In a traditional server environment, many flows are deployed on an integration server and their execution can be managed and scaled using the workload management features. This makes ACE a natural fit for a Kubernetes environment.

Verifying code changes with unit tests is a critical process in typical development workflows. GitHub Actions provides a number of custom actions to collect and process the results of tests allowing developers to browse the results, debug failed tests, and generate reports. In this article, I show you how to add unit tests to a GitHub Actions workflow and configure custom actions to process the results.

APIs have become increasingly popular in the current SaaS ecosystem due to their ability to seamlessly integrate software systems. APIs provide standardized ways for applications to share data. API monetization is a powerful way for businesses to drive growth and generate revenue from existing API consumer data and usage. By offering your APIs as products or services, your company can tap into new markets, attract more developers, and create self-sustaining ecosystems around your product line. The “API as a product” approach unlocks monetization opportunities for expansion and diversification, leading to increased profits and market share. By turning APIs into revenue streams, organizations can allocate more resources to their engineering departments, empowering them to focus on core product development and innovation. At the same time, by automating the monetization process, companies can alleviate the burden of monitoring and reporting on engineering teams. According to Gartner, 75% of application providers will revise their current product pricing models to support customers’ consumption of APIs by 2025. Monetizing APIs allows you to provide your valuable APIs to external developers, creating a collaborative ecosystem that not only fuels business growth but accelerates innovation.

If you're looking for a top player in the cloud industry, AWS (Amazon Web Services) is a great choice. One of its many offerings is AWS Amplify, a comprehensive set of tools and services that can help developers create, deploy, and manage full-stack web and mobile applications on the AWS platform. Amplify is known for providing complete AWS solutions to mobile and front-end web developers, simplifying the development process with its features. Backend development: Amplify can create and manage serverless backend APIs, authentication and authorization, storage, databases, and other standard services. Frontend development: Amplify provides a variety of libraries and tools for developing frontends in popular frameworks such as React, Angular, Vue.js, and Flutter. Hosting and deployment: Amplify offers a fully managed web hosting service with continuous deployment, so developers can focus on building their apps without worrying about infrastructure. Benefits of Using AWS There are many benefits to using AWS Amplify, including:

In this article, we delve into the exciting realm of containerizing Helidon applications, followed by deploying them effortlessly to a Kubernetes environment.  To achieve this, we'll harness the power of JKube’s Kubernetes Maven Plugin, a versatile tool for Java applications for Kubernetes deployments that has recently been updated to version 1.14.0.  What's exciting about this release is that it now supports the Helidon framework, a Java Microservices gem open-sourced by Oracle in 2018. If you're curious about Helidon, we've got some blog posts to get you up to speed:

In this article, we delve into the exciting realm of containerizing Helidon applications, followed by deploying them effortlessly to a Kubernetes environment.  To achieve this, we'll harness the power of JKube’s Kubernetes Maven Plugin, a versatile tool for Java applications for Kubernetes deployments that has recently been updated to version 1.14.0.  What's exciting about this release is that it now supports the Helidon framework, a Java Microservices gem open-sourced by Oracle in 2018. If you're curious about Helidon, we've got some blog posts to get you up to speed:

Austin, Texas, is a city filled with music, vibrant nightlife, and some legendary BBQ. It is also one of the great tech hubs of the southern United States, home to a wide variety of tech innovators like Indeed, SolarWinds, and Amazon's Whole Foods. It is simultaneously home to one of the largest tech events in the world, SXSW, as well as many smaller tech events, including BSides Austin 2023. Like other BSides, Austin had informative sessions, a number of training opportunities, and several villages, including capture the flag, lockpicking, and more. Here are just a few of the highlights from this year's excellent event.

In this article, we’ll explain how to use Ansible to build and deploy a Quarkus application. Quarkus is an exciting, lightweight Java development framework designed for cloud and Kubernetes deployments, and Red Hat Ansible Automation Platform is one of the most popular automation tools and a star product from Red Hat. Set Up Your Ansible Environment Before discussing how to automate a Quarkus application deployment using Ansible, we need to ensure the prerequisites are in place. First, you have to install Ansible on your development environment. On a Fedora or a Red Hat Enterprise Linux machine, this is achieved easily by utilizing the dnf package manager:

In this article, we’ll explain how to use Ansible to build and deploy a Quarkus application. Quarkus is an exciting, lightweight Java development framework designed for cloud and Kubernetes deployments, and Red Hat Ansible Automation Platform is one of the most popular automation tools and a star product from Red Hat. Set Up Your Ansible Environment Before discussing how to automate a Quarkus application deployment using Ansible, we need to ensure the prerequisites are in place. First, you have to install Ansible on your development environment. On a Fedora or a Red Hat Enterprise Linux machine, this is achieved easily by utilizing the dnf package manager:

In today’s rapidly evolving technological landscape, organizations are increasingly embracing cloud-native architectures and leveraging the power of Kubernetes for application deployment and management. However, as enterprises grow and their infrastructure becomes more complex, a single Kubernetes cluster on a single cloud provider may no longer suffice,  potentially leading to limitations in redundancy, disaster recovery, vendor lock-in, performance optimization, geographical diversity, cost-efficient scaling, and security and compliance measures. This is where the concept of a multi Kubernetes cluster on multi-cloud, combined with a multi-cluster service mesh, emerges as a game-changer. This does sound complex, but let’s walk through and understand each part in the coming sections. In this blog post, we will learn why a multi-cluster setup is needed? How does Istio Service Mesh work on multi-clusters? How does Admiral simplify multi-cluster Istio configuration? And then, we will set up the end-to-end service communication on multi-cloud Kubernetes clusters on AWS and Azure.

In today’s rapidly evolving technological landscape, organizations are increasingly embracing cloud-native architectures and leveraging the power of Kubernetes for application deployment and management. However, as enterprises grow and their infrastructure becomes more complex, a single Kubernetes cluster on a single cloud provider may no longer suffice,  potentially leading to limitations in redundancy, disaster recovery, vendor lock-in, performance optimization, geographical diversity, cost-efficient scaling, and security and compliance measures. This is where the concept of a multi Kubernetes cluster on multi-cloud, combined with a multi-cluster service mesh, emerges as a game-changer. This does sound complex, but let’s walk through and understand each part in the coming sections. In this blog post, we will learn why a multi-cluster setup is needed? How does Istio Service Mesh work on multi-clusters? How does Admiral simplify multi-cluster Istio configuration? And then, we will set up the end-to-end service communication on multi-cloud Kubernetes clusters on AWS and Azure.

Almost every organization has implemented CI/CD processes to accelerate software delivery. However, with this increased speed, a new security challenge has emerged. Deployment speed is one thing, but without proper software checks, developers may inadvertently introduce security vulnerabilities, leading to grave risks to business operations. As a result, most organizations are either making DevOps responsible for ensuring the security of their delivery process or creating a dedicated DevSecOps team with the same goal. In this article, we will discuss the top seven best practices that DevSecOps teams can implement in their CI/CD process to make their software delivery process more secure.

The terms Continuous Integration and Continuous Delivery/Deployment tend to be combined into the acronym CI/CD to describe the process of building and deploying software, often without distinction between the two. The terms describe distinct processes, even if combining them suggests that Continuous Delivery and Continuous Deployment are an extension of Continuous Integration and the execution of both processes is the responsibility of a single tool. Assuming CI/CD is just CI with a deployment step ignores some fundamental differences between the two processes. In this post, we look at:

The rapid growth of the Internet of Things (IoT) and the increasing need for real-time data processing have led to the emergence of a new computing paradigm called edge computing. As more devices connect to the internet and generate vast amounts of data, traditional centralized cloud computing struggles to keep pace with the demand for low-latency, high-bandwidth communication. This article aims to provide a deeper understanding of edge computing, its benefits challenges, and a detailed examination of its application in Amazon and Facebook use cases. Understanding Edge Computing Edge computing is a distributed computing model that moves data processing and storage closer to the source of data generation. Instead of relying solely on centralized cloud data centers, edge computing enables processing to occur at the "edge" of the network, using devices like IoT sensors, local servers, or edge data centers. This approach reduces the amount of data transmitted to and from central data centers, thus easing the burden on network infrastructure and improving overall performance.

In today's digital era, businesses depend on their databases for storing and managing vital information. It's essential to guarantee high availability and disaster recovery capabilities for these databases to avoid data loss and reduce downtime. Amazon Web Services (AWS) offers a remarkable solution to meet these goals via its Relational Database Service (RDS). This article dives into implementing high availability and disaster recovery using AWS RDS. Grasping AWS RDS Amazon RDS is a managed database service, making database deployment, scaling, and handling more straightforward. It accommodates database engines like MySQL, PostgreSQL, Oracle, and SQL Server. AWS RDS oversees regular tasks such as backups, software patching, and hardware provisioning, thus enabling users to concentrate on their applications instead of database management.

In today's digital era, businesses depend on their databases for storing and managing vital information. It's essential to guarantee high availability and disaster recovery capabilities for these databases to avoid data loss and reduce downtime. Amazon Web Services (AWS) offers a remarkable solution to meet these goals via its Relational Database Service (RDS). This article dives into implementing high availability and disaster recovery using AWS RDS. Grasping AWS RDS Amazon RDS is a managed database service, making database deployment, scaling, and handling more straightforward. It accommodates database engines like MySQL, PostgreSQL, Oracle, and SQL Server. AWS RDS oversees regular tasks such as backups, software patching, and hardware provisioning, thus enabling users to concentrate on their applications instead of database management.

In the ever-evolving world of data analytics, choosing the right tools can make all the difference in harnessing the power of data for your organization. Two key players in this arena are Microsoft Fabric and Power BI, each with its unique strengths and applications. In this blog, we'll delve deeper into the comparison between these platforms to help you make an informed choice for your data analytics needs. 1. Purpose of the Platforms Microsoft Fabric: A Holistic Analytics Solution Microsoft Fabric is a comprehensive analytics solution tailored for enterprises of all sizes. Its primary objective is to provide a unified platform that caters to the diverse needs of business users and data analysts alike. Fabric encompasses a wide spectrum of analytics processes, including data movement, data science, real-time analytics, and business intelligence. By consolidating these processes into a single platform, Microsoft Fabric simplifies the complex landscape of enterprise analytics, making it accessible and streamlined.

Legacy integration and analytics provider TIBCO is at an inflection point. Founded in 1997, the company built its reputation as a leader in on-premises enterprise messaging and event processing. But today's era of cloud, containers, and pervasive APIs requires a new approach. At the recent TIBCO NEXT conference, I sat down with Matt Ellis, Senior Director of Product Management, and Rajeev Kozhikkattuthodi, VP of Product, to learn how TIBCO is adapting its connected intelligence platform and product portfolio to meet modern customer needs.

In the realm of software development, Continuous Integration and Continuous Deployment (CI/CD) pipelines have become integral. They streamline the development process, automate repetitive tasks, and enable teams to release software quickly and reliably. But while CI/CD pipelines are a marvel of modern development practices, they also present potential security vulnerabilities. With the integration of various tools, systems, and environments, CI/CD pipelines often deal with sensitive information, making them a potential target for cyber attacks. Consequently, it's crucial to weave in robust security measures to protect these pipelines and maintain the integrity of your development process.

In the highly competitive scenario and vast landscape of Small-Medium Businesses (SMBs) and Mid-Market Enterprises (MMEs), choosing the ideal ERP solution can be an intricate task. In this post, we will compare two popular ERP platforms: SAP Business One and NetSuite. This comparison will help you make the right decision for your company. Both these "all-in-one" ERP solutions cater to various industry sectors with comprehensive functionalities and enterprise-level features. Startups and SMBs that focus on achieving rapid growth consider SAP Business One and Oracle NetSuite as their go-to choices.

What Is Kubernetes RBAC? Often, when organizations start their Kubernetes journey, they look up to implementing least privilege roles and proper authorization to secure their infrastructure. That’s where Kubernetes RBAC is implemented to secure Kubernetes resources such as sensitive data, including deployment details, persistent storage settings, and secrets. Kubernetes RBAC provides the ability to control who can access each API resource with what kind of access. You can use RBAC for both human (individual or group) and non-human users (service accounts) to define their types of access to various Kubernetes resources.  For example, there are three different environments, Dev, Staging, and Production, which have to be given access to the team, such as developers, DevOps, SREs, App owners, and product managers.

What Is Kubernetes RBAC? Often, when organizations start their Kubernetes journey, they look up to implementing least privilege roles and proper authorization to secure their infrastructure. That’s where Kubernetes RBAC is implemented to secure Kubernetes resources such as sensitive data, including deployment details, persistent storage settings, and secrets. Kubernetes RBAC provides the ability to control who can access each API resource with what kind of access. You can use RBAC for both human (individual or group) and non-human users (service accounts) to define their types of access to various Kubernetes resources.  For example, there are three different environments, Dev, Staging, and Production, which have to be given access to the team, such as developers, DevOps, SREs, App owners, and product managers.

A term you have probably heard a lot nowadays is continuous testing. Continuous testing explained simply, is about testing everywhere across the software development lifecycle and should include activities beyond automation, such as exploratory testing. Continuous testing implies that testing is not shifted but is found at every stage of the software development lifecycle. This is supported by the famous Continuous Testing in DevOps model created by Dan Ashby, which you can see in Figure 1.

All low-code platforms offer some or full code access, visibility, extensibility, and proprietariness. These vary significantly from vendor to vendor. Professional developers can realize their full potential on low-code platforms only with complete freedom to access and modify the code. Low-code development platforms have gained significant popularity recently, allowing users to create applications with minimal coding knowledge or experience. They abstract much of the complexity involved in traditional coding by providing pre-built components and visual interfaces.

In a constantly evolving enterprise landscape, integration remains the linchpin for seamless interactions between applications, data, and business processes. As Robert C. Martin aptly said, "A good architecture allows for major decisions to be deferred," emphasizing the need for Agile and adaptable integration strategies. The advent of cloud technologies has fundamentally reimagined how businesses approach integration. While traditional paradigms offer a foundational perspective, cloud-native integration patterns bring a transformative element to the table, reshaping the conventional wisdom around integrating modern business systems. The New Playground: Why Cloud-Native? Cloud-native architecture has become the new frontier for businesses looking to scale, adapt, and innovate in an increasingly interconnected world. But why is going cloud-native such a critical move? One primary reason is scalability. Traditional architectures, while robust, often face limitations in their ability to adapt to fluctuating demands. As Simon Wardley, a researcher in the field of innovation, once observed, "Historically, our approach to creating scalable, reliable systems required building bigger machines." But cloud-native architectures flip this script. They allow organizations to break free from the limitations of monolithic systems, embracing microservices and containers that scale horizontally.

In a constantly evolving enterprise landscape, integration remains the linchpin for seamless interactions between applications, data, and business processes. As Robert C. Martin aptly said, "A good architecture allows for major decisions to be deferred," emphasizing the need for Agile and adaptable integration strategies. The advent of cloud technologies has fundamentally reimagined how businesses approach integration. While traditional paradigms offer a foundational perspective, cloud-native integration patterns bring a transformative element to the table, reshaping the conventional wisdom around integrating modern business systems. The New Playground: Why Cloud-Native? Cloud-native architecture has become the new frontier for businesses looking to scale, adapt, and innovate in an increasingly interconnected world. But why is going cloud-native such a critical move? One primary reason is scalability. Traditional architectures, while robust, often face limitations in their ability to adapt to fluctuating demands. As Simon Wardley, a researcher in the field of innovation, once observed, "Historically, our approach to creating scalable, reliable systems required building bigger machines." But cloud-native architectures flip this script. They allow organizations to break free from the limitations of monolithic systems, embracing microservices and containers that scale horizontally.

Spark is one of the most popular and widely used big data processing frameworks in the world. It has a large open-source community, with continuous development, updates, and improvements being made to the platform. Spark has gained popularity due to its ability to perform in-memory data processing, which significantly accelerated the data processing times compared to traditional batch processing systems like Hadoop MapReduce. However, all that glitters is not gold. Spark is well known for being one of the best data processing frameworks available in the market, thanks to its capacity to process batch data, but when it comes to streaming data, Spark can be challenging if you don’t have previous experience working with any streaming framework.

In today's world, technology is evolving at a rapid pace. One of the advanced developments is edge computing. But what exactly is it? And why is it becoming so important? This article will explore edge computing and why it is considered the new frontier in international data science trends. Understanding Edge Computing Edge computing is a method where data processing happens closer to where it is generated rather than relying on a centralized data-processing warehouse. This means faster response times and less strain on network resources.

In an era where data is abundant, and information overload is a constant challenge, finding effective ways to distill and comprehend textual data has become increasingly important. Among the myriad of visualization techniques, word clouds have emerged as a powerful tool for representing and summarizing the essence of text-based information. In this article, we explore the concept of word clouds, their applications, and the benefits they offer in making sense of textual data.

Learn how to record SSH sessions on a Red Hat Enterprise Linux VSI in a Private VPC network using in-built packages. The VPC private network is provisioned through Terraform and the RHEL packages are installed using Ansible automation. What Is Session Recording and Why Is It Required? As noted in "Securely record SSH sessions on RHEL in a private VPC network," a Bastion host and a jump server are both security mechanisms used in network and server environments to control and enhance security when connecting to remote systems. They serve similar purposes but have some differences in their implementation and use cases. The Bastion host is placed in front of the private network to take SSH requests from public traffic and pass the request to the downstream machine. Bastion hosts and jump servers are vulnerable to intrusion as they are exposed to public traffic.

Kubernetes, the de-facto standard for container orchestration, supports two deployment options: imperative and declarative. Because they are more conducive to automation, declarative deployments are typically considered better than imperative. A declarative paradigm involves:

In part three of this series, we have seen how to deploy our Quarkus/Camel-based microservices in Minikube, which is one of the most commonly used Kubernetes local implementations. While such a local Kubernetes implementation is very practical for testing purposes, its single-node feature doesn't satisfy real production environment requirements. Hence, in order to check our microservices behavior in a production-like environment, we need a multi-node Kubernetes implementation. And one of the most common is OpenShift. What Is OpenShift? OpenShift is an open-source, enterprise-grade platform for container application development, deployment, and management based on Kubernetes. Developed by Red Hat as a component layer on top of a Kubernetes cluster, it comes both as a commercial product and a free platform or both as on-premise and cloud infrastructure. The figure below depicts this architecture.

In the world of business, a comparison of Snowflake and Data Bricks is important because it improves data analysis and business management. Organizations, companies, and businesses need a strategy to gather all the data in one place that is to be analyzed. Cloud-based data systems Snowflake and Data Bricks are industry leaders. However, it is important to understand which data platform is the best for your company.

This is an article from DZone's 2023 Automated Testing Trend Report.For more: Read the Report As per the reports of Global Market Insight, the automation testing market size surpassed $20 billion (USD) in 2022 and is projected to witness over 15% CAGR from 2023 to 2032. This can be attributed to the willingness of organizations to use sophisticated test automation techniques as part of the quality assurance operations (QAOps) process. By reducing the time required to automate functionalities, it accelerates the commercialization of software solutions. It also offers quick bug extermination and post-deployment debugging, and it helps the integrity of the software through early notifications of unforeseen changes.