Change Data Capture (CDC) in SQL Server is a powerful feature designed to track and capture changes made to data within a database. It provides a reliable and efficient way to identify alterations to tables, allowing for the extraction of valuable insights into data modifications over time. By enabling CDC with Azure Data Factory, SQL Server enables a systematic and automated approach to monitoring and capturing changes, facilitating better data management, auditing, and analysis within the database environment. Most Common Use-Cases: CDC With Azure Data Factory Common scenarios where the CDC with Azure Data Factory proves beneficial include:

In the fast-paced world of software development, efficiency is paramount. Automating repetitive tasks is key to achieving faster delivery cycles and improved quality. This is where Jenkins comes in — a free and open-source automation server that has become synonymous with continuous integration (CI) and continuous delivery (CD). Jenkins, the open-source automation powerhouse, plays a pivotal role in the DevOps world. But have you ever wondered how it all works under the hood? This blog delves into the intricate architecture of Jenkins, breaking down its core components and how they orchestrate the automation magic.

As the landscape of enterprise technology evolves, the marriage of Artificial Intelligence (AI) with SAP ABAP (Advanced Business Application Programming) is reshaping the way businesses approach software development within the SAP ecosystem. This article delves into the groundbreaking integration of AI with SAP ABAP programming, exploring how this fusion is revolutionizing SAP development processes. SAP ABAP and Its Legacy The Foundation of SAP Development SAP ABAP has long been the backbone of SAP development, providing a powerful and versatile language for customizing SAP applications. ABAP's capabilities have driven the customization of SAP systems to meet specific business requirements.

NCache Java Edition with distributed cache technique is a powerful tool that helps Java applications run faster, handle more users, and be more reliable. In today's world, where people expect apps to work quickly and without any problems, knowing how to use NCache Java Edition is very important. It's a key piece of technology for both developers and businesses who want to make sure their apps can give users fast access to data and a smooth experience. This makes NCache Java Edition an important part of making great apps. This article is made especially for beginners to make the ideas and steps of adding NCache to your Java applications clear and easy to understand. It doesn't matter if you've been developing for years or if you're new to caching, this article will help you get a good start with NCache Java Edition. Let’s start with a step-by-step process to set up a development workstation for NCache with the Java setup.

NCache Java Edition with distributed cache technique is a powerful tool that helps Java applications run faster, handle more users, and be more reliable. In today's world, where people expect apps to work quickly and without any problems, knowing how to use NCache Java Edition is very important. It's a key piece of technology for both developers and businesses who want to make sure their apps can give users fast access to data and a smooth experience. This makes NCache Java Edition an important part of making great apps. This article is made especially for beginners to make the ideas and steps of adding NCache to your Java applications clear and easy to understand. It doesn't matter if you've been developing for years or if you're new to caching, this article will help you get a good start with NCache Java Edition. Let’s start with a step-by-step process to set up a development workstation for NCache with the Java setup.

In today's digital age, cloud-hosted applications frequently use storage solutions like AWS S3 or Azure Blob Storage for images, documents, and more. Public URLs allow direct access to publicly accessible resources.  However, sensitive images require protection and are not readily accessible via public URLs. Accessing such an image involves a JWT-protected API endpoint, which returns the needed image. We must pass the JWT token in the header to fetch the image using the GET API. The standard method for rendering these images in HTML uses JavaScript, which binds the byte content from the API to the img src attribute. Though straightforward, this approach might not always be suitable, especially when avoiding JavaScript execution.

Your version control system, like Git, is a primary vector for secret sprawl, unintentional source poisoning, and intentional source poisoning. In a shift left model, there are degrees of leftness. The most left you can get is to test all the code before the developer tries to commit anything and train them thoroughly in the best practices. But when we rely on people to remember to do things consistently and correctly, we're cutting holes in the safety net. We need mechanisms. At Amazon, they have a saying: "Good intentions don't work. Mechanisms do." Humans can feel fatigued, rushed, distracted, or otherwise encumbered, and despite all intentions to follow best practices, they don't. When you automate enforcement of best practices, you can ensure those practices are followed in a much more consistent and correct fashion.

Good software engineering teams commit frequently and deploy frequently. Those are some of the main ideas behind continuous integration (CI) and continuous deployment (CD). Gone are the days of quarterly or yearly releases and long-lived feature branches! Today, we’ll show you how you can deploy your Heroku app automatically any time code is merged into your main branch by using GitLab CI/CD.

Good software engineering teams commit frequently and deploy frequently. Those are some of the main ideas behind continuous integration (CI) and continuous deployment (CD). Gone are the days of quarterly or yearly releases and long-lived feature branches! Today, we’ll show you how you can deploy your Heroku app automatically any time code is merged into your main branch by using GitLab CI/CD.

In today's data-driven world, efficient data processing is paramount for organizations seeking insights and making informed decisions. Google Cloud Platform (GCP) offers powerful tools such as Apache Airflow and BigQuery for streamlining data processing workflows. In this guide, we'll explore how to leverage these tools to create robust and scalable data pipelines. Setting up Apache Airflow on Google Cloud Platform Apache Airflow, an open-source platform, orchestrates intricate workflows. It allows developers to define, schedule, and monitor workflows using Directed Acyclic Graphs (DAGs), providing flexibility and scalability for data processing tasks. Setting up Airflow on GCP is straightforward using managed services like Cloud Composer. Follow these steps to get started:

In today's digital landscape, securing sensitive data has become more critical than ever. With cyber threats on the rise, organizations need robust solutions to protect their valuable information. This is where Key Management Systems (KMS) and Hardware Security Modules (HSM) play a role. These cryptographic solutions offer a secure and efficient way to manage keys and protect data.  In this article, we will explore the world of secure key management, delve into the intricacies of KMS and HSM, discuss their benefits, use cases, key considerations, and best practices, and provide insights into choosing the right solution as well as implementing it seamlessly into your existing infrastructure. Understanding and implementing these technologies helps developers safeguard their cloud applications against unauthorized access and data breaches. This not only helps in maintaining the integrity and confidentiality of data but also enhances the overall security posture of cloud infrastructure.

In the age of Big Data and Artificial Intelligence (AI), effectively managing and deploying machine learning (ML) models is essential for businesses aiming to leverage data-driven insights. PostgresML, a pioneering framework, seamlessly integrates ML model deployment directly into PostgreSQL, a widely used open-source relational database management system. This integration facilitates the effortless deployment and execution of ML models within the database environment, eliminating the need for intricate data pipelines and external services. Introduction Artificial Intelligence (AI) and Machine Learning (ML) have emerged as transformative technologies, enabling systems to learn from data, adapt to new inputs, and perform tasks without explicit programming. At the core of AI and ML are models, mathematical representations of patterns and relationships within data, which are trained to make predictions, classify data, or generate insights. However, the journey from model development to deployment poses unique challenges. Model deployment involves integrating trained models into operational systems or applications, allowing them to make real-time decisions and drive business value. Yet, this process is not without complexities.

In a digitally-driven world where organizations are entrusted with increasing volumes of sensitive data, establishing trust and credibility is non-negotiable. Regular auditing and accountability play pivotal roles in achieving these goals. An audit is like a comprehensive health check that ensures all systems are secure and in compliance with regulations. This chapter will discuss the intricacies of audits, with a focus on System and Organization Controls (SOC) audits, and why they are instrumental for cloud data security. Understanding System and Organization Controls (SOC) Audits SOC audits are formal reviews of how a company manages data, focusing on the security, availability, processing integrity, confidentiality, and privacy of a system. Considered a gold standard for measuring data handling, SOC reports demonstrate to clients and stakeholders that your organization takes security seriously.

Understanding KVM Kernel-based Virtual Machine (KVM) stands out as a virtualization technology in the world of Linux. It allows physical servers to serve as hypervisor hosting machines (VMs). Embedded within the Linux kernel, KVM empowers the creation of VMs with their virtualized hardware components, such as CPUs, memory, storage, and network cards, essentially mimicking a machine. This deep integration into the Linux kernel brings KVM's performance, security, and stability advantages, making it a dependable option for virtualization requirements. KVM functions as a type 1 hypervisor, delivering performance similar to hardware—an edge over type 2 hypervisors. Its scalability is another feature; it can dynamically adapt to support an increasing number of VMs, facilitating the implementation of cloud infrastructures. Security remains paramount for KVM due to testing and security updates from the open-source community. Additionally, its standing development history since 2006 ensures a stable virtualization platform.

To navigate the digital landscape safely, organizations must prioritize building robust cloud infrastructures, and sanctuaries for their valuable data. The foundation of a secure cloud architecture requires steadfast principles and guiding decisions like invisible forces that form a resilient structure. Here we explore the key tenets for building a secure environment within the cloud. Least Privilege The concept of 'Least Privilege' dictates that a person or system should have the minimal level of access or permissions needed to perform their role. This security measure is akin to compartmentalization, limiting the spread of damage should a breach occur.

Fargate is a serverless compute engine for containers that works with both Amazon ECS and Amazon EKS. With AWS Fargate, we can run applications without managing servers (official information page). In this post, we will take a step-by-step approach to deploying and running a .NET Core Web API application on AWS Fargate Service.

Fargate is a serverless compute engine for containers that works with both Amazon ECS and Amazon EKS. With AWS Fargate, we can run applications without managing servers (official information page). In this post, we will take a step-by-step approach to deploying and running a .NET Core Web API application on AWS Fargate Service.

Within the software development community, there’s no denying the importance of unit testing. We all understand the need to isolate code for testing and quality assurance; it’s an unquestionable necessity in writing code. But how can we be sure that the code we deploy is as good as it can possibly be? The answer is: static code analysis. Too often, businesses choose not to prioritize static analysis — which ultimately impacts the quality of their software. The truth is that we can’t afford to sidestep this part of the CI/CD development pipeline if we want to create the best possible software that helps a business compete and win in their market.

Cyber threats are growing more sophisticated, frequent, and damaging, with the average cost of a data breach now reaching $4.24 million, according to IBM’s 2021 report. Clearly, organizations need more robust cybersecurity protections in place, which is leading many to adopt a zero-trust architecture approach.  Zero-trust flips conventional security on its head by shifting from an implicit trust model to one where verification is required every step of the way. No users, devices, or workloads are inherently trusted — authentication and authorization are rigorously enforced at all times. This assumes that breaches will occur and limits lateral movement and access once threat actors break through the external perimeter. 

What Are Virtual Network Functions (VNFs)? Previously, proprietary hardware performed functions like routers, firewalls, load balancers, etc. In IBM Cloud, we have proprietary hardware like the FortiGate firewall that resides inside IBM Cloud data centers today. These hardware functions are packaged as virtual machine images in a VNF. VNFs are virtualized network services that are packaged as virtual machines (VMs) on commodity hardware. It allows service providers to run their networks on standard servers instead of proprietary ones. Some of the common VNFs include virtualized routers, firewalls, load balancers, WAN optimization, security, and other edge services. In a cloud service provider like IBM, a user can spin up these VNF images in a standard virtual server instead of proprietary hardware.    

What Are Virtual Network Functions (VNFs)? Previously, proprietary hardware performed functions like routers, firewalls, load balancers, etc. In IBM Cloud, we have proprietary hardware like the FortiGate firewall that resides inside IBM Cloud data centers today. These hardware functions are packaged as virtual machine images in a VNF. VNFs are virtualized network services that are packaged as virtual machines (VMs) on commodity hardware. It allows service providers to run their networks on standard servers instead of proprietary ones. Some of the common VNFs include virtualized routers, firewalls, load balancers, WAN optimization, security, and other edge services. In a cloud service provider like IBM, a user can spin up these VNF images in a standard virtual server instead of proprietary hardware.    

Recently, I mentioned how I refactored the script that kept my GitHub profile up-to-date. Since Geecon Prague, I'm also a happy owner of a Raspberry Pi: Though the current setup works flawlessly — and is free, I wanted to experiment with self-hosted runners. Here are my findings.

Cilium is an eBPF-based project that was originally created by Isovalent, open-sourced in 2015, and has become the center of gravity for cloud-native networking and security. With 700 active contributors and more than 18,000 GitHub stars, Cilium is the second most active project in the CNCF (behind only Kubernetes), where in Q4 2023 it became the first project to graduate in the cloud-native networking category. A week ahead of the KubeCon EU event where Cilium and the recent 1.15 release are expected to be among the most popular topics with attendees, I caught up with Nico Vibert, Senior Staff Technical Engineer at Isovalent, to learn more about why this is just the beginning for the Cilium project. Q:  Cilium recently became the first CNCF graduating “cloud native networking” project — why do you think Cilium was the right project at the right time in terms of the next-generation networking requirements of cloud-native?

Cilium is an eBPF-based project that was originally created by Isovalent, open-sourced in 2015, and has become the center of gravity for cloud-native networking and security. With 700 active contributors and more than 18,000 GitHub stars, Cilium is the second most active project in the CNCF (behind only Kubernetes), where in Q4 2023 it became the first project to graduate in the cloud-native networking category. A week ahead of the KubeCon EU event where Cilium and the recent 1.15 release are expected to be among the most popular topics with attendees, I caught up with Nico Vibert, Senior Staff Technical Engineer at Isovalent, to learn more about why this is just the beginning for the Cilium project. Q:  Cilium recently became the first CNCF graduating “cloud native networking” project — why do you think Cilium was the right project at the right time in terms of the next-generation networking requirements of cloud-native?

In the ever-evolving world of software development, efficiency and clarity in managing complex systems have become paramount. Kubernetes, the de facto orchestrator for containerized applications, brings its own set of challenges, especially when dealing with the vast amounts of JSON-formatted data it generates. Here, jq, a lightweight and powerful command-line JSON processor, emerges as a vital tool in a DevOps professional's arsenal. This comprehensive guide explores how to leverage jq to simplify, process, and analyze Kubernetes data, enhancing both productivity and insight. Understanding jq and Kubernetes Before diving into the integration of jq with Kubernetes, it's essential to grasp the basics. jq is a tool designed to transform, filter, map, and manipulate JSON data with ease. Kubernetes, on the other hand, manages containerized applications across a cluster of machines, producing and utilizing JSON outputs extensively through its API and command-line tools like kubectl.

In the ever-evolving world of software development, efficiency and clarity in managing complex systems have become paramount. Kubernetes, the de facto orchestrator for containerized applications, brings its own set of challenges, especially when dealing with the vast amounts of JSON-formatted data it generates. Here, jq, a lightweight and powerful command-line JSON processor, emerges as a vital tool in a DevOps professional's arsenal. This comprehensive guide explores how to leverage jq to simplify, process, and analyze Kubernetes data, enhancing both productivity and insight. Understanding jq and Kubernetes Before diving into the integration of jq with Kubernetes, it's essential to grasp the basics. jq is a tool designed to transform, filter, map, and manipulate JSON data with ease. Kubernetes, on the other hand, manages containerized applications across a cluster of machines, producing and utilizing JSON outputs extensively through its API and command-line tools like kubectl.

As we advance into 2024, the landscape of DevOps is undergoing a transformative shift. Emerging technologies, evolving methodologies, and changing business needs are redefining what it means to implement DevOps practices effectively. This article explores DevOps's key trends and adaptations as we navigate this digital technology transition.  Emerging Trends in DevOps AI and ML Integration The integration of artificial intelligence (AI) and machine learning (ML) within DevOps processes is no longer a novelty but a necessity. AI-driven analytics and ML algorithms are revolutionizing how we approach automation, problem-solving, and predictive analysis in DevOps.

In today's discourse, we delve into the intricacies of accessing Snowflake via the Trino project. This article illuminates the seamless integration of Trino with Snowflake, offering a comprehensive analysis of its benefits and implications. Previous Articles Previous articles on Snowflake and Trino:

In today's discourse, we delve into the intricacies of accessing Snowflake via the Trino project. This article illuminates the seamless integration of Trino with Snowflake, offering a comprehensive analysis of its benefits and implications. Previous Articles Previous articles on Snowflake and Trino:

Incorporating AI into API and microservice architecture design for the Cloud can bring numerous benefits. Here are some key aspects where AI can drive improvements in architecture design: Intelligent planning: AI can assist in designing the architecture by analyzing requirements, performance metrics, and best practices to recommend optimal structures for APIs and microservices. Automated scaling: AI can monitor usage patterns and automatically scale microservices to meet varying demands, ensuring efficient resource utilization and cost-effectiveness. Dynamic load balancing: AI algorithms can dynamically balance incoming requests across multiple microservices based on real-time traffic patterns, optimizing performance and reliability. Predictive analytics: AI can leverage historical data to predict usage trends, identify potential bottlenecks, and offer proactive solutions for enhancing the scalability and reliability of APIs and microservices. Continuous optimization: AI can continuously analyze performance metrics, user feedback, and system data to suggest improvements for the architecture design, leading to enhanced efficiency and user satisfaction. By integrating AI-driven capabilities into API and microservice architecture design on Azure, organizations can achieve greater agility, scalability, and intelligence in managing their cloud-based applications effectively. 

Incorporating AI into API and microservice architecture design for the Cloud can bring numerous benefits. Here are some key aspects where AI can drive improvements in architecture design: Intelligent planning: AI can assist in designing the architecture by analyzing requirements, performance metrics, and best practices to recommend optimal structures for APIs and microservices. Automated scaling: AI can monitor usage patterns and automatically scale microservices to meet varying demands, ensuring efficient resource utilization and cost-effectiveness. Dynamic load balancing: AI algorithms can dynamically balance incoming requests across multiple microservices based on real-time traffic patterns, optimizing performance and reliability. Predictive analytics: AI can leverage historical data to predict usage trends, identify potential bottlenecks, and offer proactive solutions for enhancing the scalability and reliability of APIs and microservices. Continuous optimization: AI can continuously analyze performance metrics, user feedback, and system data to suggest improvements for the architecture design, leading to enhanced efficiency and user satisfaction. By integrating AI-driven capabilities into API and microservice architecture design on Azure, organizations can achieve greater agility, scalability, and intelligence in managing their cloud-based applications effectively. 

In the retail industry, the security of customer data, transaction records, and inventory information is paramount. As many retail stores migrate their databases to the cloud, ensuring the security of these data repositories becomes crucial. Amazon Web Services (AWS) Relational Database Service (RDS) for SQL Server offers a powerful platform for hosting retail databases with built-in security features designed to protect sensitive information. This article provides a detailed guide on securing AWS RDS SQL Server instances, tailored for retail stores, with practical setup examples. Understanding the Importance of Database Security in Retail Before delving into the specifics of securing an RDS SQL Server instance, it's essential to understand why database security is critical for retail stores. Retail databases contain sensitive customer information, including names, addresses, payment details, and purchase history. A breach could lead to significant financial loss, damage to reputation, and legal consequences. Therefore, implementing robust security measures is not just about protecting data but also about safeguarding the business's integrity and customer trust.

With technology and data growing at an unprecedented pace, cloud computing has become a no-brainer answer for enterprises worldwide to foster growth and innovation. As we swiftly move towards the second quarter of 2024, predictions by cloud security reports highlight the challenges of cloud adoption in the cloud security landscape. Challenges Gartner Research forecasts a paradigm shift in adopting public cloud Infrastructure as a Service (IaaS) offerings. By 2025, a staggering 80% of enterprises are expected to embrace multiple public cloud IaaS solutions, including various Kubernetes (K8s) offerings. This growing reliance on cloud infrastructure raises the critical issue of security, which the Cloud Security Alliance painfully highlights. 

Terraform, the leading IaC (Infrastructure as Code orchestrator), was created 9 years ago by HashiCorp and is considered today as the de facto tool for managing cloud infrastructure with code. What started as an open-source tool quickly became one of the largest software communities in the world, and for every problem you may encounter, someone has already found and published a solution. At the end of the day, DevOps managers are looking for a simple, predictable, drama-free way to manage their infrastructure, and this is probably why many have chosen Terraform, which is a well-known, well-established tool with a very large community.

Terraform, the leading IaC (Infrastructure as Code orchestrator), was created 9 years ago by HashiCorp and is considered today as the de facto tool for managing cloud infrastructure with code. What started as an open-source tool quickly became one of the largest software communities in the world, and for every problem you may encounter, someone has already found and published a solution. At the end of the day, DevOps managers are looking for a simple, predictable, drama-free way to manage their infrastructure, and this is probably why many have chosen Terraform, which is a well-known, well-established tool with a very large community.

Are you ready to get started with cloud-native observability with telemetry pipelines? This article is part of a series exploring a workshop guiding you through the open source project Fluent Bit, what it is, a basic installation, and setting up the first telemetry pipeline project. Learn how to manage your cloud-native data from source to destination using the telemetry pipeline phases covering collection, aggregation, transformation, and forwarding from any source to any destination.

In today's fast-paced world of technology, efficient application deployment and management are crucial. Kubernetes, a game-changing platform for container orchestration, is at the forefront of this evolution. At Atmosly, we leverage Kubernetes to empower organizations in navigating the rapidly evolving digital landscape, offering solutions that intertwine with Kubernetes' strengths to enhance your technological capabilities. What Is Kubernetes? Kubernetes, or K8s, is a revolutionary container orchestration system born at Google. It has become a cornerstone of contemporary IT infrastructure, providing robust solutions for deploying, scaling, and managing containerized applications. At Atmosly, we integrate Kubernetes into our offerings, ensuring our clients benefit from its scalable, reliable, and efficient nature.

The technology landscape has long been a domain of intense innovation and dynamic change. Yet, one of the most significant changes in recent years is the increasing visibility and impact of women in tech, especially in fields like DevOps and platform engineering. Names like Nicole Forsgren, Julia Evans, Bridget Kromhout, Nora Jones, and Dora Korpar, among others, have become synonymous with excellence and innovation in these domains. The Trailblazers In the world of DevOps and platform engineering, there are inspiring stories of female leaders like Nicole Forsgren, who have not only broken through gender barriers but have also excelled in their roles. 

The continuous integration/continuous delivery (CI/CD) pipeline represents the steps new software goes through before release. However, it can contain numerous vulnerabilities for hackers to exploit. 1. Vulnerabilities in the Code Many software releases get completed on such tight time frames that developers don’t have enough time to ensure the code is secure. Company leaders know frequent software updates tend to keep customers happy and can give people the impression that a business is on the cutting edge of technology. However, rushing new releases can have disastrous consequences that give hackers easy entry for wreaking havoc.

The technology landscape is evolving at a breakneck pace. Organizations must navigate this dynamic environment to meet changing customer expectations and business challenges. This requires a fundamental shift in how digital products are envisioned, built, and managed. In this light, the integration of product management and DevOps has emerged as a pivotal trend. This fusion promises to accelerate innovation cycles, enhance customer centricity, and equip teams with the agility needed to thrive in the digital age. In this article, we analyze the depth and breadth of this integration. We explore the distinct roles of product management and DevOps, the synergies unlocked by bringing them together, proven strategies for implementation, and what the future holds for this fused function.