AWS Certified Data Engineer Associate DEA-C01 Course 2026

This course contains the use of artificial intelligence.KQL for Cybersecurity, is a meticulously structured Udemy course aimed at IT professionals seeking to master KQL for cybersecurity purposes. This course systematically walks you through the initial setup to advanced implementation with real-world applications.Key Benefits for you:SOC Basics: Establish a strong foundation with an overview of core concepts for a Security Operations CentersAzure Basics: Familiarize yourself with essential Azure services and configurations relevant to integrating Microsoft Defender XDR into cloud environments.Microsoft Security Basics: Gain insight into Microsoft's security ecosystem, including tools, best practices, and zero trust for safeguarding digital assets.The Kusto Query Language (KQL): Learn the fundamentals of KQL, a powerful query language designed for fast and efficient data exploration in cybersecurity.KQL Operators - Fundamentals: Master essential KQL operators, including filtering, sorting, and aggregation, to analyze security data effectively.KQL Operators - Joins and Union: Understand how to use Joins and Union in KQL to correlate data from multiple sources for comprehensive threat analysis.KQL in Microsoft Security Copilot: Explore how KQL integrates with Microsoft Security Copilot to enhance automated threat detection and response.KQL in Microsoft Defender for Cloud: Discover how to use KQL in Microsoft Defender for Cloud to monitor and secure cloud workloads.KQL for Microsoft Sentinel: Learn how KQL powers Microsoft Sentinel’s SIEM capabilities, enabling advanced threat hunting and security analytics.KQL in Microsoft Defender XDR: Utilize KQL in Microsoft Defender XDR to detect, investigate, and respond to sophisticated cyber threats.KQL Quer

Welcome to "GitHub Copilot for Seniors: Codify Architectural Standards"! This course is a practical guide to evolving GitHub Copilot from a basic coding assistant into a high-level engineering partner—a true "second self" for your development workflow.Course Methodology: From Basics to Architectural ScaffoldingThe curriculum is structured to guide the transition from basic AI interaction to full-stack automated engineering:Phase 1: Foundations of AI-Assisted EngineeringThe course starts with the essentials, but it skips the fluff. It focuses on context injection—the technique of curating the specific data, files, and documentation that Copilot requires to generate accurate, production-ready code. Lessons cover how to treat Copilot as a functional extension of the IDE rather than a basic chatbot.Phase 2: Defining Coding StandardsTrue mastery is achieved when the AI generates code according to rigid project requirements. The course teaches the use of Custom Instructions to codify architectural preferences—from Clean Architecture patterns to specific CSS methodologies—ensuring every generated snippet adheres to professional standards.Phase 3: The Scaffolding Engine (Standalone Prompts)This represents the core of the curriculum. It moves beyond simple chat queries to implement a reusable library of high-level commands. Through the use of custom standalone prompts—such as /add-form, /add-feature, /add-feature-backend, and /add-api-endpoint—the following capabilities are mastered:Feature Scaffolding: Generate entire features across front-end and back-end environments simultaneously.Type-Safe Logic: Create code perfectly adapted to spec

Preparing for the SPLK-1001: Splunk Core Certified User exam with a structured set of practice tests designed to strengthen your understanding of Splunk fundamentals. This course focuses on building confidence through repeated exposure to concepts that commonly challenge learners, while offering clear, explanation-based feedback for every answer.These practice tests are developed to help you refine your skills in key areas such as Splunk interface navigation, search fundamentals, field usage, time-based filtering, and working with alerts, reports, dashboards, and lookups. The goal is to ensure you not only answer questions correctly but also understand the reasoning behind each solution.What You Can ExpectInside this course, you will find a complete series of practice exams that cover the core domains of the SPLK-1001 exam. Each test includes a balanced variety of question styles aimed at strengthening conceptual clarity and practical thinking. Every answer is accompanied by an explanation to help you identify knowledge gaps, revisit essential concepts, and steadily improve with each attempt.Practice Test Structure :_ Total_720+ qnsTo support different levels of preparation, the course offers multiple test formats:2 Standard Tests with 60 questions each (similar to typical practice test length)2 Expanded Tests with 120 questions each (roughly 2X the standard size)2 Giant Tests with 180 questions each (3X the ordinary practice length)This progression allows you to begin with manageable sets and gradually work through more extensive question banks as your confidence grows.Key Domains CoveredThese practice tests reinforce understanding across the most important Splunk Core User areas, including:Splunk basics, interface, and navigation

Welcome to SonarQube, SonarCloud, SonarLint Tutorials with ProjectsSonarQube, SonarCloud, SonarLint Tutorials with Projects and Integration of SonarQube with CI Tools like GitHub Actions, Jenkins and GitLabSection 1:SonarQube Introduction | What is SonaQube | SonarQube ComponentsSonarQube Introduction | How does SonarQube Work | Components of SonarQubeSection 2:SonarQube Installation on Ubuntu, CentOS and Windows OSSonarQube Installation Prerequisites | Hardware and Software RequirementsHow to install SonarQube 9.9 on Ubuntu 22.04 LTSHow to Install SonarQube on Windows OSSection 3:SonarScanner Tutorial | How to use SonarScanner with Maven, Gradle and .NETIntegrate SonarScanner for Maven ProjectsHow to Integrate SonarScanner for Gradle ProjectHow to Run SonarScanner CLI from the zip fileSection 4:Integrate SonarQube with GitHub ActionsIntegrate SonarQube for Java Gradle Project using GitHub ActionsIntegrate SonarQube for Node.js Project with GithHub ActionsSonarQube code analysis with gthub action for maven projectSection 5:Integrate SonarQube

This course is updated to include new services, including generative AI, Gemini on Vertex AI, Cloud Workstations, Gemini Code Assist, observability, and more! I also added over 90 minutes of new content to cover the latest Google Cloud services.Are you brand new to Google Cloud Platform (GCP)? Do you need to learn the basics of key GCP services? If so, this course is for you!Hi, I am Janakiram MSV, a Google Certified Professional Cloud Architect and also a Google Developer Expert for Cloud and IoT technologies. With 15+ years of experience working on Cloud Computing at Microsoft and Amazon Web Services, I can assure you that this course is the perfect resource to jumpstart your GCP journey. GCP is one of the fastest-growing cloud platforms in the industry. This course aims to provide a thorough overview of GCP. From the core building blocks, such as Compute, Storage, and Networking, to the advanced services, this course introduces the key concepts and then shows you how to start being productive. Each section includes a hands-on demo of one of the key services. You will also learn the use cases and scenarios for some of the most significant services of Google Cloud.This course is carefully designed to help beginners get started with GCP. Topics covered include:The Big Picture of GCPEssential building blocksComputeStorageNetworkIdentity ManagementAdditional ServicesDatabasesData and AnalyticsArtificial Intelligence Machine LearningDeveloper ToolsManagement OperationsEnterprise ServicesJoin me in this course if you're ready to dive into GCP. You won't be disappointed!

Azure Data Factory is a cloud-based data integration service that allows organizations to create, schedule, and orchestrate data workflows. It enables seamless movement and transformation of data from various sources to destinations, supporting both on-premises and cloud-based environments. Its serverless nature removes the need for infrastructure management, making it scalable and cost-effective for handling large volumes of data.The architecture of Azure Data Factory consists of pipelines, datasets, linked services, and activities. Pipelines define a workflow of activities, datasets represent the data structures, linked services provide connectivity to data sources, and activities perform actions such as data movement or transformation. This modular design allows for flexible and reusable data integration solutions.Data integration in Azure Data Factory supports over 90 built-in connectors, including SQL databases, SaaS applications, REST APIs, and file systems. It enables organizations to unify diverse data sources into a centralized repository for analytics, reporting, and business intelligence purposes. The platform supports both batch and real-time data ingestion.ETL (Extract, Transform, Load) and data flow features in Azure Data Factory allow for complex data transformations. Users can create mapping data flows with a visual interface to perform operations such as joins, aggregations, and data cleansing without writing code. These capabilities streamline the process of preparing data for analytics and machine learning workflows.Monitoring and management of pipelines are facilitated through the Azure portal, where users can track activity runs, check performance metrics, and set up alerts for failures. Integration with Azure Monitor and Log Analytics enhances observability, enabling proactive troubleshooting and ensuring reliability of data processes.Security and compliance in Azure Data Factory are addressed through data encryption at rest and in transit, managed ident