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Free Download Udemy - Certification Course in Azure Data Engineering Published: 3/2025 MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Language: English | Duration: 14h 37m | Size: 6.67 GB Learn SQL for Data Engineering, Data Warehousing, Data Lake, Data Factory, Databricks, PySpark, Snowflakes and DevOps What you'll learn Learn SQL for Data Engineering, including querying and transforming data, optimizing database performance, and handling large datasets efficiently Gain expertise in Data Warehousing Concepts, including OLTP vs. OLAP, dimensional modeling, schema designs (Star & Snowflake), and ETL/ELT Learn about Azure Data Engineering Fundamentals, covering key Azure services such as Azure Data Lake Storage, Blob Storage, Synapse Analytics, and security Develop hands-on skills in Azure Data Factory (ADF) by building ETL pipelines, integrating data from various sources, and transforming data using Azure service Gain proficiency in Databricks and PySpark, including distributed computing, Spark SQL, RDDs, and performance optimization for handling big data Learn how to build and execute PySpark jobs for large-scale data processing and integrate Databricks with Azure services Understand Delta Tables and Versioning, including ACID transactions, schema enforcement, and time-travel capabilities Explore Snowflake for Data Engineering, covering architecture, data loading, query optimization, and integration with Azure Learn how to design and deploy Production Pipelines, following best practices for scalable pipeline architectures, exception handling, and monitoring Learn Azure DevOps for CI/CD pipeline deployment, version control, and automated testing Discover how to leverage Azure Data Engineering Analytics, including data analysis, visualization, and monitoring with Azure services. Requirements You should have an interest in the fundamentals of Azure Data Engineering. Basic understanding of programming and algorithms Description DescriptionTake the next step in your career! Whether you're an aspiring data engineer, an experienced IT professional, a cloud solutions architect, or a data analyst, this course is your opportunity to sharpen your Azure Data Engineering skills, enhance your ability to design scalable data solutions, and advance your professional growth in the field of cloud-based data engineering.With this course as your guide, you learn how to:Master the fundamental skills and concepts required for Azure Data Engineering, including SQL, Data Warehousing, ETL/ELT processes, and cloud-based data integration.Build and optimize data pipelines using Azure Data Factory (ADF), Databricks, Snowflake, PySpark, and Delta Tables, ensuring efficient data processing and transformation.Access industry-standard templates and best practices for data architecture, schema design, and performance optimization in cloud environments.Explore real-world applications of Azure services, including data lake storage, real-time analytics, data monitoring, and security best practices for enterprise-level data management.Invest in learning Azure Data Engineering today and gain the skills to design and manage scalable, high-performance data solutions that drive business success.The Frameworks of the CourseEngaging video lectures, case studies, projects, downloadable resources, and interactive exercises-this course is designed to explore Azure Data Engineering, covering SQL, Data Warehousing, ETL/ELT processes, and cloud-based data solutions using Azure services.The course includes multiple case studies, resources such as templates, worksheets, reading materials, quizzes, self-assessments, and hands-on labs to deepen your understanding of Azure Data Engineering concepts and real-world applications.In the first part of the course, you'll learn SQL basics and advanced techniques, data warehousing fundamentals, and data ingestion and transformation using Azure Data Factory (ADF) and Synapse Analytics.In the middle part of the course, you'll develop a deep understanding of Databricks and PySpark, Delta Tables, versioning, and real-time data streaming using Azure Event Hub and Stream Analytics.In the final part of the course, you'll gain expertise in Snowflake for Data Engineering, designing production pipelines, CI/CD implementation with Azure DevOps, and monitoring data workflows. Part 1Introduction and Study Plan· Introduction and know your instructor· Study Plan and Structure of the CourseModule 1. SQL Basics and Advanced Concepts1.1. Introduction to SQL1.1.1. Basics of relational databases and SQL.1.1.2. SQL syntax and query structure.1.1.3. SELECT, WHERE, GROUP BY, and ORDER BY clauses1.2. Advanced SQL techniques1.2.1. Joins (INNER, OUTER, LEFT, RIGHT).1.2.2. Subqueries, CTEs, and Window Functions.1.2.3. Aggregations and analytical functions.1.3. SQL for Data Engineering1.3.1. Data manipulation and transformation.1.3.2. Handling large datasets and performance tuning.1.3.3. Data ingestion and validation using SQL.Module 2. Data Warehousing Concepts2.1. Introduction to Data Warehousing2.1.1. OLTP vs. OLAP.2.1.2. Star and Snowflake schema designs.2.1.3. Dimensional modeling concepts.2.2. Data Pipeline Design2.2.1. ETL vs. ELT processes.2.2.2. Data staging, integration, and transformation layers.2.3. Hands-On Activity2.3.1. Creating sample schemas and loading sample data.Module 3. Azure Data Engineering Fundamentals3.1. Overview of Azure Data Engineering3.1.1. Introduction to Azure cloud platform.3.1.2. Key Azure services for Data Engineering.3.2. Azure Storage Solutions3.2.1. Azure Data Lake Storage.3.2.2. Blob storage and file management.3.2.3. Security and access control mechanisms.3.3. Azure Data Integration3.3.1. Introduction to Azure Synapse Analytics.3.3.2. Data movement and integration tools in Azure.Module 4. Azure Services for Data Engineering4.1. Azure Functions and Logic Apps4.1.1. Automating workflows using Logic Apps.4.1.2. Serverless computing with Azure Functions.4.2. Azure Event Hub and Stream Analytics4.2.1. Streaming data ingestion.4.2.2. Real-time analytics in Azure.4.3. Monitoring and Optimization4.3.1. Cost optimization techniques.4.3.2. Monitoring and debugging Azure workloadsModule 5. Azure Data Factory (ADF)5.1. Introduction to Azure Data Factory5.1.1. ADF architecture and components.5.1.2. Pipelines, triggers, and datasets.5.2. Building ETL Pipelines in ADF5.2.1. Creating and managing data pipelines.5.2.2. Data transformations using ADF.5.3. Integration with Other Services5.3.1. Integrating ADF with Databricks, SQL server, and Snowflake.5.4. Hands-On Activity5.4.1. Building a sample ETL pipeline in ADF.Module 6. Databricks and PySpark6.1. Introduction to Databricks6.1.1. Overview of Databricks and its architecture.6.1.2. Setting up Databricks workspaces.6.2. Introduction to PySpark6.2.1. Basics of distributed computing.6.2.2. Dataframes, RDDs, and Spark SQL.6.3. Advanced PySpark Techniques6.3.1. Writing and optimizing PySpark jobs.6.3.2. Working with large datasets.6.4. Hands-On Activities6.4.1. Building PySpark applications.6.4.2. Integrating Databricks with Azure services.Module 7. Delta Tables and Versioning7.1. Delta Lake Fundamentals7.1.1. Overview of Delta tables.7.1.2. ACID transactions and schema enforcement.7.2. Versioning and Time Travel7.2.1. Querying data at specific points in time.7.2.2. Implementing CDC (Change Data Capture) workflows.Module 8. Snowflake Core Concepts8.1. Introduction to Snowflake8.1.1. Architecture and key features of Snowflake.8.1.2. Warehouses, databases, and schema in Snowflake.8.2. Data Loading and Querying in Snowflake8.2.1. Copying data into Snowflake.8.2.2. Writing and optimizing queries.8.3. Snowflake for Data Engineering8.3.1. Integration with Azure services.8.3.2. Best practices for using Snowflake in production.Module 9. Production Pipelines and Deployment9.1. Designing Production Pipelines9.1.1. Best practices for scalable pipelines.9.1.2. Handling exceptions and retries.9.2. CI/CD for Azure Data Engineering9.2.1. Using Azure DevOps for pipeline deployment.9.2.2. Version control and automated testing.9.3. Monitoring and Maintenance9.3.1. Monitoring data pipelines in production.9.3.2. Troubleshooting and performance tuning.Part 2Module 10. Capstone Project10.1. Project Design and Implementation10.1.1. Design a complete Data Engineering solution.10.1.2. Use Azure services, Databricks, Snowflake, and PySpark. Who this course is for Data professionals looking to gain expertise in SQL, Data Warehousing, and ETL/ELT processes for efficient data management and transformation. New professionals seeking to build a career in Azure Data Engineering by learning cloud-based data solutions, data pipeline development, and big data processing using Azure services. Existing data engineers, architects, and IT professionals who want to enhance their skills in their respective domain to optimize data workflows and improve performance. Technical leads, managers, and decision-makers looking to understand scalable data engineering architectures, cloud-based data integration strategies, and real-time data analytics using Azure. 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We'll cover the necessary steps, potential pitfalls, and best practices to ensure a seamless integration. (Starts at 1 hour 38 minutes, completes at 2 hours 35 minutes)3. Configuring Multiple SCAN Names for High Availability: In this groundbreaking section, we'll show you how to set up multiple SCAN names to provide unparalleled high availability for your applications. This innovative approach, pioneered by our instructor, ensures uninterrupted connectivity even in the event of switch failures. We'll explore the concept of using multiple subnets, each with two switches for HA, to provide maximum redundancy and uptime.4. Important Note: Due to a technical glitch, the video freezes at 4 hours 15 minutes 50 seconds. To continue, please forward to 4 hours 20 minutes and then return to 4 hours 17 minutes to resume the recording.5. Changing SCAN VIPs and SCAN Name: In this section, we'll cover the process of modifying SCAN VIPs and SCAN names to adapt to changing infrastructure Requirements. We'll discuss the necessary steps, potential implications, and best practices to ensure a smooth transition. (Starts at 5 hours 46 minutes)6. Changing the Port Number for SCAN Listener: Here, we'll show you how to modify the port number for SCAN listeners to ensure seamless communication. We'll cover the necessary steps, potential pitfalls, and best practices to ensure a successful configuration. (Starts at 7 hours 8 minutes)7. Changing SCAN Listener and Node Listener Default Naming Convention: In this final section, we'll explore how to customize the naming convention for SCAN listeners and node listeners to suit your organization's needs. We'll discuss the necessary steps, potential implications, and best practices to ensure a successful configuration. (Starts at 7 hours 50 minutes)8. 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Learn to define an automation strategy, identify test cases suitable for automation, and conduct cost-benefit analysis for automation initiatives. Discover key test automation metrics, KPIs, and best practices for improving test execution performance. Learn techniques for handling test data and configuring test environments for seamless automation execution. Gain expertise in identifying root causes of test failures and implementing debugging strategies. Understand how AI and machine learning can enhance test automation, automate security and performance testing, and handle complex test scenarios. Requirements Willingness or Interest to learn about ISTQB (CTAL-TAE) for Successful Implementation. 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Participants will gain an understanding of essential framework components, including reusable libraries, utilities, and test data management strategies, enabling them to build robust automation solutions.Strategic planning for test automation is another critical area of focus. Learners will discover how to create a comprehensive test automation strategy, identify test cases suitable for automation, and perform cost-benefit analyses to determine the return on investment (ROI). Risk-based prioritization techniques are also introduced to help testers allocate automation efforts effectively.Managing test automation projects is essential for ensuring the success of automation initiatives. This course covers techniques for planning and estimating automation projects, defining and tracking key performance indicators (KPIs), and managing test data and environments. Participants will learn how to measure the effectiveness of their automation efforts and make data-driven improvements.The course also explores test automation reporting and monitoring, highlighting the importance of detailed test reports, continuous monitoring, and feedback loops in maintaining automation health. Debugging techniques and root cause analysis methods are covered to help engineers quickly identify and resolve test failures.To enhance test automation effectiveness, participants will learn strategies to improve test coverage, optimize test execution performance, and maintain automated test scripts over time. Best practices for script updates and maintenance will be discussed to ensure long-term sustainability.Finally, the course addresses advanced test automation concepts, including handling complex test scenarios, integrating AI and machine learning into automation frameworks, and automating security and performance testing. These advanced topics prepare learners for the evolving landscape of software testing and enable them to implement cutting-edge automation techniques.By the end of this course, participants will have a comprehensive understanding of test automation engineering, from foundational concepts to advanced strategies. They will be equipped with the skills needed to design scalable automation frameworks, optimize test execution, and contribute to high-quality software delivery through effective test automation practices.Thank you Who this course is for Software Testers and QA Engineers - Professionals looking to expand their knowledge of test automation and move beyond manual testing. Test Automation Engineers - Those who want to enhance their skills in designing scalable, maintainable, and efficient automation frameworks. Software Developers in Test (SDETs) - Developers who work on test automation and want to strengthen their understanding of automation frameworks and CI/CD integration. 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