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Azure Data Factory Deployment Essentials - A DevOps Approach - Printable Version

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Azure Data Factory Deployment Essentials - A DevOps Approach - OneDDL - 12-13-2023

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Free Download Azure Data Factory Deployment Essentials - A DevOps Approach
Published 12/2023
Created by Step2C Education
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English | Duration: 34 Lectures ( 2h 44m ) | Size: 1.6 GB

Hands-On Deployment: Mastering Azure Data Factory, Azure Data Lake, and Azure SQL DB with DevOps Practices
What you'll learn
Continuous Integration (CI) for Data Pipelines
Version Control for Data Projects
Introduction to Azure DevOps
Creating Your First Azure Data Factory Project
Introduction to Azure Data Factory Components
Provision Azure Data Lake Storage for effective data management
Requirements
Internet connections
mobile / laptop / pc
Headphone
Description
Unlock the full potential of Azure Data Factory by mastering the art of deployment with a hands-on, practical approach. In this comprehensive course, you'll delve into the intricacies of data orchestration, ETL, and deployment using Azure Data Factory. From setting up your infrastructure to implementing advanced deployment strategies, this course covers it all.Key Highlights:Infrastructure Setup: Learn to configure Azure Data Lake, Azure SQL Database, and establish the groundwork for your data projects.Azure Data Factory Basics: Understand the components of Azure Data Factory, create pipelines, and explore data movement and transformation techniques.DevOps Integration: Dive into the world of DevOps and discover how to leverage Azure DevOps for version control, continuous integration, and continuous deployment in data projects.CI/CD for Data Pipelines: Automate your build and deployment processes, ensuring seamless integration and efficient management of your data pipelines.Advanced Deployment Scenarios: Explore parameterization, dynamic configurations, monitoring, and logging strategies for robust and scalable data pipelines.Real-World Case Studies: Gain insights from real-world projects, including large-scale data migration and real-time data integration, and apply learnings to your own deployments.Troubleshooting and Best Practices: Learn to debug issues, implement best practices, and optimize your data pipelines for performance.Future Trends and Updates: Stay ahead of the curve by exploring emerging trends in data integration and understanding how to adapt to future updates in Azure.Who Should EnrollBig Grinata engineers, analysts, and professionals seeking a comprehensive understanding of Azure Data Factory deployment using DevOps practices. Whether you're a beginner or an experienced user, this course caters to all levels, providing actionable insights and practical skills for successful data project deployment.
Who this course is for
Data Engineers and Analysts: Individuals working with data integration, ETL processes, and data orchestration who want to enhance their skills using Azure Data Factory and implement DevOps practices in their workflows.
Azure Enthusiasts: Professionals interested in harnessing the power of Azure services specifically for data engineering, gaining practical experience with Azure Data Factory, and understanding how to seamlessly deploy data solutions in the cloud.
DevOps Practitioners: Those seeking to integrate DevOps principles into the data integration landscape, automate workflows, and optimize collaboration between data and IT teams.
IT Professionals and Architects: Professionals responsible for designing, implementing, or overseeing data projects in Azure who want to stay updated on best practices, emerging trends, and advanced deployment strategies.
Students and Aspiring Data Professionals: Individuals pursuing a career in data engineering or related fields who want to build a strong foundation in Azure Data Factory and DevOps to stand out in the job market.
Business Intelligence Professionals: BI developers and professionals interested in extending their knowledge to include end-to-end data engineering processes, from data ingestion to deployment.
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