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Azure Data Lake

Track :

Programming

Lessons no : 46

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What will you learn in this course?
  • Master Azure Data Lake architecture for scalable big data storage and processing techniques
  • Implement data ingestion, transformation, and management strategies using Azure Data Lake tools and services
  • Configure security, access controls, and data governance to ensure compliance and data protection in Azure Data Lake
  • Optimize data storage and retrieval for performance and cost-efficiency in large-scale data environments
  • Integrate Azure Data Lake with data warehouses, operational stores, and analytics platforms for seamless data workflows
  • Utilize Azure Data Lake analytics and processing frameworks for real-time and batch data analysis
  • Design and deploy data pipelines for structured, semi-structured, and unstructured data management
  • Apply best practices for data cataloging, metadata management, and data lifecycle in Azure Data Lake
  • Leverage Azure Data Lake security features, including encryption, identity management, and role-based access control
  • Develop scalable data solutions by combining Azure Data Lake with Azure Data Factory and Azure Databricks
  • Troubleshoot common issues and monitor performance to ensure reliable data lake operations
  • Create comprehensive documentation and governance policies for enterprise-wide Azure Data Lake deployment

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Lessons | 46
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Duy Nguyễn

Good Course
2025-10-27

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grapping good knowledge
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Certainly a good way to learn digitally. Thank you team mindluster.
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good teaching and explanation
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Very good and very excellent course. i am very happy about this course and mind luster
2023-10-22

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Azure Data Lake is a cloud platform designed to support big data analytics. It provides unlimited storage for structured, semi-structured or unstructured data. It can be used to store any type of data of any size.Azure Data Lake works with existing IT investments for identity, management, and security for simplified data management and governance. It also integrates seamlessly with operational stores and data warehouses so you can extend current data applications. A data lake is a vast pool of raw data, the purpose for which is not yet defined. A data warehouse is a repository for structured, filtered data that has already been processed for a specific purpose. ... In fact, the only real similarity between them is their high-level purpose of storing data.