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Why Data Virtualization?
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Classroom Contents
Azure Data Lake - Summit Preview
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- 1 Intro
- 2 Technical Assistance
- 3 Thank you to our Presenting Sponsor
- 4 Agenda Azure Data Lake: What, Why and How
- 5 What is a Data Lake?
- 6 Data Lake Objectives
- 7 Data Lake Use Cases
- 8 Big Data in Azure: Storage
- 9 Deciding Between Storage Services
- 10 Big Data in Azure: Compute
- 11 Deciding Between Compute Services
- 12 Azure Data Lake Store - a Shared Foundation
- 13 Azure Data Lake Store - Distributed File System
- 14 Azure Data Lake Analytics (ADLA)
- 15 U-SQL: EXTRACT
- 16 U-SQL: Single Input File
- 17 U-SQL: Multiple Input Files
- 18 U-SQL: Built-In Extractors and Outputters
- 19 U-SQL: Variables
- 20 U-SQL: Processing Rules
- 21 U-SQL: Keywords
- 22 Cost Model
- 23 AU Analyzer to Optimize for Cost and Speed
- 24 ADLA Integration with Other Services
- 25 Basics of U-SQL Query Execution
- 26 Two Types of Tables in ADLA Catalog
- 27 Multi-Platform Architecture
- 28 Data Lake + Data Warehouse: Inverse Relationship
- 29 Why Data Virtualization?
- 30 Two Ways to Approach Federated Queries in ADLA
- 31 Data Movement vs. Data Virtualization
- 32 PolyBase for Data Loading
- 33 PolyBase Design Pattern for Data Management
- 34 Getting Started with a Data Lake Project
- 35 Definitions
