Understanding Azure Data Factory : operationalizing big data and advanced analytics solutions /: operationalizing big data and advanced analytics solutions. ([2019])

• Record Type: Book
• Further Information: Note: Sudhir Rawat, Abhishek Narain.
• Authors: Narain, Abhishek
• Other Names: Rawat, Sudhir
• Contents: Intro; Table of Contents; About the Authors; About the Technical Reviewer; Introduction; Chapter 1: Introduction to Data Analytics; What Is Big Data?; Why Big Data?; Big Data Analytics on Microsoft Azure; What Is Azure Data Factory?; High-Level ADF Concepts; Activity; Pipeline; Datasets; Linked Service; Integration Runtime; When to Use ADF?; Why ADF?; Summary; Chapter 2: Introduction to Azure Data Factory; Azure Data Factory v1 vs. Azure Data Factory v2; Data Integration with Azure Data Factory; Architecture; Concepts; Pipelines; Activities; Execution Activities (Copy and Data Transform) Activity PolicyControl; Activity Dependency; Datasets; Dataset Structure; When to Specify a Dataset Structure?; Linked Services; Linked Service Example; Integration Runtime; Azure IR; Self-Hosted IR; Azure-SSIS IR; Hands-on: Creating a Data Factory Instance Using a User Interface; Prerequisites; Steps; Hands-on: Creating a Data Factory Instance Using PowerShell; Prerequisites; Log In to PowerShell; Create a Data Factory; Summary; Chapter 3: Data Movement; Overview; How Does the Copy Activity Work?; Supported Connectors; Configurations; Supported File and Compression Formats Copy Activity PropertiesProperty Details; How to Create a Copy Activity; Schema Capture and Automatic Mapping in Copy Data Tool; Scenario: Creating a Copy Activity Using the Copy Data Tool (Binary Copy); Copy Performance Considerations; Data Integration Units; Parallel Copy; Staged Copy; How Staged Copy Works; Configuration; Staged Copy Billing Impact; Considerations for the Self-Hosted Integration Runtime; Considerations for Serialization and Deserialization; Considerations for Compression; Considerations for Column Mapping; Summary; Chapter 4: Data Transformation: Part 1 Data TransformationHDInsight; Hive Activity; Pig Activity; MapReduce Activity; Streaming Activity; Spark Activity; Azure Machine Learning; Azure Data Lake; Chapter 5: Data Transformation: Part 2; Data Warehouse to Modern Data Warehouse; ETL vs. ELT; Azure Databricks; Build and Implement Use Case; Stored Procedure; Custom Activity; Chapter 6: Managing Flow; Why Managing Flow Is Important; Expressions; Functions; Activities; Let's Build the Flow; Build the Source Database; Build Azure Blob Storage as the Destination; Build the Azure Logic App; Build the Azure Data Factory Pipeline; Summary Chapter 7: SecurityOverview; Cloud Scenario; Securing the Data Credentials; Data Encryption in Transit; Data Encryption at Rest; Hybrid Scenario; On-Premise Data Store Credentials; Encryption in Transit; Considerations for Selecting Express Route or VPN; Firewall Configurations and IP Whitelisting for Self-Hosted Integration Runtime Functionality; IP Configurations and Whitelisting in Data Stores; Proxy Server Considerations; Storing Credentials in Azure Key Vault; Prerequisites; Steps; Using the Authoring UI; Reference Secret Stored in Key Vault; Using the Authoring UI
• Publisher Details: New York, New York : Apress
• Publication Date: 2019
• Extent: 1 online resource (376 p.)
• Subjects: 005.7; Big data; Quantitative research; Big data; Electronic books
• Languages: English
• ISBNs: 9781484241226; 1484241223
• Related ISBNs: 9781484241219; 1484241215
• Access Rights: Legal Deposit; Only available on premises controlled by the deposit library and to one user at any one time; The Legal Deposit Libraries (Non-Print Works) Regulations (UK).
• Access Usage: Restricted: Printing from this resource is governed by The Legal Deposit Libraries (Non-Print Works) Regulations (UK) and UK copyright law currently in force.