MCA Microsoft Certified Associate Azure Data Engineer Study Guide - Perkins, Benjamin
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Présentation Mca Microsoft Certified Associate Azure Data Engineer Study Guide de Perkins, Benjamin Format Broché
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Résumé : Introduction xxvii Part I?Azure Data Engineer Certification and Azure Products 1 Chapter 1?Gaining the Azure Data Engineer Associate Certification 3 The Journey to Certification 7 How to Pass Exam DP- 203 8 Understanding the Exam Expectations and Requirements 9 Use Azure Daily 17 Read Azure Articles to Stay Current 17 Have an Understanding of All Azure Products 20 Azure Product Name Recognition 21 Azure Data Analytics 23 Azure Synapse Analytics 23 Azure Databricks 26 Azure HDInsight 28 Azure Analysis Services 30 Azure Data Factory 31 Azure Event Hubs 33 Azure Stream Analytics 34 Other Products 35 Azure Storage Products 36 Azure Data Lake Storage 37 Azure Storage 40 Other Products 42 Azure Databases 43 Azure Cosmos DB 43 Azure SQL Server Products 46 Additional Azure Databases 46 Other Products 47 Azure Security 48 Azure Active Directory 48 Role- Based Access Control 51 Attribute- Based Access Control 53 Azure Key Vault 53 Other Products 55 Azure Networking 56 Virtual Networks 56 Other Products 59 Azure Compute 59 Azure Virtual Machines 59 Azure Virtual Machine Scale Sets 60 Azure App Service Web Apps 60 Azure Functions 60 Azure Batch 60 Azure Management and Governance 60 Azure Monitor 61 Azure Purview 61 Azure Policy 62 Azure Blueprints (Preview) 62 Azure Lighthouse 62 Azure Cost Management and Billing 62 Other Products 63 Summary 64 Exam Essentials 64 Review Questions 66 Chapter 2 CREATE DATABASE dbName...
Biographie: ABOUT THE AUTHOR Benjamin Perkins is currently employed at Microsoft in Munich, Germany, as a Senior Escalation Engineer on the Azure team. He is a C# programming expert and cloud engineer who has been working professionally in the IT industry for almost three decades. His roles in IT have spanned the entire spectrum including programmer, system architect, technical support engineer, team leader, and mid-level management. While employed at Hewlett-Packard and Compaq Computer Corporation, he received numerous awards, degrees, and certifications....
Sommaire:
GO 69
The Brainjammer 70
A Historical Look at Data 71
Variety 73
Velocity 74
Volume 74
Data Locations 74
Data File Formats 75
Data Structures, Types, and Concepts 83
Data Structures 83
Data Types and Management 92
Data Concepts 95
Data Programming and Querying for Data Engineers 125
Data Programming 126
Querying Data 143
Understanding Big Data Processing 169
Big Data Stages 169
Etl, Elt, Eltl 174
Analytics Types 175
Big Data Layers 176
Summary 177
Exam Essentials 177
Review Questions 179
Part II Design and Implement Data Storage 181
Chapter 3 Data Sources and Ingestion 183
Where Does Data Come From? 185
Design a Data Storage Structure 189
Design an Azure Data Lake Solution 190
Recommended File Types for Storage 198
Recommended File Types for Analytical Queries 199
Design for Efficient Querying 200
Design for Data Pruning 203
Design a Folder Structure That Represents the Levels of Data Transformation 203
Design a Distribution Strategy 205
Design a Data Archiving Solution 206
Design a Partition Strategy 207
Design a Partition Strategy for Files 209
Design a Partition Strategy for Analytical Workloads 210
Design a Partition Strategy for Efficiency and Performance 211
Design a Partition Strategy for Azure Synapse Analytics 211
Identify When Partitioning Is Needed in Azure Data Lake Storage Gen 2 212
Design the Serving/Data Exploration Layer 213
Design Star Schemas 214
Design Slowly Changing Dimensions 215
Design a Dimensional Hierarchy 219
Design a Solution for Temporal Data 220
Design for Incremental Loading 222
Design Analytical Stores 223
Design Metastores in Azure Synapse Analytics and Azure Databricks 224
The Ingestion of Data into a Pipeline 228
Azure Synapse Analytics 228
Azure Data Factory 268
Azure Databricks 275
Event Hubs and IoT Hub 301
Azure Stream Analytics 303
Apache Kafka for HDInsight 314
Migrating and Moving Data 316
Summary 317
Exam Essentials 317
Review Questions 319
Chapter 4 The Storage of Data 321
Implement Physical Data Storage Structures 322
Implement Compression 322
Implement Partitioning 325
Implement Sharding 328
Implement Different Table Geometries with Azure Synapse Analytics Pools 329
Implement Data Redundancy 331
Implement Distributions 341
Implement Data Archiving 342
Azure Synapse Analytics Develop Hub 346
Implement Logical Data Structures 360
Build a Temporal Data Solution 361
Build a Slowly Changing Dimension 365
Build a Logical Folder Structure 368
Build External Tables 369
Implement File and Folder Structures for Efficient Querying and Data Pruning 372
Implement a Partition Strategy 375
Implement a Partition Strategy for Files 376
Implement a Partition Strategy for Analytical Workloads 377
Implement a Partition Strategy for Streaming Workloads 378
Implement a Partition Strategy for Azure Synapse Analytics 378
Design and Implement the Data Exploration Layer 379
Deliver Data in a Relational Star Schema 379
Deliver Data in Parquet Files 385
Maintain Metadata 386
Implement a Dimensional Hierarchy 386
Create and Execute Queries by Using a Compute Solution That Leverages SQL Serverless and Spark Cluster 388
Recommend Azure Synapse Analytics Database Templates 389
Implement ...
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