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E-grāmata: Exam Ref 70-767 Implementing a SQL Data Warehouse

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  • Formāts: 416 pages
  • Sērija : Exam Ref
  • Izdošanas datums: 09-Nov-2017
  • Izdevniecība: Microsoft Press
  • Valoda: eng
  • ISBN-13: 9781509304509
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Exam Ref 70-767 Implementing a SQL Data WarehousePalielināt
  • Formāts: 416 pages
  • Sērija : Exam Ref
  • Izdošanas datums: 09-Nov-2017
  • Izdevniecība: Microsoft Press
  • Valoda: eng
  • ISBN-13: 9781509304509
Citas grāmatas par šo tēmu:

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Direct from Microsoft, this Exam Ref is the official study guide for the new Microsoft 70-767 Implementing a SQL Data Warehouse certification exam.

Exam Ref 70-767 Implementing a SQL Data Warehouse offers professional-level preparation that helps candidates maximize their exam performance and sharpen their skills on the job. It focuses on the specific areas of expertise modern IT professionals need to successfully build modern data warehouses to support advanced business intelligence solutions. Coverage includes:

  • Designing and implementing an effective data warehouse, including dimension tables, fact tables, indexes, storage, partitioning, and more
  • Establishing successful processes for extracting, transforming, and loading data (ETL) with SQL Server Integration Services (SSIS) and Transact-SQL (T-SQL)
  • Integrating solutions that encompass cloud data and big data, using Polybase Integrate, Hadoop Integrate, the Azure Blob service, and related tools
  • Ensuring high levels of data quality with a Data Quality Services (DQS) knowledge base and a Master Data Services (MDS) model

Microsoft Exam Ref publications stand apart from third-party study guides because they:

  • Provide guidance from Microsoft, the creator of Microsoft certification exams
  • Target IT professional-level exam candidates with content focused on their needs, not "one-size-fits-all" content
  • Streamline study by organizing material according to the exam’s objective domain (OD), covering one functional group and its objectives in each chapter
  • Feature Thought Experiments to guide candidates through a set of "what if?" scenarios, and prepare them more effectively for Pro-level style exam questions
  • Explore big picture thinking around the planning and design aspects of the IT pro’s job role

For more information on Exam 70-767 and the MCSA: SQL 2016 Business Intelligence Development credential, visit microsoft.com/learning.

Introduction xiii
Organization of this book
xiii
Microsoft certifications
xiv
Acknowledgments
xiv
Microsoft Virtual Academy
xiv
Quick access to online references
xv
Errata, updates, & book support
xv
We want to hear from you
xv
Stay in touch
xv
Important: How to use this book to study for the exam
xvii
Chapter 1 Design and implement a data warehouse 1(78)
Skill 1.1 Design and implement dimension tables
2(16)
Determine attributes
2(5)
Design shared and conformed dimensions
7(1)
Design hierarchies
8(3)
Determine dimension keys and key relationships for a data warehouse
11(3)
Determine star or snowflake schema requirements
14(2)
Determine auditing or lineage requirements
16(1)
Implement data lineage of a dimension table
17(1)
Skill 1.2 Design and implement fact tables
18(6)
Identify measures
18(1)
Design and implement fact tables
19(2)
Implement additive, semi-additive, and non-additive measures
21(1)
Identify dimension table relationships
21(3)
Skill 1.3 Design and implement indexes for a data warehouse workload
24(16)
Design an indexing solution
24(4)
Implement clustered, nonclustered, filtered, and columnstore indexes
28(5)
Select appropriate indexes
33(7)
Skill 1.4 Design storage for a data warehouse
40(7)
Design an appropriate storage solution, including hardware, disk, and file layout
41(6)
Skill 1.5 Design and implement partitioned tables and views
47(26)
Design a partition structure to support a data warehouse
48(10)
Implement sliding windows
58(6)
Implement partition elimination
64(3)
Design a partition structure that supports the quick loading and scale-out of data
67(6)
Thought experiment
73(1)
Thought experiment answer
74(3)
Chapter summary
77(2)
Chapter 2 Extract, transform, and load data 79(124)
Skill 2.1 Design and implement an extract, transform, and load (ETL) control flow by using a SQL Server Integration Services (SSIS) package
80(43)
Understanding new terminologies
80(3)
Design and implement ETL control flow elements, including containers, tasks, and precedence constraints
83(21)
Create variables and parameters
104(3)
Create checkpoints, sequence and loop containers, and variables in SSIS
107(8)
Implement data profiling, parallelism, transactions, logging, and security
115(8)
Skill 2.2 Design and implement an ETL data flow by using an SSIS package
123(22)
Implement slowly changing dimension, fuzzy grouping, fuzzy lookup, audit, blocking, non-blocking, and term lookup transformations
123(20)
Data flow source and destination column mapping
143(1)
Determine appropriate scenarios for Transact-SQL joins versus SSIS lookup
144(1)
Skill 2.3 Implement an ETL solution that supports incremental data extraction
145(4)
Design fact table patterns
146(1)
Enable Change Data Capture
147(1)
Create a SQL MERGE statement
148(1)
Skill 2.4 Implement an ETL solution that supports incremental data loading
149(10)
Design a control flow to load change data
149(2)
Load data by using Transact-SQL Change Data Capture functions
151(4)
Load data by using Change Data Capture in SSIS
155(4)
Skill 2.5 Debug SSIS packages
159(26)
Fix performance, connectivity, execution, and failed logic issues by using the debugger
160(9)
Add data viewers
169(2)
Implement breakpoints
171(3)
Enable logging for package execution
174(4)
Implement error handling for data types
178(3)
Profile data with different tools
181(2)
Error handling at package level
183(2)
Skill 2.6 Deploy and configure SSIS packages and projects
185(15)
Create an SSIS catalog
185(4)
Deploy packages by using the deployment utility, SQL Server, and file systems
189(9)
Run and customize packages by using DTUTIL
198(2)
Thought exercise
200(1)
Thought exercise answer
201(1)
Chapter summary
201(2)
Chapter 3 Build data quality solutions 203(56)
Skill 3.1 Create a knowledge base
204(10)
Install DQS
204(3)
Create a Data Quality Services (DQS) knowledge base
207(1)
Determine appropriate use cases for a DQS Knowledge Base
208(1)
Perform domain management
209(3)
Perform knowledge discovery
212(2)
Skill 3.2 Maintain data quality by using DQS
214(6)
Add matching knowledge to a knowledge base
214(1)
Create a matching policy
214(2)
Prepare a DQS Knowledge Base for data deduplication
216(1)
Clean data by using DQS knowledge
217(2)
Clean data by using the SSIS DQS task
219(1)
Skill 3.3 Implement a Master Data Services (MDS) model
220(26)
Install MDS
221(5)
Use the Master Data Services Configuration Manager
226(1)
Create a Master Data Services database and web application
227(3)
Implement MDS
230(1)
Create models, entities, hierarchies, collections, and attributes
231(6)
Define security roles
237(2)
Import and export data
239(1)
Stage and load data
239(3)
Create and edit a subscription
242(1)
Implement entities, attributes, hierarchies, and business rules
243(3)
Skill 3.4 Manage data by using MDS
246(10)
Use MDS tools
247(1)
Deploy a sample model using MDSModelDeploy.exe
247(6)
Create a Master Data Management hub
253(3)
Thought exercise
256(1)
Thought exercise answer
256(1)
Chapter summary
256(3)
Index 259
Jose Chinchilla is the President of Agile Bay. He writes and speaks about SQL Server administration and performance, data warehousing, business intelligence, predictive analytics and big data. 







Raj Uchhana is an enterprise data architect specializing in business intelligence, enterprise data warehousing, and Microsofts Power BI. Raj can be reached at: Raj@BusinessIntelligenceNow.com  
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