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Implementing a SQL Data Warehouse Training (20767)
Nivå: Intermediate
Snittbetyg:
4,57/5 Based on 94 Reviews
This five-day instructor-led course provides students with the knowledge and skills to provision a Microsoft SQL Server database. The course covers SQL Server provision both on-premise and in Azure, and covers installing from new and migrating from an existing install.
Plus, get prepped for Microsoft exam 70-767, a requirement for MCSA: SQL 2016 BI Development and MCSE: Data Management and Analytics.
Nyckelfunktioner:
- Microsoft Official Course (MOC) content
- After-course instructor coaching benefit
- Prepare for Microsoft 70-767 certification exam, Implementing a SQL Data Warehouse (beta)
- Eligible to use with your Microsoft Software Assurance Training Vouchers (SATVs)
Du kommer lära dig att:
- Describe the key elements of a data warehousing solution
- Describe the main hardware considerations for building a data warehouse
- Implement a logical design for a data warehouse
- Implement a physical design for a data warehouse
- Create columnstore indexes
- Implementing an Azure SQL Data Warehouse
- Describe the key features of SSIS
- Implement a data flow by using SSIS
- Implement control flow by using tasks and precedence constraints
- Create dynamic packages that include variables and parameters
- Debug SSIS packages
- Describe the considerations for implement an ETL solution
- Implement Data Quality Services
- Implement a Master Data Services model
- Describe how you can use custom components to extend SSIS
- Deploy SSIS projects
- Describe BI and common BI scenarios
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I klass & Live, Online-utbildning
- 5-day instructor-led training course
- Microsoft Certified training
- SATV eligible
- One-on-one after course instructor coaching
- Pay later by invoice -OR- at the time of checkout by credit card
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- Coaching av ett expertteam
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I klass & Live, Online-utbildning
Note: This course runs for 5 dagar
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1 - 5 mar 9:00 - 4:30 GMT Online (AnyWare)
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24 - 28 maj 9:00 - 4:30 BST Online (AnyWare)
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16 - 20 aug 9:00 - 4:30 BST Online (AnyWare)
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15 - 19 nov 9:00 - 4:30 GMT Online (AnyWare)
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22 - 26 mar 9:00 - 4:30 EDT Online (AnyWare)
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19 - 23 apr 9:00 - 4:30 EDT New York / Online (AnyWare)
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20 - 24 sep 9:00 - 4:30 EDT Herndon, VA / Online (AnyWare)
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18 - 22 okt 9:00 - 4:30 EDT New York / Online (AnyWare)
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Kurs med startgaranti
När du ser symbolen för “Guaranteed to Run” vid ett kurstillfälle vet du att kursen blir av. Garanterat.SQL Data Warehouse Course Information
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Requirements
- In addition to their professional experience, students who attend this training should already have the following technical knowledge:
- At least 2 years’ experience of working with relational databases, including:
- Designing a normalised database
- Creating tables and relationships
- Querying with Transact-SQL
- Some exposure to basic programming constructs (such as looping and branching)
- An awareness of key business priorities such as revenue, profitability, and financial accounting is desirable
- In addition to their professional experience, students who attend this training should already have the following technical knowledge:
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Exam Information
This course can help you prepare for the following Microsoft certification exam — 70-767: Implementing a Data Warehouse using SQL
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Redeem Your Microsoft Training Vouchers (SATV):
- This course is eligible for Microsoft Software Assurance Training Voucher (SATV) redemption.
SQL Data Warehouse Course Outline
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Module 1: Introduction to Data Warehousing
Describe data warehouse concepts and architecture considerations.
Lessons
- Overview of Data Warehousing
- Considerations for a Data Warehouse Solution
Lab: Exploring a Data Warehouse Solution
After completing this module, students will be able to:
- Describe the key elements of a data warehousing solution
- Describe the key considerations for a data warehousing solution
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Module 2: Planning Data Warehouse Infrastructure
This module describes the main hardware considerations for building a data warehouse.
Lessons
- Considerations for Building a Data Warehouse
- Data Warehouse Reference Architectures and Appliances
Lab: Planning Data Warehouse Infrastructure
After completing this module, students will be able to:
- Describe the main hardware considerations for building a data warehouse
- Explain how to use reference architectures and data warehouse appliances to create a data warehouse
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Module 3: Designing and Implementing a Data Warehouse
This module describes how you go about designing and implementing a schema for a data warehouse.
Lessons
- Logical Design for a Data Warehouse
- Physical Design for a Data Warehouse
Lab: Implementing a Data Warehouse Schema
After completing this module, students will be able to:
- Implement a logical design for a data warehouse
- Implement a physical design for a data warehouse
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Module 4: Columnstore Indexes
This module introduces Columnstore Indexes.
Lessons
- Introduction to Columnstore Indexes
- Creating Columnstore Indexes
- Working with Columnstore Indexes
Lab: Using Columnstore Indexes
After completing this module, students will be able to:
- Create Columnstore indexes
- Work with Columnstore Indexes
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Module 5: Implementing an Azure SQL Data Warehouse
This module describes Azure SQL Data Warehouses and how to implement them.
Lessons
- Advantages of Azure SQL Data Warehouse
- Implementing an Azure SQL Data Warehouse
- Developing an Azure SQL Data Warehouse
- Migrating to an Azure SQ Data Warehouse
Lab: Implementing an Azure SQL Data Warehouse
After completing this module, students will be able to:
- Describe the advantages of Azure SQL Data Warehouse
- Implement an Azure SQL Data Warehouse
- Describe the considerations for developing an Azure SQL Data Warehouse
- Plan for migrating to Azure SQL Data Warehouse
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Module 6: Creating an ETL Solution
At the end of this module you will be able to implement data flow in a SSIS package.
Lessons
- Introduction to ETL with SSIS
- Exploring Source Data
- Implementing Data Flow
Lab: Implementing Data Flow in an SSIS Package
After completing this module, students will be able to:
- Describe ETL with SSIS
- Explore Source Data
- Implement a Data Flow
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Module 7: Implementing Control Flow in an SSIS Package
This module describes implementing control flow in an SSIS package.
Lessons
- Introduction to Control Flow
- Creating Dynamic Packages
- Using Containers
Lab: Implementing Control Flow in an SSIS Package
- Using tasks and precedence in a control flow
- Using variables and parameters
- Using containers
Lab: Using Transactions and Checkpoints
- Using transactions
- Using checkpoints
After completing this module, students will be able to:
- Describe control flow
- Create dynamic packages
- Use containers
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Module 8: Debugging and Troubleshooting SSIS Packages
This module describes how to debug and troubleshoot SSIS packages.
Lessons
- Debugging an SSIS Package
- Logging SSIS Package Events
- Handling Errors in an SSIS Package
Lab: Debugging and Troubleshooting an SSIS Package
- Debugging an SSIS package
- Logging SSIS package execution
- Implementing an event handler
- Handling errors in data flow
After completing this module, students will be able to:
- Debug an SSIS package
- Log SSIS package events
- Handle errors in an SSIS package
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Module 9: Implementing an Incremental ETL Process
This module describes how to implement an SSIS solution that supports incremental DW loads and changing data.
Lessons
- Introduction to Incremental ETL
- Extracting Modified Data
- \
- Loading modified data
- Temporal Tables
Lab: Extracting Modified Data
- Using a datetime column to incrementally extract data
- Using change data capture
- Using the CDC control task
- Using change tracking
Lab: Loading Incremental Changes
- Loading data from CDC output tables
- Using a lookup transformation to insert or update dimension data
- Implementing a slowly changing dimension
- Using the merge statement
After completing this module, students will be able to:
- Describe incremental ETL
- Extract modified data
- Describe temporal tables
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Module 10: Enforcing Data Quality
This module describes how to implement data cleansing by using Microsoft Data Quality services.
Lessons
- Introduction to Data Quality
- Using Data Quality Services to Cleanse Data
- Using Data Quality Services to Match Data
Lab: Cleansing Data
- Creating a DQS knowledge base
- Using a DQS project to cleanse data
- Using DQS in an SSIS package
Lab: De-duplicating Data
- Creating a matching policy
- Using a DS project to match data
After completing this module, students will be able to:
- Describe data quality services
- Cleanse data using data quality services
- Match data using data quality services
- De-duplicate data using data quality services
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Module 11: Using Master Data Services
This module describes how to implement master data services to enforce data integrity at source.
Lessons
- Introduction to Master Data Services
- Implementing a Master Data Services Model
- Managing Master Data
- Creating a Master Data Hub
Lab: Implementing Master Data Services
- Creating a master data services model
- Using the master data services add-in for Excel
- Enforcing business rules
- Loading data into a model
- Consuming master data services data
After completing this module, students will be able to:
- Describe the key concepts of master data services
- Implement a master data service model
- Manage master data
- Create a master data hub
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Module 12: Extending SQL Server Integration Services (SSIS)
This module describes how to extend SSIS with custom scripts and components.
Lessons
- Using Custom Components in SSIS
- Using Scripting in SSIS
Lab: Using Scripts
After completing this module, students will be able to:
- Using a script task
- Use custom components in SSIS
- Use scripting in SSIS
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Module 13: Deploying and Configuring SSIS Packages
This module describes how to deploy and configure SSIS packages.
Lessons
- Overview of SSIS Deployment
- Deploying SSIS Projects
- Planning SSIS Package Execution
Lab: Deploying and Configuring SSIS Packages
- Creating an SSIS catalogue
- Deploying an SSIS project
- Creating environments for an SSIS solution
- Running an SSIS package in SQL server management studio
- Scheduling SSIS packages with SQL server agent
After completing this module, students will be able to:
- Describe an SSIS deployment
- Deploy an SSIS package
- Plan SSIS package execution
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Module 14: Consuming Data in a Data Warehouse
This module describes how to debug and troubleshoot SSIS packages.
Lessons
- Introduction to Business Intelligence
- An Introduction to Data Analysis
- Introduction to Reporting
- Analysing Data with Azure SQL Data Warehouse
Lab: Using Business Intelligence Tools
- Exploring a reporting services report
- Exploring a PowerPivot workbook
- Exploring a power view report
After completing this module, students will be able to:
- Describe at a high level business intelligence
- Show an understanding of reporting
- Show an understanding of data analysis
- Analyse data with Azure SQL data warehouse
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SQL Data Warehouse Training FAQs
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Can I learn to implement a SQL data warehouse online?
Yes! We know your busy work schedule may prevent you from getting to one of our classrooms which is why we offer convenient online training to meet your needs.
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Can I use SATVs to take this SQL Data Warehouse course?
Yes! This course is eligible for Microsoft Software Assurance Training Voucher (SATV) redemption.
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Which Microsoft exam does this SQL Data Warehouse course prepare me for?
This course helps prepare you for the Microsoft 70-767 certification exam, Implementing a SQL Data Warehouse (beta).
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Is this SQL Data Warehouse course part of a Microsoft SQL Server certification path?
Yes! It fulfills a requirement for MCSA: SQL 2016 BI Development and MCSE: Data Management and Analytics certifications.
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