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20767: Implementing a SQL 2016 Data Warehouse Training & Certification Course

Overview

Implementing a SQL Data Warehouse (Course 20767)

Training and Certification for SQL DW 20767: Overview

Get access to the instructor-led training program from experienced faculty at SpireTec on implementing a SQL Data Warehouse (SQL DW) for your mobile applications or websites. With the growing adoption of Azure’s platform-as-a-service, there has been a heightened demand for database engineers, data scientists or business intelligence experts with proficiency in implementing and managing SQL Data warehouse.

From provisioning SQL Data Warehouse on-premise to Azure – the training on implementing data warehouse helps you master standard skills required. Learn to migrate a data warehouse to SQL database, manage databases for multi-tenant apps, create dev and test databases to speed up development cycles, scale production business services, and containerize data in the cloud for isolation and security.

Implementing Data Warehouse: Exam 20767 Topics

  1. Understand core components of a data warehousing solution
  2. Know key hardware considerations for building a data warehouse
  3. Implement a logical design for a data warehouse
  4. Implement a physical design for a data warehouse
  5. Create columnstore indexes
  6. Implementing an Azure SQL Data Warehouse
  7. Describe the key features of SSIS
  8. Implement a data flow by using SSIS
  9. Implement control flow by using tasks and precedence constraints
  10. Create dynamic packages that include variables and parameters
  11. Debug SSIS packages
  12. Describe the considerations for implement an ETL solution
  13. Implement Data Quality Services
  14. Implement a Master Data Services model
  15. Describe how you can use custom components to extend SSIS
  16. Deploy SSIS projects
  17. Describe BI and common BI scenarios

Who Should Take SQL Data Warehouse Implementation Course?

Database Engineers

Database engineers are responsible for creating and managing databases for an organization as per its requirement.  The database engineers install and configure the database, and make sure it’s functional and secure. The average salary of a database developer in the USA is $75,217. A certification on implementing a SQL Data warehouse based on course 20767-C validates your skills in front of recruiters and helps you find jobs with top-notch companies and also get a better compensation.

Data Scientists

Data scientists are responsible for mining complex data and providing systems-related advice for their organization. They design IT structure to manage statistical data and create different models based on the needs of their company. Leveraging advanced analytical skills and implementing data warehouse, they spreadhead product development, sales and marketing, and innovation.

Business Intelligence Professionals

Business intelligence (BI) analysts help guide and improve the way that businesses’ management staff foster collaboration within and between departments.  Since they have to work with a vast growing data volume, they must earn an Azure 20767-C certification on implementing a SQL data warehouse.

Prerequisites for 20767-C certification

In addition to professional experience, basic knowledge of the Microsoft Windows operating system and its core functionality, working knowledge of relational databases and basic experience with database design is recommended.

Recommended course

20768: Developing SQL 2016 Data Models

Full Description

 Module 1: Introduction to Data Warehousing

This module describes data warehouse concepts and architecture consideration.

Lessons

  1. Overview of Data Warehousing
  2. Considerations for a Data Warehouse Solution

Lab: Exploring a Data Warehouse Solution

  • Exploring data sources
  • Exploring an ETL process
  • Exploring a data warehouse

After completing this module, you will be able to:

  • Describe the key elements of a data warehousing solution
  • Describe the key considerations for a data warehousing solution

 

Module 2: Planning Data Warehouse Infrastructure

This module describes the main hardware considerations for building a data warehouse.

Lessons

  1. Considerations for data warehouse infrastructure.
  2. Planning data warehouse hardware.

Lab: Planning Data Warehouse Infrastructure

  • Planning data warehouse hardware

After completing this module, you 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

 

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

  1. Data warehouse design overview
  2. Designing dimension tables
  3. Designing fact tables
  4. Physical Design for a Data Warehouse

Lab: Implementing a Data Warehouse Schema

  • Implementing a star schema
  • Implementing a snowflake schema
  • Implementing a time dimension table

After completing this module, you will be able to:

  • Implement a logical design for a data warehouse
  • Implement a physical design for a data warehouse

 

Module 4: Columnstore Indexes

This module introduces Columnstore Indexes.

Lessons

  1. Introduction to Columnstore Indexes
  2. Creating Columnstore Indexes
  3. Working with Columnstore Indexes

Lab: Using Columnstore Indexes

  • Create a Columnstore index on the FactProductInventory table
  • Create a Columnstore index on the FactInternetSales table
  • Create a memory-optimized Columnstore table

After completing this module, you will be able to:

  • Create Columnstore indexes
  • Work with Columnstore Indexes

 

Module 5: Implementing an Azure SQL Data Warehouse

This module describes Azure SQL Data Warehouses and how to implement them.

Lessons

  1. Advantages of Azure SQL Data Warehouse
  2. Implementing an Azure SQL Data Warehouse
  3. Developing an Azure SQL Data Warehouse
  4. Migrating to an Azure SQ Data Warehouse
  5. Copying data with the Azure data factory

Lab: Implementing an Azure SQL Data Warehouse

  • Create an Azure SQL data warehouse database
  • Migrate to an Azure SQL Data warehouse database
  • Copy data with the Azure data factory

After completing this module, you 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

 

Module 6: Creating an ETL Solution

At the end of this module, you will be able to implement data flow in an SSIS package.

Lessons

  1. Introduction to ETL with SSIS
  2. Exploring Source Data
  3. Implementing Data Flow

Lab: Implementing Data Flow in an SSIS Package

  • Exploring source data
  • Transferring data by using a data row task
  • Using transformation components in a data row

After completing this module, you will be able to:

  • Describe ETL with SSIS
  • Explore Source Data
  • Implement a Data Flow

 

Module 7: Implementing Control Flow in an SSIS Package

This module describes implementing control flow in an SSIS package.

Lessons

  1. Introduction to Control Flow
  2. Creating Dynamic Packages
  3. Using Containers
  4. Managing consistency.

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, you will be able to:

  • Describe control flow
  • Create dynamic packages
  • Use containers

 

Module 8: Debugging and Troubleshooting SSIS Packages

This module describes how to debug and troubleshoot SSIS packages.

Lessons

  1. Debugging an SSIS Package
  2. Logging SSIS Package Events
  3. 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 the data flow

After completing this module, you will be able to:

  • Debug an SSIS package
  • Log SSIS package events
  • Handle errors in an SSIS package

 

Module 9: Implementing a Data Extraction Solution

This module describes how to implement an SSIS solution that supports incremental DW loads and changing data.

Lessons

  1. Introduction to Incremental ETL
  2. Extracting Modified Data
  3. Loading modified data
  4. 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 a data warehouse

  • 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, you will be able to:

  • Describe incremental ETL
  • Extract modified data
  • Load modified data.
  • Describe temporal tables

 

Module 10: Enforcing Data Quality

This module describes how to implement data cleansing by using Microsoft Data Quality services.

Lessons

  1. Introduction to Data Quality
  2. Using Data Quality Services to Cleanse Data
  3. 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, you 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

 

Module 11: Using Master Data Services

This module describes how to implement master data services to enforce data integrity at the source.

Lessons

  1. Introduction to Master Data Services
  2. Implementing a Master Data Services Model
  3. Hierarchies and collections
  4. 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, you 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

 

Module 12: Extending SQL Server Integration Services (SSIS)

This module describes how to extend SSIS with custom scripts and components.

Lessons

  1. Using scripting in SSIS
  2. Using custom components in SSIS

Lab: Using scripts

  • Using a script task

After completing this module, you will be able to:

  • Use custom components in SSIS
  • Use scripting in SSIS

 

Module 13: Deploying and Configuring SSIS Packages

This module describes how to deploy and configure SSIS packages.

Lessons

  1. Overview of SSIS Deployment
  2. Deploying SSIS Projects
  3. 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, you will be able to:

  • Describe an SSIS deployment
  • Deploy an SSIS package
  • Plan SSIS package execution

 

Module 14: Consuming Data in a Data Warehouse

This module describes how to debug and troubleshoot SSIS packages.

Lessons

  1. Introduction to Business Intelligence
  2. An Introduction to Data Analysis
  3. Introduction to reporting
  4. Analyzing Data with Azure SQL Data Warehouse

Lab : Using a data warehouse

  • Exploring a reporting services report
  • Exploring a PowerPivot workbook
  • Exploring a power view report

 

Fees & Schedule

Delivery Mode Course Duration Fees
Live Virtual Training 5 Days Ask for Quote
Onsite Classroom Training 5 Days Ask for Quote
Customized Training 5 Days Ask for Quote

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