20767: Implementing SQL Data Warehouse Training

This module describes data warehouse concepts and architecture consideration.

• Overview of Data Warehousing
• 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

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

• Considerations for data warehouse infrastructure.
• 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

This module describes how you go about designing and implementing a schema for a data warehouse.

• Data warehouse design overview
• Designing dimension tables
• Designing fact tables
• 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

This module introduces Columnstore Indexes.

• Introduction to Columnstore Indexes
• Creating Columnstore Indexes
• 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

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

• 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
• 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

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

• Introduction to ETL with SSIS
• Exploring Source Data
• 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

This module describes implementing control flow in an SSIS package.

• Introduction to Control Flow
• Creating Dynamic Packages
• Using Containers
• 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

This module describes how to debug and troubleshoot SSIS packages.

• 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, you will be able to:

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

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

• 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 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

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

• 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, 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

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

• Introduction to Master Data Services
• Implementing a Master Data Services Model
• Hierarchies and collections
• 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

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

• Using scripting in SSIS
• 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

This module describes how to deploy and configure SSIS packages.

• Overview of SSIS Deployment
• Deploying SSIS Projects
• Planning SSIS Package Execution

Lab: Deploying and Configuring SSIS Packages

• Creating an SSIS catalog
• 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
• Plan SSIS package execution

This module describes how to debug and troubleshoot SSIS packages.

• Introduction to Business Intelligence
• An Introduction to Data Analysis
• Introduction to reporting
• 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

After completing this module, you will be able to:

• Describe at a high level business intelligence
• Show an understanding of reporting
• Show an understanding of data analysis
• Analyze data with Azure SQL data warehouse

Yes, it is. Then exam is 75% oriented to SQL Server 2016 on-premises. It is 25% oriented to Azure Big Data, Azure SQL Data Warehouse and other related topics.

It is similar to 70-463 exam; however, this exam now covers Integrate with Cloud data and big data chapter and some other updates.

Yes, it prepares you for MCSA: SQL 2016 BI Development and MCSE: Data Management and Analytics certifications.

The class room live lab course outline as described by Microsoft contains 17 exercises including - Exploring a Data Warehouse Solution, Planning Data Warehouse Infrastructure, Using Columnstore Indexes, Implementing a Data Warehouse Schema and Implementing an Azure SQL Data Warehouse etc.