Implementing a Data Warehouse with Microsoft SQL Server 2014

1800 € HT 4 jours SQL S2014DW

Description
- This module provides an introduction to the key components of a data warehousing solution and the high-level considerations you must take into account when you embark on a data warehousing project
Lessons
- Overview of Data Warehousing
- Considerations for a Data Warehouse Solution
Lab
- Exploring a Data Warehousing Solution
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 project

Description
- This module discusses considerations for selecting hardware and distributing SQL Server facilities across servers
Lessons
- Considerations for building a Data Warehouse
- Data Warehouse Reference Architectures and Appliances
Lab
- Planning Data Warehouse Infrastructure
After completing this module, you will be able to:
- Describe key considerations for BI infrastructure
- Plan data warehouse infrastructure

Description
- This module describes the key considerations for the logical design of a data warehouse, and then discusses best practices for its physical implementation
Lessons
- Logical Design for a Data Warehouse
- Physical design for a data warehouse
Lab
- Implementing a Data Warehouse Schema
After completing this module, you will be able to:
- Describe a process for designing a dimensional model for a data warehouse
- Design dimension tables for a data warehouse
- Design fact tables for a data warehouse
- Design and implement effective physical data structures for a data warehouse

Description
- This module discusses considerations for implementing an ETL process, and then focuses on Microsoft SQL Server Integration Services (SSIS) as a platform for building ETL solutions
Lessons
- Introduction to ETL with SSIS
- Exploring Data Sources
- Implementing Data Flow
Lab
- Implementing Data Flow in an SSIS Package
After completing this module, you will be able to:
- Describe the key features of SSIS
- Explore source data for an ETL solution
- Implement a data flow by using SSIS

Description
- This module describes how to implement ETL solutions that combine multiple tasks and workflow logic
Lessons
- Introduction to Control Flow
- Creating Dynamic Packages
- Using Containers
- Managing Consistency
Lab
- Implementing Control Flow in an SSIS Package
Lab
- Using Transactions and Checkpoints
After completing this module, you will be able to:
- Implement control flow with tasks and precedence constraints
- Create dynamic packages that include variables and parameters
- Use containers in a package control flow
- Enforce consistency with transactions and checkpoints

Description
- This module describes how you can debug packages to find the cause of errors that occur during execution
- It then discusses the logging functionality built into SSIS that you can use to log events for troubleshooting purposes
- Finally, the module describes common approaches for handling errors in control flow and data flow
Lessons
- Debugging an SSIS Package
- Logging SSIS Package Events
- Handling Errors in an SSIS Package
Lab
- Debugging and Troubleshooting an SSIS Package
After completing this module, you will be able to:
- Debug an SSIS package
- Implement logging for an SSIS package
- Handle errors in an SSIS package

Description
- This module describes the techniques you can use to implement an incremental data warehouse refresh process
Lessons
- Introduction to Incremental ETL
- Extracting Modified Data
- Loading Modified data
Lab
- Extracting Modified Data
Lab
- Loading Incremental Changes
After completing this module, you will be able to:
- Plan data extraction
- Extract modified data

Description
- This module introduces Microsoft SQL Server Data Quality Services (DQS), and describes how you can use it to cleanse and deduplicate data
Lessons
- Introduction to Data Quality
- Using Data Quality Services to Cleanse Data
- Using Data Quality Services to Match data
Lab
- Cleansing Data
Lab
- De-duplicating data
After completing this module, you will be able to:
- Describe how Data Quality Services can help you manage data quality
- Use Data Quality Services to cleanse your data
- Use Data Quality Services to match data

Description
- Master Data Services provides a way for organizations to standardize data and improve the quality, consistency, and reliability of the data that guides key business decisions
- This module introduces Master Data Services and explains the benefits of using it
Lessons
- Master Data Services Concepts
- Implementing a Master Data Services Model
- Managing Master Data
- Creating a Master Data Hub
Lab
- Implementing Master Data Services
After completing this module, you will be able to:
- Describe key Master Data Services concepts
- Implement a Master Data Services model
- Use Master Data Services tools to manage master data
- Use Master Data Services tools to create a master data hub

Description
- This module describes the techniques you can use to extend SSIS
- The module is not designed to be a comprehensive guide to developing custom SSIS solutions, but to provide an awareness of the fundamental steps required to use custom components and scripts in an ETL process that is based on SSIS
Lessons
- Using Scripts in SSIS
- Using Custom Components in SSIS
Lab
- Using Custom Components and Scripts
After completing this module, you will be able to:
- Include custom scripts in an SSIS package
- Describe how custom components can be used to extend SSIS

Description
- In this module, students will learn how to deploy packages and their dependencies to a server, and how to manage and monitor the execution of deployed packages
Lessons
- Overview of SSIS Deployment
- Deploying SSIS Projects
- Planning SSIS Package Execution
Lab
- Deploying and Configuring SSIS Packages
After completing this module, you will be able to:
- Describe considerations for SSIS deployment
- Deploy SSIS projects
- Plan SSIS package execution

Description
- This module introduces business intelligence (BI) solutions and describes how you can use a data warehouse as the basis for enterprise and self-service BI
Lessons
- Introduction to Business Intelligence
- Introduction to Reporting
- An Introduction to Data Analysis
Lab
- Using Business Intelligence Tools
After completing this module, you will be able to:
- Describe BI and common BI scenarios
- Describe how a data warehouse can be used in enterprise BI scenarios
- Describe how a data warehouse can be used in self-service BI scenarios