Implementación de un Data Warehouse SQL (MOC 20767)
5 Días
Descripción
Este curso de cinco días impartido por un instructor proporciona a los estudiantes el conocimiento y las habilidades para aprovisionar una base de datos Microsoft SQL Server 2016. El curso cubre la provisión de SQL Server 2016 tanto en las instalaciones como en Azure, y cubre la instalación desde una ubicación nueva y la migración desde una instalación existente.
Requisitos previos
Además de su experiencia profesional, los estudiantes que asisten a esta capacitación ya deben tener los siguientes conocimientos técnicos:
- Conocimiento básico del sistema operativo Microsoft Windows y su funcionalidad principal.
- Conocimiento de trabajo de bases de datos relacionales.
- Alguna experiencia con el diseño de la base de datos.
A quien va dirigido
La audiencia principal de este curso son los profesionales de bases de datos que necesitan cumplir un rol de desarrollador de inteligencia empresarial. Tendrán que concentrarse en el trabajo práctico creando soluciones de BI que incluyan la implementación de Data Warehouse, ETL y limpieza de datos.
Objetivos
Después de completar este curso, los estudiantes podrán:
- Describe los elementos clave de una solución de almacenamiento de datos
- Describe las principales consideraciones de hardware para construir un almacén de datos
- Implementar un diseño lógico para un almacén de datos
- Implementar un diseño físico para un almacén de datos
- Crear índices de almacén de columnas
- Implementación de un almacén de datos SQL Azure
- Describe las características principales de SSIS
- Implementar un flujo de datos mediante el uso de SSIS
- Implementar el flujo de control mediante el uso de tareas y restricciones de precedencia
- Crear paquetes dinámicos que incluyen variables y parámetros
- Depurar paquetes de SSIS
- Describe las consideraciones para implementar una solución de ETL
- Implementar servicios de calidad de datos
- Implementar un modelo de servicios de datos maestros
- Describa cómo puede usar componentes personalizados para extender SSIS
- Implementar proyectos de SSIS
- Describir los escenarios BI y BI comunes
Temario
Module 1: Introduction to Data Warehousing
This module describes data warehouse concepts and architecture consideration.
Lessons
- 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
Module 2: Planning Data Warehouse Infrastructure
This module describes the main hardware considerations for building a data warehouse.
Lessons
- 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
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
- 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
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
- 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
- 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
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
- 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
- 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
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, 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
- 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
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, 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 source.
Lessons
- 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
Module 12: Extending SQL Server Integration Services (SSIS)
This module describes how to extend SSIS with custom scripts and components.
Lessons
- 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
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 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
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
- 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