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Máster en Microsoft MCSA: SQL 2016 Business Intelligence Development en Madrid

CAS TRAINING

Programa de Máster en Microsoft MCSA: SQL 2016 Business Intelligence Development

CAS TRAINING
Presencial Duración: 51 horas
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Presencial
Impartido en: Madrid
La certificación MCSA: SQL 2016 Business Intelligence Development le cualifica para un puesto como desarrollador BI.

Descripción

Con nuestra formación podrás prepararte para exámenes necesarios y obtener la certificación MCSA: SQL Server.

La certificación MCSA: SQL 2016 business intelligence development, acredita que la persona, tiene capacidades para la implementación de soluciones BI, extracción, transformación y carga (ETL) y almacenamientos de datos.



A quién va dirigido

El Máster en microsoft MCSA: SQL 2016 business intelligence development se dirige a: toda persona con conocimientos en Informática, con Formación Profesional, a Titulados Universitarios, a Profesionales del sector IT.

Objetivos

El objetivo principal del máster es preparar a los participantes para los exámenes de 70-767 Implementing a SQL Data WarehouseReports with Microsoft SQL Server y 70-768 Developing SQL Data Models.



Temario

MOC 20767: Implementing a SQL Data Warehouse 


1. Introduction to Data Warehousing

1.1. Overview of Data Warehousing

1.2. Considerations for a Data Warehouse Solution


2. Planning Data Warehouse Infrastructure

2.1. Considerations for Building a Data Warehouse

2.2. Data Warehouse Reference Architectures and Appliances


3. Designing and Implementing a Data Warehouse

3.1. Logical Design for a Data Warehouse

3.2. Physical Design for a Data Warehouse


4. Columnstore Indexes

4.1. Introduction to Columnstore Indexes

4.2. Creating Columnstore Indexes

4.3. Working with Columnstore Indexes


5. Implementing an Azure SQL Data Warehouse

5.1. Advantages of Azure SQL Data Warehouse

5.2. Implementing an Azure SQL Data Warehouse

5.3. Developing an Azure SQL Data Warehouse

5.4. Migrating to an Azure SQ Data Warehouse


6. Creating an ETL Solution

6.1. Introduction to ETL with SSIS

6.2. Exploring Source Data

6.3. Implementing Data Flow


7. Implementing Control Flow in an SSIS Package

7.1. Introduction to Control Flow

7.2. Creating Dynamic Packages

7.3. Using Containers


8. Debugging and Troubleshooting SSIS Packages

8.1. Debugging an SSIS Package

8.2. Logging SSIS Package Events

8.3. Handling Errors in an SSIS Package


9. Implementing an Incremental ETL Process

9.1. Introduction to Incremental ETL

9.2. Extracting Modified Data

9.3. Temporal Tables


10. Enforcing Data Quality

10.1. Introduction to Data Quality

10.2. Using Data Quality Services to Cleanse Data

10.3. Using Data Quality Services to Match Data


11. Using Master Data Services

11.1. Master Data Services Concepts

11.2. Implementing a Master Data Services Model

11.3. Managing Master Data

11.4. Creating a Master Data Hub


12. Extending SQL Server Integration Services (SSIS)

12.1. Using Custom Components in SSIS

12.2. Using Scripting in SSIS


13. Deploying and Configuring SSIS Packages

13.1. Overview of SSIS Deployment

13.2. Deploying SSIS Projects

13.3. Planning SSIS Package Execution


14. Consuming Data in a Data Warehouse

14.1. Introduction to Business Intelligence

14.2. Introduction to Reporting

14.3. An Introduction to Data Analysis

14.4. Analyzing Data with Azure SQL Data Warehouse


MOC 20768: Developing SQL Data Models

 

1. Introduction to Business Intelligence and Data Modeling

1.1. Introduction to Business Intelligence

1.2. The Microsoft business intelligence platform


2. Creating Multidimensional Databases

2.1. Introduction to multidimensional analysis

2.2. Creating data sources and data source views

2.3. Creating a cube

2.4. Overview of cube security


3. Working with Cubes and Dimensions 

3.1. Configuring dimensions

3.2. Define attribute hierarchies

3.3. Sorting and grouping attributes


4. Working with Measures and Measure Groups

4.1. Working with measures

4.2. Working with measure groups


5. Introduction to MDX

5.1. MDX fundamentals

5.2. Adding calculations to a cube

5.3. Using MDX to query a cube


6. Customizing Cube Functionality

6.1. Implementing key performance indicators

6.2. Implementing actions

6.3. Implementing perspectives

6.4. Implementing translations


7. Implementing a Tabular Data Model by Using Analysis Services

7.1. Introduction to tabular data models

7.2. Creating a tabular data model

7.3. Using an analysis services tabular model in an enterprise BI solution


8. Introduction to Data Analysis Expression (DAX)

8.1. DAX fundamentals

8.2. Using DAX to create calculated columns and measures in a tabular data model


9. Performing Predictive Analysis with Data Mining

9.1. Overview of data mining

9.2. Using the data mining add-in for Excel

9.3. Creating a custom data mining solution

9.4. Validating a data mining model

9.5. Connecting to and consuming a data mining model

Titulación obtenida

Finalizado el máster el participante obtendrá una Titulación Oficial, emitida por el centro que certifica los estudios realizados.

Requisitos

Para acceder al Máster en microsoft MCSA: SQL 2016 business intelligence development, es necesario que el aspirante tenga dos años de experiencia con bases de datos relacionales además de: diseño de una base de datos normalizada, elaboración de tablas y relaciones, realizando consultas con Transact-SQL y a construcciones básicas de programación (como bucle y ramificación).




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