MCSA SQL 2016 Business Intelligence Development

MCSA SQL 2016 Business Intelligence Development

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Madrid


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Objetivos

Objetivos de certificación MCSA: • 70-767 Implementing a SQL Data WarehouseReports with Microsoft SQL Server: Este examen va dirigido a los desarrolladores de ETL (extracción, transformación y carga) y almacenamientos de datos que creen soluciones de Business Intelligence (BI). Sus responsabilidades incluyen la limpieza de datos, además de la implementación de ETL y almacenamientos de datos. • 70-768 Developing SQL Data Models: Este examen está dirigido a desarrolladores de inteligencia empresarial (BI) que se centran en la creación de soluciones de BI que requieren la implementación de modelos de datos multidimensionales, la implementación y el mantenimiento de cubos OLAP y la implementación de modelos de datos tabulares.

A quién va dirigido

• Personas con conocimientos en Informática. • Formación Profesional. • Titulados Universitarios. • Profesionales del sector IT que deseen actualizar sus conocimientos.

Requisitos

• Al menos 2 años de experiencia con bases de datos relacionales, incluyendo: • Diseño de una base de datos normalizada. • Creación de tablas y relaciones. • Consultando con Transact-SQL. • Alguna exposición a construcciones básicas de programación (como bucle y ramificación).

Temario completo de este curso

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

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