Curso Microsoft MCSA: SQL 2016 Business Intelligence Development

Cas Training Impartición

Curso en Madrid

Nuestro portal te presenta el curso de "Microsoft MCSA: SQL 2016 Business Intelligence Development " impartido por Cas Training

Sedes

Madrid

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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