Seminar Details

Implementing a Data Warehouse with Microsoft® SQL Server® (MOC20463)

ETC
MOC20463
Hersteller-ID: 20463
Prüfungs ID: 70-463
Dauer: 5 Tage
Preis: € 2.265.- exkl.MwST
Wissensgarantie: 12 Monate

Info: Dieses Seminar behandelt prüfungsrelevante Themen zum Microsoft Examen: 70-463 Implementing a Data Warehouse with Microsoft SQL Server 2012
Data warehousing is a solution organizations use to centralize business data for reporting and analysis. This five-day instructor-led course focuses on teaching individuals how to create a data warehouse with SQL Server 2014, and implement ETL with SQL Server Integration Services. This course helps people prepare for exam 70-463 Implementing a Data Warehouse with Microsoft SQL Server 2012.
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Zielgruppe

This course is intended for database professionals who need to create and support a data warehousing solution. Primary responsibilities include: Implementing a data warehouse. Developing SSIS packages for data extraction, transformation, and loading. Enforcing data integrity by using Master Data Services. Cleansing data by using Data Quality Services.

Vorkenntnisse

This course requires that you meet the following prerequisites:
  • At least 2 years’ experience of working with relational databases, including:
  • Designing a normalized database.
  • Creating tables and relationships.
  • Querying with Transact-SQL.
  • Some exposure to basic programming constructs (such as looping and branching).
  • An awareness of key business priorities such as revenue, profitability, and financial accounting is desirable.

Schwerpunkte

  1. Introduction to Data Warehousing
    1. Overview of Data Warehousing
    2. Considerations for a Data Warehouse Solution
      1. Exploring a Data Warehousing Solution
  2. Planning Data Warehouse Infrastructure
    1. Considerations for Data Warehouse Infrastructure
    2. Planning Data Warehouse Hardware
      1. Planning Data Warehouse Infrastructure
  3. Designing and Implementing a Data Warehouse
    1. Data Warehouse
    2. Designing Dimension Tables
    3. Designing Fact Tables
    4. Physical Design for a Data Warehouse
      1. Implementing a Data Warehouse
  4. Creating an ETL Solution with SSIS
    1. Introduction to ETL with SSIS
    2. Exploring Data Sources
    3. Implementing Data Flow
      1. Implementing Data Flow in an SSIS Package
  5. Implementing Control Flow in an SSIS Package
    1. Introduction to Control Flow
    2. Creating Dynamic Packages
    3. Using Containers
    4. Managing Consistency
      1. Implementing Control Flow in an SSIS Package
      2. Using Transactions and Checkpoints
  6. Debugging and Troubleshooting SSIS Packages
    1. Debugging an SSIS Package
    2. Logging SSIS Package Events
    3. Handling Errors in an SSIS Package
      1. Debugging and Troubleshooting an SSIS Package
  7. Implementing a Data Extraction Solution
    1. Planning Data Extraction
    2. Extracting Modified Data
      1. Extracting Modified Data
  8. Loading Data into a Data Warehouse
    1. Planning Data Loads
    2. Using SSIS for Incremental Loads
    3. Using Transact-SQL Loading Techniques
      1. Loading a Data Warehouse
  9. Module 9: Enforcing Data Quality
    1. Introduction to Data Quality
    2. Using Data Quality Services to Cleanse Data
    3. Using Data Quality Services to Cleanse Data
      1. Cleansing Data
  10. Master Data Services
    1. Introduction to Master Data Services
    2. Implementing a Master Data Services Model
    3. Managing Master Data
    4. Creating a Master Data Hub
      1. Implementing Master Data Services
  11. Extending SQL Server Integration Services
    1. Using Scripts in SSIS
    2. Using Custom Components in SSIS
      1. Using Custom Scripts
  12. Deploying and Configuring SSIS Packages
    1. Overview of SSIS Deployment
    2. Deploying SSIS Projects
    3. Planning SSIS Package Execution
      1. Deploying and Configuring SSIS Packages
  13. Consuming Data in a Data Warehouse
    1. Introduction to Business Intelligence
    2. Enterprise Business Intelligence
    3. Self-Service BI and Big Data
      1. Using a Data Warehouse
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