• wykład
• laboratorium
• prezentacje
• ćwiczenia praktyczne
• dyskusje

• administracja
• programowanie
• użytkowanie
• projektowanie

• certyfikowany trener
• materiały szkoleniowe (w wersji papierowej)
• notatnik, długopis
• lunch
• kawa, herbata, napoje, ciasteczka
• dyplom

Podstawową grupą szkoleniową są specjaliści baz danych, którzy mają pełnić rolę Business Intelligence Developer, czyli zajmują się rozwiązaniami BI we wdrażaniu hurtownii danych, ETL oraz filtrowania danych. 

Po ukończeniu tego kursu uczestnicy będą:

Wiedza z zakresu szkolenia 

http://cts.com.pl/MS-10774--Beta-Querying-Microsoft-SQL-Server-2012

MS 10774 - Beta: Querying Microsoft SQL Server 2012

 Module 1: Introduction to Data WarehousingThis module provides an introduction to the key components of a data warehousing solution and the high-level considerations you must take into account when embarking on a data warehousing project.Lessons

• Describe data warehouse concepts and architecture considerations
• Considerations for a Data Warehouse Solution

Lab : Exploring a Data Warehousing Solution

• Exploring Data Sources
• Exploring an ETL Process
• Exploring a Data Warehouse

Module 2: Data Warehouse Hardware ConsiderationsThis module describes the considerations for selecting the appropriate hardware platform for your data warehouse solution.Lessons

• The Challenges of Building a Data Warehouse
• Data Warehouse Reference Architectures
• Data Warehouse Appliances

Lab : No lab

Module 3: Designing and Implementing a Data Warehouse

• Logical Design for a Data Warehouse
• Physical Design for a Data Warehouse

Lab : Implementing a Data Warehouse Schema

• Implementing a Star Schema
• Implementing a Snowflake Schema
• Implement a Time Dimension Table

Module 4: Design and implement a schema for a data warehouse

• Introduction to ETL with SSIS
• Exploring Source Data
• Implementing Data Flow

Lab : Implementing Data Flow in an SSIS Package

• Exploring Source Data
• Transfer Data with a Data Flow Task
• Using Transformations in a Data Flow

Module 5: Implementing Control Flow in an SSIS Package

• 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

Module 6: Debugging and Troubleshooting SSIS PackagesThis module describes how you can debug packages to find the cause of errors that occur during execution. It then discusses the logging functionality built into SSIS that you can use to log events for troubleshooting purposes. Finally, the module describes common approaches for handling errors in control flow and data flow.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 a Data Flow

Module 7: Implementing an Incremental ETL ProcessThis module describes the techniques you can use to implement an incremental data warehouse refresh process.Lessons

• Introduction to Incremental ETL
• Extracting Modified Data
• Loading Modified Data

Lab : Extracting Modified Data

• Using a DateTime Column to Incrementally Extract Data
• Using a DateTime Column to Incrementally Extract Data
• Using Change Tracking

Lab : Loading Incremental Changes

• Using a Lookup task to insert dimension data
• Using a Lookup task to insert or update dimension data
• Implementing a Slowly Changing Dimension
• Using a MERGE statement to load fact data

Module 8: Incorporating Data from the Cloud in a Data WarehouseThis modules describes how integrate cloud data into a data warehouse ecosystem.Lessons

• Overview of Cloud Data Sources
• SQL Server Azure
• Azure Data Market

Lab : Using Cloud data in a Data Warehouse Solution

• Extracting data from SQL Azure
• Acquiring Data from the Azure Data Market

Module 9: Enforcing Data QualityThis modules describes how to use Data Quality Services (DQS) for cleansing and deduplicating your data.Lessons

• Introduction to Data Cleansing
• 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
• Use DQS in an SSIS Package

Lab : De-Duplicating Data

• Creating a Matching Policy
• Using a DQS Project to Match Data

Module 10: Using Master Data Services

• Master Data Services Concepts
• Implementing a Master Data Services Model
• Using the Master Data Services Excel Add-in

Lab : Implementing Master Data Services

• Creating a Basic MDS Model
• Editing an MDS Model With Excel
• Loading Data into MDS
• Enforcing Business Rules
• Consuming Master Data Services Data

Module 11: Extending SSISThis module describes how to extend SSIS by using custom scripts and components.Lessons

• Using Custom Components in SSIS
• Using Scripting in SSIS

Lab : Using Scripts and Custom Components

• Using a Custom Component
• Using the Script Task

Module 12: Deploying and Configuring SSIS PackagesThis modules describes how to deploy and configure SSIS packages.Lessons

• Overview of Deployment
• Deploying SSIS Projects
• Planning SSIS Package Execution

Lab : Deploying and Configuring SSIS Packages

• Create an SSIS Catalog
• Deploy an SSIS Project
• Create Environments for an SSIS Solution
• Running an SSIS Package in SQL Server Management Studio
• Scheduling SSIS Packages with SQL Server Agent

Module 13: Consuming Data in a Data WarehouseThis module describes how information workers can consume data from the data warehouse.Lessons

• Using Excel to Analyze Data in a data Warehouse.
• An Introduction to PowerPivot
• An Introduction to Crescent

Lab : Using a Data Warehouse

• Use PowerPivot to Query the Data Warehouse
• Visualizing Data by Using Crescent