SQL Server 2012 Business Intelligence Boot Camp

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1 SQL Server 2012 Business Intelligence Boot Camp Length: 5 Days Technology: Microsoft SQL Server 2012 Delivery Method: Instructor-led (classroom) About this Course 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 2012, implement ETL with SQL Server Integration Services, and validate and cleanse data with SQL Server Data Quality Services and SQL Server Master Data Services. This course helps people prepare for exam Additionally we teach how to empower information workers through self-service analytics and reporting. Students will learn how to implement multidimensional analysis solutions, create PowerPivot and tabular data models, deliver rich data visualizations with PowerView and SQL Server Reporting Services, and discover business insights by using data mining. This course helps people prepare for exam Audience Profile This course is intended for database professionals who need to fulfill a Business Intelligence Developer role. They will need to focus on hands-on work creating BI solutions including Data Warehouse implementation, ETL, data cleansing, create analysis and reporting solutions. Primary responsibilities include: Implementing a data warehouse. Developing SQL Server Integration Services (SSIS) packages for data extraction, transformation, and loading (ETL). Enforcing data integrity by using Master Data Services. Cleansing data by using Data Quality Services. Implementing reporting solutions by using SQL Server Reporting Services. Implementing multidimensional databases by using SQL Server Analysis Services. Creating tabular semantic data models for analysis by using SQL Server Analysis Services. Create visualizations of data by using PowerView. Create data mining solutions by using SQL Server Analysis Services. At Course Completion After completing this course, students will be able to: Describe data warehouse concepts and architecture considerations. Select an appropriate hardware platform for a data warehouse. Design and implement a data warehouse. Implement Data Flow in an SSIS Package. Implement Control Flow in an SSIS Package. Debug and Troubleshoot SSIS packages. Implement an SSIS solution that supports incremental data warehouse loads and changing data. Integrate cloud data into a data warehouse ecosystem infrastructure. Implement data cleansing by using Microsoft Data Quality Services. Implement Master Data Services to enforce data integrity. Extend SSIS with custom scripts and components.

2 Deploy and Configure SSIS packages. Describe how information workers can consume data from the data warehouse. Describe the components, architecture, and nature of a BI solution. Create reports with Reporting Services. Create reusable report items that simplify self-service reporting. Manage report execution and delivery. Create a multidimensional database with Analysis Services. Implement dimensions in a cube. Implement measures and measure groups in a cube. Use MDX Syntax. Customize a cube. Implement a Tabular Data Model in PowerPivot. Use DAX to query a tabular model. Implement a Tabular Database. Use PowerView to create interactive data visualizations. Use Data Mining for Predictive Analysis Module: Introduction to Data Warehousing This module provides an introduction to the key components of a data warehousing solution and the high-level considerations you must take into account when starting a data warehousing project. Overview of Data Warehousing Considerations for a Data Warehouse Solution Lab : Exploring a Data Warehousing Solution Exploring data sources Exploring an ETL solution Exploring a data warehouse Describe the key elements of a data warehousing solution. Describe the key considerations for a data warehousing project. Module: Data Warehouse Hardware This module describes the characteristics of typical data warehouse workloads, and explains how you can use reference architectures and data warehouse appliances to ensure you build the system that is right for your organization. Considerations for Building a Data Warehouse Data Warehouse Reference Architectures and Appliances Describe the main hardware considerations for building a data warehouse. Explain how to use reference architectures and data warehouse appliances to create

3 a data warehouse. Module: Designing and Implementing a Data Warehouse In this module, you will learn how to implement the logical and physical architecture of a data warehouse based on industry-proven design principles. 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 Implementing a Time Dimension Table Implement a logical design for a data warehouse. Implement a physical design for a data warehouse. Module: Creating an ETL Solution with SSIS This module discusses considerations for implementing an ETL process, and then focuses on SQL Server Integration Services (SSIS) as a platform for building ETL solutions. Introduction to ETL with SSIS Exploring Source Data Implementing Data Flow Lab : Implementing Data Flow in a SSIS Package Exploring Source Data Transferring Data by Using a Data Flow Task Using Transformations in a Data Flow Describe the key features of SSIS. Explore source data for an ETL solution. Implement a data flow using SSIS. Module: Implementing Control Flow in an SSIS Package Control flow in SQL Server Integration Services packages enables you to implement complex ETL solutions that combine multiple tasks and workflow logic. This module covers how to implement control flow, and design robust ETL processes for a data warehousing solution that coordinate data flow operations with other automated tasks. Introduction to Control Flow Creating Dynamic Packages

4 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 Implement control flow with tasks and precedence constraints. Create dynamic packages that include variables and parameters. Use containers in a package control flow. Enforce consistency with transactions and checkpoints. Module: Debugging and Troubleshooting SSIS Packages This module describes how you can debug SQL Server Integration Services (SSIS) packages to find the cause of errors that occur during execution. Then module then covers the logging functionality built into SSIS you can use to log events for troubleshooting purposes. Finally, the module describes common approaches for handling errors in control flow and data flow. 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 Debug an SSIS package. Implement logging for an SSIS package. Handle errors in an SSIS package. Module: Implementing an Incremental ETL Process This module describes the techniques you can use to implement an incremental data warehouse refresh process. Introduction to Incremental ETL

5 Extracting Modified Data Loading Modified Data Lab : Extracting Modified Data Using a DateTime Column to Incrementally Extract Data Using a Change Data Capture Using Change Tracking Lab : Loading Incremental Changes Using a Lookup Transformation to Insert Dimension Data Using a Lookup Transformation to Insert or Update Dimension Data Implementing a Slowly Changing Dimension Using a MERGE Statement to Load Fact Data Describe the considerations for implementing an incremental extract, transform, and load (ETL) solution. Use multiple techniques to extract new and modified data from source systems. Use multiple techniques to insert new and modified data into a data warehouse. Module: Incorporating Data from the Cloud into a Data Warehouse In this module, you will learn about how you can use cloud computing in your data warehouse infrastructure and learn about the tools and services available from the Microsoft Azure Marketplace. Overview of Cloud Data Sources SQL Server Database The Windows Azure Marketplace Lab : Using Cloud Data in a Data Warehouse Solution Creating a SQL Azure Database Extracting Data from a SQL Azure Database Obtaining Data from the Windows Azure Marketplace Describe cloud data scenarios. Describe SQL Azure. Describe the Windows Azure Marketplace. Module: Enforcing Data Quality Ensuring the high quality of data is essential if the results of data analysis are to be trusted. This module explains how to use the SQL Server 2012 Data Quality Services (DQS) to provide a computer assisted process for cleansing data values and identifying and removing duplicate data entities.

6 Introduction to Data Quality 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 Using DQS in an SSIS Package Lab : Deduplicating Data Creating a Matching Policy Using a DQS Project to Match Data Describe how Data Quality Services can help you manage data quality. Use Data Quality Services to cleanse your data. Use Data Quality Services to match data. Module: Using Master Data Services This module introduces Master Data Services and explains the benefits of using it in a data warehousing context. The module also describes the key configuration options for Master Data Services, and explains how to import and export data. Finally, the module explains how to apply rules that help to preserve data integrity, and introduces the new Master Data Services Add-in for Excel. Introduction to Master Data Services Implementing a Master Data Services Model Using the Master Data Services Add-in for Excel Lab : Implementing Master Data Services Creating a Basic Model Editing a Model by Using the Master Data Services Add-in for Excel Loading Data into a Model Enforcing Business Rules Consuming Master Data Services Data Describe key Master Data Services concepts. Implement a Master Data Services model. Use the Master Data Services Add-in for Excel to view and modify a model. Module: Extending SQL Server Integration Services This module describes the techniques you can use to extend SQL Server Integration Services (SSIS). The module is not designed to be a comprehensive guide to developing

7 custom SSIS solutions, but to provide an awareness of the fundamental steps required to use custom components and scripts in an ETL process that is based on SSIS. Using Custom Components in SSIS Using Scripts in SSIS Lab : Using Custom Components and Scripts Using a Custom Component Using a Script Task Describe how custom components can be used to extend SSIS. Describe how you can include custom scripts in an SSIS package. Module: Deploying and Configuring SSIS Packages SQL Server Integration Services provides tools that make it easy to deploy packages to another computer. The deployment tools also manage any dependencies, such as configurations and files that the package needs. In this module, you will learn how to use these tools to install packages and their dependencies on a target computer. Overview of SSIS Deployment Deploying SSIS Projects Planning SSIS Package Execution Lab : Deploying and Configuring SSIS Packages Create a 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 Describe SSIS deployment. Explain how to deploy SSIS projects using the project deployment model. Plan SSIS package execution. Module: Consuming Data in a Data Warehouse This module introduces Business Intelligence (BI), describes the components of SQL Server that you can use to create a BI solution, and the client tools that users can use to create reports and analyze data. Introduction to Business Intelligence Introduction to Reporting

8 Introduction to Data Analysis Lab : Using Business Intelligence Tools Exploring a Reporting Services Report Exploring a PowerPivot Workbook Exploring a Power View Report Describe BI and common BI scenarios. Explain the key features of SQL Server Reporting Services. Explain the key features of SQL Server Analysis Services. Module: Introduction to Business Intelligence and Data Modeling This module provides an introduction to Business (BI) Intelligence. It describes common BI scenarios, current trends in BI, and the typical roles that are involved in creating a BI solution. It also introduces the Microsoft BI platform and describes the roles Microsoft SQL Server 2012 and Microsoft SharePoint 2010 play in Microsoft BI solutions. Introduction to Business Intelligence The Microsoft Business Intelligence Platform Lab : Reporting and Analyzing Data Exploring a Reporting Services Repot Exploring a PowerPivot Workbook Exploring a Power View Report Describe common BI scenarios and current BI trends. Describe the main technologies that make up the Microsoft BI platform. Module: Implementing Reports with SQL Server Reporting Services This module discusses the tools and techniques a professional business intelligence developer can use to create and publish reports with SQL Server Reporting Services. Introduction to Reporting Services Creating a Report with Report Designer Grouping and Aggregating Data in a Report Showing Data Graphically Filtering Reports by Using Parameters Publishing and Viewing a Report Lab : Creating a Report with Report Designer Creating a Report

9 Grouping and Aggregating Data Lab : Enhancing and Publishing a Report Adding a Chart to a Report Adding Parameters to a Report Publishing a Report Describe the key features of Reporting Services. Use Report Designer to create a report. Group and aggregate data in a report. Use charts and other visualizations to show data graphically in a report. Use parameters to filter data in a report. Publish and view a report. Module: Supporting Self Service Reporting This module describes Microsoft SQL Server Reporting Services features that you can use to enable self-service reporting. Introduction to Self Service Reporting Shared Data Sources and Datasets Report Parts Lab : Implementing Self Service Reporting Using Report Builder Simplifying Data Access for Business Users Using Report Parts Support self-service reporting with Report Builder. Create shared data sources and datasets for self-service reporting scenarios. Use report parts as reusable report elements. Module: Managing Report Execution and Delivery This module describes how to apply security settings and configure reports for delivery. Managing Report Security Managing Report Execution Subscriptions and Data Alert Troubleshooting Reporting Services Lab : Configuring Report Execution and Delivery Configuring Report Execution Implementing a Standard Subscription

10 Implementing a Data-Driven Subscription Configure security settings for a report server. Configure report execution settings to optimize performance. Use subscriptions and alerts to automate report and data delivery. Troubleshoot reporting issues. Module: Creating Multidimensional DatabasesThe fundamental purpose of using SQL Server Analysis Services online analytical processing (OLAP) solutions is to build cubes that you can use to perform complex queries and return the results in a reasonable time. This module provides an introduction to multidimensional databases and introduces the core components of an OLAP cube. Introduction to Multidimensional Analysis Creating Data Sources and Data Source Views Creating a Cube Overview of Cube Security Lab : Creating a Multidimensional Database Creating a Data Source Creating and Modifying a Data Source View Creating and Modifying a Cube Describe the considerations for a multidimensional database. Create data sources and data source views. Create a cube. Implement security in a multidimensional database. Module: Working with Dimensions In SQL Server Analysis Services, dimensions are a fundamental component of cubes. This module provides an insight into the creation and configuration of dimensions and dimension hierarchies. Configuring Dimensions Defining Attribute Hierarchies Sorting and Grouping Attributes Lab : Defining Dimensions Configuring Dimensions Defining Relationships and Hierarchies Sorting and Grouping Dimensions Attributes

11 Configure dimensions. Define attribute hierarchies. Sort and group attributes. Module: Working with Measures and Measure Groups A measure represents a column that contains quantifiable data, usually numeric, that you can aggregate. This module describes measures and measure groups. The module also explains how you can use measures to define fact tables and associate dimensions. Working with Measures Working with Measure Groups Lab : Configuring Measures and Measure Groups Configuring Measures Defining Dimension Usage and Relationships Configuring Measure Group Storage Describe measures. Describe measure groups. Module: Introduction to MDX Multidimensional Expressions (MDX) is the query language that you use to work with and retrieve multidimensional data in SQL Server Analysis Services. This module describes the fundamentals of MDX. It also explains how to build calculations, such as calculated members and named sets. MDX Fundamentals Adding Calculations to a Cube Using MDX to Query a Cube Lab : Using MDX Querying a Cube by Using MDX Creating a Calculated Member Describe MDX. Add calculations to a cube. Describe how to use MDX in client applications. Module: Customizing Cube Functionality In this module, you will learn how to customize cube functionality by using several technologies available to you in SQL Server Analysis Services. These technology customizations include: Key Performance Indicators, Actions, Perspectives, and

12 Translations. Working with Key Performance Indicators Working with Actions Working with Perspectives Working with Translations Lab : Customizing a Cube Implementing an Action Implementing a Perspective Implementing a Translation Describe Key Performance Indicators. Implement Actions. Explain Perspectives. Describe Translations. Module: Implementing a Tabular Data Model with Microsoft PowerPivot This module introduces tabular data models, explains how to install and use the PowerPivot for Excel add-in, and describes how to share a workbook to PowerPivot Gallery. Introduction to Tabular Data Models and PowerPivot Technologies Creating a Tabular Data Model by Using PowerPivot for Excel Sharing a PowerPivot Workbook and Using PowerPivot Gallery Lab : Using PowerPivot for Excel Creating a Tabular Data Model by Using PowerPivot for Excel Using a Tabular Data Model in Excel Sharing a PowerPivot Workbook to PowerPivot Gallery Using a PowerPivot Workbook as a Data Source Describe the key features and benefits of tabular data models and PowerPivot technologies. Create a PowerPivot for Excel workbook. Share a PowerPivot for Excel workbook to PowerPivot Gallery and use a PowerPivot for Excel workbook as a data source. Module: Introduction to DAX This module covers the fundamentals of the DAX language. It also explains how you can use DAX to create calculated columns and measures, and how you can use these in your tabular data models.

13 DAX Fundamentals Using DAX to Create Calculated Column and Measures in a Tabular Data Model Lab : Creating Calculated Columns and Measures by Using DAX Creating Calculated Columns Creating Measures Using Time Intelligence Creating a Dynamic Measure Describe the fundamentals of DAX. Use DAX to create calculated columns and measures. Module: Implementing an Analysis Services Tabular Data Model With SQL Server 2012, you can install Analysis Services in Tabular mode and create tabular data models that information workers can access by using tools such as Excel and Power View. This module describes Analysis Services tabular data models and explains how to develop a tabular data model by using the SQL Server Data Tools. Introduction to Analysis Services Tabular Data Model Projects Developing an Analysis Services Tabular Data Model in SQL Server Data Tools Lab : Working with an Analysis Services Tabular Data Model Creating an Analysis Data Services Tabular Data Model from a PowerPivot Workbook Implementing a Perspective Implementing Partitions Deploying an Analysis Services Tabular Data Model Enabling Access to a Tabular Data Model Configuring DirectQuery Storage Model Implementing Security in a Tabular Data Model Describe Analysis Services tabular data model Projects. Implement an Analysis Services tabular data model by Using SQL Server Data Tools. Module 13: Creating Data Visualizations with Power View SQL Server 2012 introduces Power View, a SharePoint-based data exploration tool that provides a way for information workers to interactively create data visualizations that help them to better understand the data that they are working with. This module introduces Power View and describes how you can use it to create a range of different types of reports quickly and easily. Introduction to Power View

14 Visualizing Data with Power View Lab : Creating Data Visualizations with Power View Modify the Tabular Data Model Create a Simple Power View Report Using Interactive Visualizations Create a Scatter Chart and a Play Axis Describe the Power View and its place in the BI ecosystem. Create data visualizations by using Power View. Module: Performing Predictive Analysis with Data Mining SQL Server Analysis Services includes data mining tools that you can use to identify patterns in your data, helping you to determine why particular things happen and to predict what will happen in the future. This module introduces data mining, describes how to create a data mining solution, how to validate data mining models, how to use the Data Mining Add-ins for Excel, and how to incorporate data mining results into Reporting Services reports. Overview of Data Mining Creating a Data Mining Solution Validating a Data Mining Solution Consuming a Data Mining Solution Lab : Using Data Mining to Support a Marketing Campaign Using Table Analysis Tools Creating a Data Mining Model Using the Data Mining Add-in for Excel Validating Data Mining Models Using a Data Mining Model in a Report Describe the key data mining concepts and use the Data Mining Add-ins for Excel. Create a Data Mining solution. Validate data mining models. Use data mining data in a report.

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