SQL Server 2017 Business Intelligence: Integration Services and Analysis Services
Duration: 5 Days (Face-to-Face or Remote-Live), or 35 hours of coursework with personal facilitation over a four week span (On-Demand)
US Price: $2495 (Face-to-Face or Remote-Live), or $1495 (On-Demand)
Delivery Options: Attend face-to-face in the classroom, remote-live or via on-demand training
Registration: Click here to view upcoming schedules and register for face-to-face sessions, click here to register for live remote attendance sessions, or click here to register for on-demand training with a start date that is convenient for you.
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SQL Server 2017 provides a rich environment for business intelligence development. The focus of this course is to familiarize developers with the use of the SQL Server Database Engine, SQL Server Integration Services (SSIS), and SQL Server Analysis Services (SSAS) for business intelligence purposes. Students will design an OLAP database utilizing a star schema (Data Warehouse), and then populate it using ETL routines designed in SSIS. After the data warehouse has been populated, SSAS will be used to build both Multidimensional and Tabular models that will serve as reporting data sources.
Students will learn how to design and build data warehouses and marts using SQL Server Management Studio. In a series of exercises, students develop SSIS packages designed to maintain a data warehouse using the Data Flow control flow task. Also demonstrated are other control flow tasks that can interact with an NTFS file system, FTP server, execute Win32 processes, send emails, and run .NET scripts.
Based on the populated data warehouse they have created, students will then learn how to develop both Multidimensional and Tabular SSAS models using the languages Multidimensional Expressions (MDX) and Data Analysis Expressions (DAX). Cubes will be customized to include Key Performance Indicators (KPIs), Calculated Members, Named Sets, Navigational Hierarchies, and Perspectives.
Familiarity with database concepts, Windows desktop navigation and Transact-SQL. Attendance at our Microsoft Transact-SQL Programming course is highly recommended although not required.
Business Intelligence Framework Overview
- SQL Server Data Tools Overview
- Installation and Configuration
- Components of a BI Solution
- Introduction to the BI Semantic Model
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Integration Services Architecture
- Architecture of the SSIS Data Engine
- Using Data Transformation Tasks
- Managing Connections to Sources and Destinations
- ADO.NET Data Source and Destination
- Understanding Data Buffers
- Control Flow Tasks and Containers
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Common SSIS Tasks
- Executing SQL Statements
- Connecting to FTP Servers
- Sending E-mail
- Notifying Administrators of Errors
- Completing Bulk Inserts
- Copying, Moving and Deleting Files and Folders
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Data Transformations
- Converting Data Types
- Merging Data from Multiple Sources
- Splitting Data to Multiple Destinations
- Counting Rows
- Sampling and Sorting Records
- Copying Columns
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Advanced SSIS Tasks
- Executing .NET Scripts and Win32 Processes
- Using the Windows Management Instrumentation (WMI) Tasks
- Performing Database Maintenance and Backups During SSIS Routines
- Using Variables and Input Parameters
- Profiling Database Tables
- Comparing XML Files Against Schemas
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Advanced Data Transformations
- Filling in Missing Data with Lookups
- Locating Near Duplicate Rows with Fuzzy Grouping
- Adding Audit Information to Results
- Counting the Occurrence of Keywords
- Sending Rows that Process Correctly and Incorrectly to Different Destinations
- Responding to Truncation Errors
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SSIS Administration and Automation
- Deploying SSIS Projects
- Manually Running SSIS Tasks
- Automating SSIS Package Execution
- Configuring Notifications for Execution Success, Failure or Both
- SSIS Security
- Troubleshooting Techniques
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Data Warehouse Design
- Understanding Fact and Dimension Tables
- Modeling Slowly Changing and Rapidly Changing Dimensions
- Modeling Fact Tables
- Using Star and Snowflake Schemas for Dimension Tables
- Implementing Surrogate Keys
- Defining Business Keys
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Creating and Populating Data Warehouses
- Creating Data Warehouses (OLAP Databases)
- Adding Fact Tables
- Adding Dimension Tables and Joining Them to Fact Tables
- Loading Data into Fact and Dimension Tables
- Validation Techniques for Data Loads
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Creating and Managing Cubes
- Creating Data Sources to Connect to Data Warehouses
- Using SSAS to Create Cubes
- Applying Friendly Names to Measures and Attributes
- Customizing Dimensions and Measures
- Setting up Navigational Hierarchies
- Optimizing Cubes with Attribute Relationships
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Multidimensional (MDX) Essentials
- Using MDX Queries to Pull Data from Cubes
- Understanding Tuples and Sets
- MDX Expressions vs. Queries
- Grouping Attribute Values into Named Sets
- Adding Custom Calculations for Cubes Using MDX
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MDX Functions
- Using MDX Aggregate Functions
- Using Navigations Functions to Move Though Hierarchies
- Grouping, Filtering and Sorting Functions
- Time-Based MDX Functions
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Customizing Cubes
- Adding Key Performance Indicators (KPIs)
- Customizing Dimensions and Attributes
- Adding Translations to Support Multiple Languages
- Adding Custom Calculations
- Subdividing Cubes Using Perspectives
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Cube Deployment and Administration
- Cube Storage Calculations
- Configuring Desired Aggregation
- Configuring Caching
- Deploying and Processing Cubes
- Connecting to Cubes from Excel and Other Clients
- Partitioning and Processing Cubes
- Backing Up and Restoring Options
- Securing Cubes
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Creating and Customizing Tabular Models
- Creating Tabular Modules in SSDT
- Introducing DAX
- Customizing Tabular Models
- Refreshing Data in Tabular Models
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Understanding the Data Mining Process
- Types of Business Analysis Supported by Data Mining
- Data Mining Process Explained
- Understanding the Key Components of Data Mining
- Using Built-In Data Mining Algorithms
- Matching Mining Algorithms to Business Needs
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Working with Data Mining Structures
- Adding Data Mining Structures
- Mining for Hidden Information
- Discovering Patterns in Data
- Creating Predictive Models
- Using the Data Mining Wizard
- Modifying Mining Structures with the Data Mining Designer
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Using the Semantic Models in the Presentation Layer
- Using SSAS Data Sources in Excel and Power View
- Using SSAS Data Sources in SSRS
- Using SSAS Data Sources in Power BI
- Using SSAS Data Sources in SharePoint Performance Point Services
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