Get an overview of the components of a business intelligence (BI) solution from an enterprise to a personal level, from simple to the most advanced types of analyses, and get familiar with the BI in SQL Server 2016 suite, Power BI, and other Microsoft tools.

Attendees should have basic understanding of data analysis and relational data models. They should have experience with at least some of the following tasks:

• Reporting from a line of business system
• On-line analytical processing
• Data warehousing
• Data quality

• Describe data warehousing components of the SQL Server Database Engine
• Understand benefits and limitations of reporting with SQL Server Reporting Services
• Define the role of SQL Server Integration Services in the extract-transform-load procedures
• Evaluate the data quality issues and possible resolutions
• Make a decision when to switch from reporting to on-line analytical processing
• Find the role of personal business intelligence in their ecosystems
• Asses the need for advanced analyses with data mining and / or machine learning
• Deliver a simple BI solution
• Define the architecture, the design, and the tools for a more advanced BI solution

Module 1.  Introduction to BI

Module 2.  Data warehousing with SQL Server

Module 3.  Extract-transform-load (ETL) with SQL Server Integration Services (SSIS)

Module 4.  Enhancing and maintaining data quality with Data Quality Services (DQS) and Master Data Services (MDS)

Module 5.  Reporting with SQL Server Reporting Services (SSRS)

Module 6.  On-line analytical processing (OLAP) with SQL Server Analysis Services (SSAS) Multidimensional

Module 7.  Small and medium business (SMB) and personal OLAP with SSAS Tabular and Power Pivot

Module 8.  Personal ETL and EIM with Power Query and Excel with MDS and Fuzzy Lookup Add-ins

Module 9.  SMB and personal ad-hoc and mobile reporting, and creating dashboards with Power View, Power Map, Power BI Services, and Power BI Desktop

Module 10.  Data mining and machine learning with SSAS Multidimensional, Excel, R, and Azure ML