Data Mashup with Microsoft Excel Using Power Query and M

Master the art of loading external data into Excel for use in reporting, charting, dashboarding, and business intelligence. This book provides a complete and thorough explanation of Microsoft Excel's Get and Transform feature set, showing you how to connect to a range of external databases and other data sources to find data and pull that data into your local spreadsheet for further analysis. Leading databases are covered, including Microsoft Azure data sources and web sources, and you will learn how to access those sources from your Microsoft Excel spreadsheets.
Getting data into Excel is a prerequisite for using Excel's analytics capabilities. This book takes you beyond copying and pasting by showing you how to connect to your corporate databases that are hosted in the Azure cloud, and how to pull data from Oracle Database and SQL Server, and other sources.
Accessing data is only half the problem, and the other half involves cleansing and rearranging your data to make it useful in spreadsheet form. Author Adam Aspin shows you how to create datasets and transformations. For advanced problems, there is help on the M language that is built into Excel, specifically to support mashing up data in support of business intelligence and analysis. If you are an Excel user, you won't want to be without this book that teaches you to extract and prepare external data ready for use in what is arguably the world's leading analytics tool.

What You Will Learn

• Connect to a range of external data, from databases to Azure sources
• Ingest data directly into your spreadsheets, or into PowerPivot data models
• Cleanse and prepare external data so it can be used inside Excel
• Refresh data quickly and easily to always have the latest information
• Transform data into ready-to-use structures that fit the spreadsheet format

• Execute M language functions for complex data transformations
  Who This Book Is For
  Excel users who want to access data from external sourcesincluding the Microsoft Azure platformin order to create business intelligence reporting, dashboards, and visualizations. For Excel users needing to cleanse and rearrange such data to meet their own, specific needs.

  Makes using external data in Excel truly simple Covers a range of data sources, including all the leading databases Provides clear and coherent explanations with extensive descriptive images

  Autorentext

  Adam Aspin is an independent business intelligence consultant based in the United Kingdom. He has worked with SQL Server for over 25 years. During this time, he has developed several dozen reporting and analytical systems based on the Microsoft analytics stack.
  Business intelligence has been Adam's principal focus for the last 20 years. He has applied his skills for a variety of clients in a range of industry sectors. He is the author of Apress books: SQL Server Data Integration Recipes, Pro Power BI Desktop (now in its third edition), Business Intelligence with SQL Server Reporting Services, and High Impact Data Visualization.
  A graduate of Oxford University, Adam began his career in publishing before moving into IT. Databases soon became a passion, and his experience in this arena ranges from dBase to Oracle, and Access to MySQL, with occasional sorties into the world of DB2. He is, however, most at home in the Microsoft universe when using SQL Server Analysis Services, SQL Server Reporting Services, and SQL Server Integration Services and Power BI (both on-premise and in Azure).
  A fluent French speaker, Adam has worked in France and Switzerland for many years.

  Zusammenfassung
  Intermediate-Advanced user level

  Inhalt

  1. Using Power Query to Discover and Load Data into Excel.- 2. Discovering and Loading File-Based Data with Power Query.- 3. Loading Data from Databases and Data Warehouses.- 4. Loading Data from the Web and the Cloud.- 5. Generic Data Sources.- 6. Structuring Imported Data.- 7. Data Transformation.- 8. Restructuring Data.- 9. Complex Data Loads.- 10. Organizing and Managing Queries.- 11. Parameterizing Queries.- 12. The M Language.- 13. Appendix A: Sample Data.