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- 1. PowerBI SELF-SERVICE BUSINESS INTELLIGENCE FOR THE DEVELOPER Power Query, PowerPivot, Power View, Power Map, Power What…?
- 2. Hi, I’m Jeff… Founding partner Vanishing Clouds Microsoft partner for 15 years; small business/development MCTS, MCSE, MCAD, … vNext OC Treasurer
- 3. Arch of Microsoft’s BI solutions Cubes (Multi-Dimensional) vs. Tabular PowerBI is “just Excel” PowerBI Desktop Power Query and “M” PowerPivot and DAX
- 4. Microsoft’s BI Spectrum Excel • Excellent visuals • Limited scale, etc. Power* COM add-ins • Powerful ETL • “Stairway” to DAX • Interactive, easy visuals Standalone • “Just” Power* • Monthly sprints SSAS • Tabular vs. Multi-Dimensional • Install-time decision • New language, terminology Hadoop/HDInsight • Big Data (in the cloud) • Open source, MSFT commits • “Divide and concur” using commodity servers
- 5. Problems with Just Excel Joining tables via VLOOKUP Scale Shaping – preprocess with SQL, manual workflow or just copy/paste Sources – “modern” REST/web Smarts – business logic beyond a pivot table
- 6. Tables vs. Cubes GL Accounts Region Balance Sales North America 100 COGS North America 72 SG&A North America 10 Op Profit North America 18 Sales South America 58 COGS South America 48 SG&A Far East 6 Net Profit Far East 12 Regions GL Accounts North America South America Far East Sales 100 58 COGS 72 48 SG&A 10 6 Net Profit 12 Op Profit 18 (pivot) 2D GL Accounts Region Scenario Balance Sales North America Actual 100 COGS North America Actual 72 SG&A North America Actual 10 Op Profit North America Actual 18 Sales South America Actual 58 COGS South America Actual 48 ŸŸŸ SG&A Far East Budget 6 Net Profit Far East Budget 12 3D GL Accounts Region District Scenario Balance Sales North America NE Actual 25 COGS North America NE Actual 12 ŸŸŸ ? Hierarchy GL Accounts Region District Scenario Date Balance Sales North America NE Actual Jan 01 25 COGS North America NE Actual Jan 01 12 ŸŸŸ Hyper-Cube
- 7. Multi-Dimensional Cubes Technology from Panorama; Israeli/Canadian High performance; e.g., pre-calculated subtotals Sum quarters, then any YTD is adding at most 5 terms Specialized vocabulary Facts vs. dimensions vs. measures; star vs. snowflake MDX is widely seen as difficult to learn Jan Feb Mar Apr May Jun Jul Q1 Q2 Given: SUM: YTD July: Q1 Q2 Jul
- 8. Newer Tabular Model Technology from Vertipaq (xVelocity) Relational (like) – FKs, one-to-many, etc. Familiar “Good enough” performance DAX is “hard enough” to learn Tabular Model DirectQuery In-Memory Third Party Application Excel Power View Reporting Services ODataFiles Cloud Services SQL Server Databases Non SQL Server Databases
- 9. Power* Power Query PowerPivot Power View/Map Role Discover Analyze Visualize Language “M” DAX N/A Technology Oslo DSL In-memory (xVelocity) Silverlight! XL10/13 Install Install (COM add-in) COM Install (COM) XL16/Future Integrated (replace import?) Integrated - Tab Integrated – on Insert PowerBI Subscription Adds: • SharePoint site with engine/preview and some editing (10MB 250MB, refresh) • “Data steward” concerns: shared queries and searchable data catalog; gateway to on-prem • Mobile • Q&A – natural language
- 10. Demos Raw Excel Web Scraping Combining source: Excel and OData Slicers; Timelines
- 11. Power Query E – Data Access Excel – any table (not region) PQ provides connections, but doesn’t “use” them Relational – added OLE DB/ODBC; can include instance/db/SQL Fast Load (a.k.a. query folding) and permissions (cred cached in machine local store) CSV/Text (including JSON)/File System (includes folders) Web – general (tables) and MSFT “indexed” like Wikipedia Optimized for GET and tables Online Search for MSFT’s and “your” catalog OData, includes SP Azure – credentials, BLOb storage Other sources – Exchange, AD, Facebook, SAP, …
- 12. Power Query T – Informally “M” Functional, strongly-typed, domain specific language More similar to Excel functions that OOP “Control flow” (if…then…else and try…otherwise) are functions Comments // and /*…*/ Structured data types: List – an ordered sequence { … } (special form: {1..10}); also indexes Record – “one row” of named fields [«name» = «value»]; selects field Table – most important #table() function Example: Source = OData.Feed("http://...svc/ "), Orders_table = Source{[Name="Orders", Signature="table"]}[Data], https://msdn.microsoft.com/en-us/library/mt211003.aspx
- 13. Power Query L - Connections Don’t load to Excel unless you “have” to Immutable (can’t change after first close) Later Refreshing Permissions – “cannot” mix Public/Organization/Private Fast Load Publish to PowerBI portal: https://app.powerbi.com
- 14. PowerPivot Model The Excel data model—a “hidden layer” above Excel From Data or PowerPivot tabs Column-store technology compresses most data well Scales to millions of rows (in XL13+ only limited by RAM) Hint: Bypass Excel when loading from Power Query Business data types (Address, URL) Direct support for KPIs Excellent time functionality—but BYOC (bring your own calendar)
- 15. Data Analysis eXtensions Simpler (tabular) than MDX; “part way” between M and Excel Syntax “reversed” from M: [] around columns Statically typed with liberal coercion: "1"+1 = 2; "1"&1 = "11" Calculated Columns vs. Calculated Fields (nee Measures) It’s all about the evaluation context: Row context – typically for calculated columns Filter context – typically for calculated fields/measures (think in a PivotTable) Powerful functions like SUMX and CALCULATE Related() and RelatedTable() go one-to-many but not many-to-one In-memory vs. Direct Query
- 16. We Haven’t Covered DAX and SSAS’s Tabular mode SSAS Multi-Dimensional mode (pros/cons) PowerBI Service Dashboard (SharePoint portal) Data Steward and Shared/Recommended Queries On-premise data (gateway) and refreshing models Mobile Q&A Natural Language Reporting (much)
Editor's Notes
- Microsoft offers a wide spectrum of Business Intelligence tools, from individual users running “just Excel” through the Power* suite, to SQL Server Analysis Server and Azure solutions for big data.
- While “just Excel” is the most popular tool for data analysis it has issues: no “INNER JOIN” (although VLOOKUP kinda does that); it is limited to 1 million rows, often requires complex SQL to shape the data (or manual copy/paste workflows), etc.
- Cubes are the “traditional” way to do BI. At it’s simplest, a cube is like pivoting normalized data. Conceptually, the dimensions of a cube correspond to the keys of a normalized DB. (Typically we use the natural, often denormalized, keys which are more familiar to end users.) For 2D—that is 2 keys, we’re used to using a PIVOT function to show rows X columns. With 3 keys, we conceptual extend this pivot to 3D. Cubes also excel at dealing with hierarchies (geographies often “nest” as do dates; e.g., date, month, quarter, year). When we get to 4 or more keys/dimensions it gets tough to visualize but it’s the same idea—just a hyper-cube.
- Multi-dimensional BI offers high-performance but is generally considered difficult to learn, with specialized vocabulary, concepts and languages. One simple illustration of its power: by precomputing values along each “dimension” reports can be much quicker to deliver. A simple illustration is pre-summing each quarter’s total. Obviously if the user asks for a quarter’s subtotal, this is much faster. But it’s also faster to SUM(Jan...Jul), that is the year-to-date for July. Instead of summing all 7 months, you can add two quarters plus a month. This case doesn’t save a lot, but you can imagine pre-calculating lots of sums could save a lot of CPU time when reporting—at the cost of extra storage (and complexity). The “trick” becomes knowing when it’s worth doing a subtotal—and SSAS traditionally has lots of tools to let DBAs/data stewards trade off time vs. space, control when subtotals are refreshed, etc.
- The tabular model was introduced in Excel 2010 and SSAS 2008 R2. It tries to simplify the terminology/technology—or at least use more familiar concepts.