The information herein is for informational purposes only and represents the opinions and views of Project Botticelli and/or Rafal Lukawiecki. The material presented is not certain and may vary based on several factors. Microsoft makes no warranties, express, implied or statutory, as to the information in this presentation. Portions © 2012 Project Botticelli Ltd & entire material © 2012 Microsoft Corp unless noted otherwise. Some slides contain quotations from copyrighted materials by other authors, as individually attributed or as already covered by Microsoft Copyright ownerships. All rights reserved. Microsoft, Windows, Windows Vista and other product names are or may be registered trademarks and/or trademarks in the U.S. and/or other countries. The information herein is for informational purposes only and represents the current view of Project Botticelli Ltd as of the date of this presentation. Because Project Botticelli & Microsoft must respond to changing market conditions, it should not be interpreted to be a commitment on the part of Microsoft, and Microsoft and Project Botticelli cannot guarantee the accuracy of any information provided after the date of this presentation. Project Botticelli makes no warranties, express, implied or statutory, as to the information in this presentation. E&OE. This seminar is based on a number of sources including a few dozen of Microsoft-owned presentations, used with permission. Thank you: Chris Volckerick, Henrik Kraght, Phil Battersby, Peter Simpson, Arun Justus, TK Anand, Kuleen Bharadwaj, Michael Tejedor, and Diwakar Rajagopal.

Yahoo! Hadoop Cluster, Source: developer.yahoo.com, 2007

“Buster Cluster”, an early research project by Miles Osborne, University of Edinburgh, School of Informatics. Picture used with permission.

// Map Reduce function in JavaScript // var map = function (key, value, context) { var words = value.split(/[^a-zA-Z]/); for (var i = 0; i < words.length; i++) { if (words[i] !== "") { context.write(words[i].toLowerCase(), 1); } }; var reduce = function (key, values, context) { var sum = 0; while (values.hasNext()) { sum += parseInt(values.next()); } context.write(key, sum); }; // Map Reduce function in JavaScript // var map = function (key, value, context) { var words = value.split(/[^a-zA-Z]/); for (var i = 0; i < words.length; i++) { if (words[i] !== "") { context.write(words[i].toLowerCase(), 1); } }; var reduce = function (key, values, context) { var sum = 0; while (values.hasNext()) { sum += parseInt(values.next()); } context.write(key, sum); };

Build on the strengths and success of Analysis Services and expand its reach to a much broader user base Embrace the relational data model – well understood by developers and IT Pros Bring together the relational and multidimensional models under a single unified BI platform— best of both worlds! Provide flexibility in the platform to suit the diverse needs of BI applications

BI Semantic Model Data Model Business Logic/Queries Data Access ROLAP MOLAP VertiPaq Direct Query MDXDAX Multi- dimensional Tabular Your Apps Reporting Services & Power View Excel PowerPivot DatabasesLOB ApplicationsFilesOData FeedsCloud Services SharePoint Insights

New applications New technology options Existing applications Based on Unified Dimensional Model Existing applications Every UDM becomes a BI Semantic Model Existing applications Based on Unified Dimensional Model

BI Semantic Model Data model Business logic and queries Data access SQL ServerDynamics CRM End User Model Developer VertiPaq DAX Tabular

BI Semantic Model Data model Business logic and queries Data access MDXDAX Multi- dimensional Tabular VertiPaq End User Model Developer SQL ServerDynamics CRM

MOLAP MDX Multi-dimensional BI Semantic Model Data model Business logic and queries Data access End User Model Developer OracleSAP

MOLAP DAX MDX Multi- dimensional Tabular BI Semantic Model Data model Business logic and queries Data access End User Model Developer OracleSAP

 Rich data modeling  Sophisticated business logic using MDX and DAX  Fine-grained security – row/cell level  Multi- dimensional or tabular modeling  MDX and DAX for logic and queries  Cached and passthrough storage  VertiPaq for high performance, MOLAP for mission critical scale  DirectQuery and ROLAP for real-time  State-of-the-art compression algorithms FlexibilityRichnessScalability

Analysis Services Architecture Excel Services PowerPivot for SharePoint Excel Browser SharePoint Reporting Services Apps PowerPivot for Excel xlsx SQL Server Data Tools Personal BI Team BI Organizational BI Analysis Services BI Semantic Model