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Andrew Fryer Technical Evangelist Microsoft UK Ltd Mission Critical BI Behind every cloud is another cloud The cloud infrastructure is made available to the general public or a large industry group and is owned by an organization selling cloud services. The cloud infrastructure is operated solely for an organization. It may be managed by the organization or a third party and may exist on premise or off premise. The cloud infrastructure is a composition of two or more clouds (private, community, or public) that remain unique entities but are bound together by standardized or proprietary technology that enables data and application portability (e.g., cloud bursting for loadbalancing between clouds). SQL Azure SQL Server 2012 Azure Data Sync Azure Reporting Services Appliances Azure Data Market Azure Storage © 2011, Enterprise Strategy Group, Inc. All Rights Reserved. PROTEC T DATA CONTRO L ACCESS ENSURE COMPLIANCE Protect data-at-rest Data/Key separation Use strong authentication Monitor all activity Transparent Data Encryption Crypto Enhancements Extensible Key Managements Kerberos authentication enhancements SQL Server Audit Change Data Capture Detect non-compliant configurations Policy-Based Management Industry Certification Common Criteria Certification (EAL4+) User-Defined Server Roles Audit Resilience Default Schema for Groups Audit in all SKUs User-Defined Audit Contained Database Authentication Audit Filtering T-SQL Stack Info Mission Critical BI Cloud AlwaysOn protects multiple databases at once Always On provides read only copies of databases BI Mission Critical Cloud Lies damn lies and statistics Microsoft BI components Business User Experience Business Collaboration Platform Data Infrastructure & BI Platform Flexible: Familiar: Rigid: Powerful: Near real time: Hard: Secure: Control: Can model anything Enterprise scale Fine grain control Lots of expertise 15 minute refresh Process & versions hard to change MDX isn’t easy Easy to Use: users get it Mashups: make your own Agile: Deploy in a click or 2 Collaboration: Between IW & IT Chaos: IT can’t manage it Refresh: Could be a day out No Downsides Easy to Use: Collaboration: Powerful: Mashups: Secure: Cool: Agile: Control: users get it make your own Deploy in a click or 2 Between IW & IT Fine grain control Process & versions Enterprise scale End user analysis 17 18 BIN 202, BINHOL271 Crescent only works from Tabular BISM for now Crescent and alerting depend on SharePoint Silverlight based 21 BIN 302, 311 Architecture Usage Unified Dimensional Model Report Model PowerPivot BI Semantic Model Reporting 3rd Party apps Excel PowerPivot SharePoint BI Semantic Model Data model Multi-dimensional Tabular MDX DAX Business logic & queries Data access Databases LOB Applications ROLAP MOLAP Files VertiPaq OData Feeds Direct Query Cloud Services Tools BISM Reporting Services Data Feeds Data Sets Align to the Azure Data Market Still in BIDS Still in Excel The case for the data warehouse Still need to combine and refine source data Improve consistency and accuracy Capture History Near real time BI Data Quality Services Reference Data Services SSIS Data Flow Values/Rules Source + Mapping Data correction Destination Component New Records Corrections & Suggestions Reference Data Definition Correct Records SSIS Package Invalid Records New MDS Interface MDS Excel add-in Crawlers api Index server Interfaces Discover Barcelona Inventory Barcelona Cleanse, match DQS Data Sources Acquire SSIS Curate MDS Match, de-duplicate DQS Publish SSIS IS DQS MDS Barcelona Columnstore Uses VertiPaq compression Row store: … Column store: C1 C2 C3 C4 C5 C6 Columnstore Performance Performance Illustration 32-logical processors 256GB RAM, 1Tb database with 1.4 billion rows Total CPU time Elapsed time Columnstore 31.0 1.10 No columnstore 502 501 Speedup 16X 455X Column store whitepaper select w_city, w_state, d_year, SUM(cs_sales_price) as cs_sales_price from warehouse, catalog_sales, date_dim where w_warehouse_sk = cs_warehouse_sk and cs_sold_date_sk = d_date_sk and w_state in ('SD','OH') and d_year in (2001,2002,2003) group by w_city, w_state, d_year order by d_year, w_state, w_city;