Este documento describe las características de Business Intelligence en SQL Server 2008. Explica brevemente las dimensiones de BI, SQL Server BI y cómo los datos y la inteligencia se integran. Luego muestra una demostración de cómo consumir un cubo desde Microsoft Excel. Finalmente, discute las próximas características de BI en SQL Server, incluidas las actualizaciones de SQL Server 2008 R2 y las nuevas capacidades de autoservicio de BI.
24. Miembros OLAP. Dominando las bases Dimensión Producto Electro Bazar Medidas Bebidas Alimentos Cantidad de clientes 200 Ventas 3,000,000 Utilidad 500,000 Crecimiento 15% Directa Dimensión Canal de Ventas Mayorista Minorista Otros Abril Marzo Febrero Enero Tiempo
26. SQL Server BI El Sistema Nervioso de nuestros Datos SQL Server 2008AnalysisServices SQL Server 2008IntegrationServices SQL Server 2008ReportingServices SQL Server 2008RDBMS
27. SQL Server BI El Sistema Nervioso de nuestros Datos SSIS 2008 SQL Server 2008IntegrationServices RDMS
28. SQL Server BI El Sistema Nervioso de nuestros Datos SQL Server 2008AnalysisServices Datamarts y Data Warehouse Analysis Services Minería de datos
29. SQL Server BI El Sistema Nervioso de nuestros Datos SQL Server 2008ReportingServices
5 minMicrosoft® SQL Server® Project code-named “Madison” is a highly scalable data warehouse appliance that delivers performance at low cost through a massively parallel processing (MPP).“Madison” is a highly scalable appliance for enterprise data warehousing. It is the next step in the evolution of the data warehouse appliance created by DATAllegro. Madison uses massively parallel processing (MPP) to deliver the high performance and scalability on SQL Server 2008, Windows Server® 2008 and industry-standard hardware. The MPP architecture helps enable better scalability, better and more predictable performance, reduced risk and a lower cost per terabyte than other DW solutions.As a component of service oriented architectures (SOA), SQL Server StreamInsight analyzes streams of event data and helps respond to patterns in these event streams. StreamInsight can combine with SOA platforms such as BizTalk Server to listen to and track events from multiple data sources. The patterns detected by StreamInsight then can be analyzed and used to make better business decisions.SQL Server StreamInsight’s ability to derive insights from data streams and act in near real time provides significant business benefits. With increasing volume and speed of data, customers are looking for ways to better utilize and act on this information in real-time. We are delivering a solution as part of our data platform to help address these customer needs. Some of the possible scenarios include:Algorithmic trading and fraud detection for financial servicesIndustrial process control (chemicals, oil and gas) for manufacturingElectric grid monitoring and advanced metering for utilitiesClick stream web analyticsNetwork and data center system monitoring.
5 min Microsoft® SQL Server® Project code-named “Madison” is a highly scalable data warehouse appliance that delivers performance at low cost through a massively parallel processing (MPP).“Madison” is a highly scalable appliance for enterprise data warehousing. It is the next step in the evolution of the data warehouse appliance created by DATAllegro. Madison uses massively parallel processing (MPP) to deliver the high performance and scalability on SQL Server 2008, Windows Server® 2008 and industry-standard hardware. The MPP architecture helps enable better scalability, better and more predictable performance, reduced risk and a lower cost per terabyte than other DW solutions.As a component of service oriented architectures (SOA), SQL Server StreamInsight analyzes streams of event data and helps respond to patterns in these event streams. StreamInsight can combine with SOA platforms such as BizTalk Server to listen to and track events from multiple data sources. The patterns detected by StreamInsight then can be analyzed and used to make better business decisions.SQL Server StreamInsight’s ability to derive insights from data streams and act in near real time provides significant business benefits. With increasing volume and speed of data, customers are looking for ways to better utilize and act on this information in real-time. We are delivering a solution as part of our data platform to help address these customer needs. Some of the possible scenarios include:Algorithmic trading and fraud detection for financial servicesIndustrial process control (chemicals, oil and gas) for manufacturingElectric grid monitoring and advanced metering for utilitiesClick stream web analyticsNetwork and data center system monitoring.