Description of the Microsoft SQL Server 2012:
For a comparing of the different SQL Server 2012 Editions, please see later in this description.
Microsoft SQL Server Overview
Microsoft SQL Server 2012 is a cloud-ready information platform that will help organizations unlock breakthrough insights across the organization and quickly build solutions to extend data across on-premises and public cloud, backed by mission critical confidence.
* The AlwaysOn you need for your database stability and security
* Blazing-fast query performance with ColumnStore Index
* Rapid data discovery via Power View and PowerPivot
* Credible, consistent data via BI Semantic Model and Data Quality Services
* Scale on demand from devices to datacenter to cloud
* Write applications once, run anywhere with SQL Server Data Tools
The Standard Edition
The Standard Edition supports up to 16 cores in one physical server. It provides the relational database capabilities you would expect, as well as basic BI and reporting features. Its notable omissions include PowerPivot, Power View, Master Data Services, advanced auditing, transparent data encryption, columnstore indexes, and other data warehousing features. SQL Server 2012 Standard Edition also includes support for two-node AlwaysOn failover clusters.
EIM delivers a rich set of technologies designed to improve the overall relevance and value of organizational information. Empowering people to discover and resolve problems with data and streamline existing data management processes using familiar tools like Microsoft Excel.
* Deliver credible, consistent data to the right users.
* Help ensure data confidence with easy to use tools.
* Self-service data management with the MDS Add-in for Excel.
* Improved developer productivity.
* Simplified deployment and management.
* Based on a Data Quality Knowledge Base (DQKB) that stores all the knowledge related to a specific type of data source, and is created/maintained by the organization’s data experts (Data Stewards).
* DQKB contains multiple data domains that capture the semantics of your data.
* Acquires additional knowledge the more you use it.
* Support use of user-generated knowledge and Intellectual Property from 3rd party reference data providers.
* Build once, reuse for multiple data quality improvements.
* Cleanse the data and keep it clean.
* Business-IT shared responsibility for data integration, data management, and data quality.
* Designed for ease of use to empower users.
* Help the consumers of data; structure the data worthwhile to them.
* Speed the adoption/conversion to a managed process by enabling users to build their own simple solutions.
* Enable business users, experts in data content rather than data management tools, to continue working in familiar tools like Microsoft® Excel.
* Increase Developer and IT Pro productivity with new SSIS usability, deployment, configuration and management enhancements.
Open and Extensible
* Windows Azure Marketplace DataMarket interoperability in DQS.
* Explore and integrate with cloud-based 3rd party reference data and user-generated knowledge.
* Integrate SSIS and DQS with the new DQS Cleansing transform to leverage the Data Quality Knowledge Base.
* Integrate SSIS and MDS with subscription views and entity based staging.
* Enable organizations to integrate existing databases, data warehouses, enterprise applications, and other heterogeneous sources of information.
* Leveraging existing data and application assets can help mitigate risk and allows the extension of Line of Business systems into analytical applications that provide a complete view across data sources.
Managed and Secure
* Build confidence in your enterprise data.
* Leverage a familiar tool, SQL Server Management Studio (SSMS), to manage SSIS projects.
* Make use of parameters as a simplified way to configure projects and packages.
* Make use of configurable logging to collect comprehensive information on package execution.
* Leverage built-in SSIS reports and views for troubleshooting package failures, performance, and data issues.
* Ensure data integrity by centralizing storage and distributing administration.
* Ensure object definitions across systems.
* Create, maintain and storage of master data structures used for providing object mapping, reference data, metadata management, and dimension and hierarchy management.