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In database query operations, various SQL implementations use hints as additions to the SQL standard that instruct the database engine on how to execute the query. For example, a hint may tell the engine to use as little memory as possible (even if the query will run slowly), or to use or not to use an index (even if the query optimizer would decide otherwise).



Multidimensional Expressions is a query language for OLAP databases, much like SQL is a query language for relational databases. It is also a calculation language, with syntax similar to spreadsheet formulas.



OnLine Analytical Processing is an approach to answering multi-dimensional analytical queries swiftly. OLAP is part of the broader category of business intelligence, which also encompasses relational database, report writing and data mining. Typical applications of OLAP include business reporting for sales, marketing, management reporting, business process management, budgeting and forecasting, financial reporting and similar areas, with new applications coming up, such as agriculture. The term OLAP was created as a slight modification of the traditional database term OLTP.


An OLAP cube is an array of data understood in terms of its 0 or more dimensions. A cube can be considered a multi-dimensional generalization of a two- or three-dimensional spreadsheet. For example, a company might wish to summarize financial data by product, by time-period, and by city to compare actual and budget expenses. Product, time, city and scenario (actual and budget) are the data’s dimensions.


Object Linking and Embedding, DataBase for Online Analytical Processing, abbreviated ODBO, is a Microsoft published specification and an industry standard for multi-dimensional data processing. ODBO is the API for exchanging metadata and data between an OLAP server and a client on a Windows platform. ODBO extends the ability of OLE DB to access OLAP data stores.


OnLine Transaction Processing is a class of information systems that facilitate and manage transaction-oriented applications, typically for data entry and retrieval transaction processing. The term is somewhat ambiguous; some understand a “transaction” in the context of computer or database transactions, while others (such as the Transaction Processing Performance Council) define it in terms of business or commercial transactions. OLTP has also been used to refer to processing in which the system responds immediately to user requests. An automated teller machine (ATM) for a bank is an example of a commercial transaction processing application. Online transaction processing applications are high throughput and insert or update-intensive in database management. These applications are used concurrently by hundreds of users. The key goals of OLTP applications are availability, speed, concurrency and recoverability. Reduced paper trails and the faster, more accurate forecast for revenues and expenses are both examples of how OLTP makes things simpler for businesses. However, like many modern online information technology solutions, some systems require offline maintenance, which further affects the cost-benefit analysis of online transaction processing system.


Plan Guide

A Plan Guide is a way to optimize the performance of queries when their text cannot be directly changed. A Plan Guide influences the optimization of queries by attaching query hints or a fixed query plan to them. A Plan Guide can be useful when a small subset of queries in a database application provided by a third-party vendor are not performing as expected. In the plan guide, the Transact-SQL statement to be optimized optimized is specified and either an OPTION clause that contains the query hints to use or a specific query plan to use to optimize the query is declared. When the query executes, SQL Server matches the Transact-SQL statement to the plan guide and attaches the OPTION clause to the query at run time or uses the specified query plan.


Query Optimization

Query optimization is a function of many relational database management systems. The query optimizer attempts to determine the most efficient way to execute a given query by considering the possible query plans. Generally, the query optimizer cannot be accessed directly by users: once queries are submitted to database server, and parsed by the parser, they are then passed to the query optimizer where optimization occurs. However, some database engines allow guiding the query optimizer with hints.



Transact-SQL is Microsoft’s and Sybase’s proprietary extension to SQL. SQL, the acronym for Structured Query Language, is a standardized computer language that was originally developed by IBM for querying, altering and defining relational databases, using declarative statements. T-SQL expands on the SQL standard to include procedural programming, local variables, various support functions for string processing, date processing, mathematics, etc. and changes to the DELETE and UPDATE statements. These additional features make Transact-SQL Turing complete.


Windows Management Instrumentation

Windows Management Instrumentation is a set of extensions to the Windows Driver Model that provides an operating system interface through which instrumented components provide information and notification. WMI is Microsoft’s implementation of the Web-Based Enterprise Management and Common Information Model standards from the Distributed Management Task Force. WMI allows scripting languages like VBScript or Windows PowerShell to manage Microsoft Windows personal computers and servers, both locally and remotely.

Windows PowerShell

Windows PowerShell is a task automation and configuration management framework from Microsoft, consisting of a command-line shell and associated scripting language built on the .NET Framework. PowerShell provides full access to COM and WMI, enabling administrators to perform administrative tasks on both local and remote Windows systems as well as WS-Management and CIM enabling management of remote Linux systems and network devices.
In: PowerShell: Warming Up



XML for Analysis is an industry standard for data access in analytical systems, such as OLAP and data mining. XMLA is based on other industry standards such as XML, SOAP and HTTP. XMLA is maintained by XMLA Council with Microsoft, Hyperion and SAS being the official XMLA Council founder members.

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