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What is olap?

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2009-05-21 05:15:35
2009-05-21 05:15:35

OLAP stands for Online Analytical Processing. An OLAP system can be considered as a category of applications and technologies that are used for collecting, managing, processing and presenting multi-dimensional data for analysis and management purposes.

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The OLAP allows Nabisco to accurately track sales and consumer preferences

difference between Data Mining and OLAP

Nabisco has created the online analytical processing (OLAP) data mart

there are not OLAP cubes in SSRS OLAP is availeble in SSAS only

OLTP vs. OLAPWe can divide IT systems into transactional (OLTP) and analytical (OLAP). In general we can assume that OLTP systems provide source data to data warehouses, whereas OLAP systems help to analyze it.- OLTP (On-line Transaction Processing) is characterized by a large number of short on-line transactions (INSERT, UPDATE, DELETE). The main emphasis for OLTP systems is put on very fast query processing, maintaining data integrity in multi-access environments and an effectiveness measured by number of transactions per second. In OLTP database there is detailed and current data, and schema used to store transactional databases is the entity model (usually 3NF).- OLAP (On-line Analytical Processing) is characterized by relatively low volume of transactions. Queries are often very complex and involve aggregations. For OLAP systems a response time is an effectiveness measure. OLAP applications are widely used by Data Mining techniques. In OLAP database there is aggregated, historical data, stored in multi-dimensional schemas (usually star schema).

OLAP allows Nabisco to accurately track sales and consumer preferences, with the largest data mart holding sales, price discount, spoilage, transportation, and promotional information for two years

OLAP (Online Analytical Processing)

A statistical database is a database used for statistical analysis purposes. It is an OLAP instead of OLTP system, although this term precedes that modern decision, and classical statistical databases are often closer to the relational model than the multidimensional model commonly used in OLAP systems today.

3 Main reasons: 1.OLTP systems require high concurrency, reliability, locking which provide good performance for short and simple OLTP queries. An OLAP query is very complex and does not require these properties. Use of OLAP query on OLTP system degrades its performance. 2.An OLAP query reads HUGE amount of data and generates the required result. The query is very complex too. Thus special primitiveshave to provided to support this kind of data access. 3.OLAP systems access historical data and not current volatile data while OLTP systems access current up-to-date data and do not need historical data.

OLTP : customer oriented. OLAP : Market oriented OLTP : ER based application oriented concern OLAP : subject oriented concern. Current data : Historical data used for detailed for decesion making Access patterns are short. : COCancelMPLEX

"OLTP Short database transactions Online update/insert/delete Normalization is promoted High volume transactions Transaction recovery is necessary OLAP Current and historical data Long database transactions Batch update/insert/delete Denormalization is promoted Low volume transactions Transaction recovery is not necessary " http://www.geekinterview.com/question_details/209

Hi, The difference can be highlighted by defining the two entities separately, OLTP: Online Transaction Processing is a system normally used in the regular routine of database or User end Transactions (These Transactions are short Transactions). In OLTP, the data is fully normalized in order to avoid redundancy,Duplication of data etc, for achieving normalization various Joins are used that connects the tables. (without these joins OLTP cannot be fully Normalized). Now move on, OLAP: Online Analytical Processing is a system helpful for analysis purposes, it is used in Business Intelligence for analysis reporting, unlike OLTP it doesn't have too many joins in it because in OLAP it is not required to normalize the data, the basic purpose is to make Facts and Dimensions. <<Specifically for reporting purposes the data retrieval should be faster (The joins make the data processing slow) so the basic need of designing OLAP is to make the reporting system faster.>>

The primary advantage of OLAP data storage is better performance for accessing multidimensional data. OLAP systems are also accompanied by calculation engines and data manipulation languages. So a second advantage is that it gives analytical capabilities that are not in SQL or are more difficult to obtain. Finally, if you know how to use it, it is easier to work with multidimensional data in a multidimensional system. There are no table joins, storage is set up to include aggregates along with leaf level data, data is articulated in terms of functional categories (rather than rows and columns, or integer indexes), and so on. This is discussed in, The Multidimensional Data Modeling Toolkit, if you want more information.

A Data Base Management System (DBMS) is a generic term like tin of food. Microsoft Access (msaccess) is a relational database (RDBMS). There are also network, hierarchical, OLAP and other types of databases. So msaccess is a DBMS but not the other way around.

The best answer I can find is here: http://www.receptional.com/business-intelligenceBusiness Intelligence makes it possible to analyze data effectively using BI Tools. OLAP Cube is one of such system that enables management to have a detailed view on multi-dimensional data that are generated from operational data.

Realtional databse is the type of databse which is bascially designed to store the deta on OLTP(Online transaction Processing) systems. Entity relatioship databases are basically designed for reporting perpose. It store the Data from OLAP( online Analytical Processing) systems.

These maybe appear to be similar but they have very important differences. Data Base: It contains a collection of current status of the stored data. The structure is achieved by organising the data according to a data base model. Today, the most common model is the rational model. The most important task of on-line operational database system is to perform on-line transaction and query processing. So, these systems are named Online Transactional Processing (OLTP) systems. Moreover, database system support ad-hoc query and online transaction processing, can be used for other purposes such as data warehousing. Data Warehouse: Data warehouse is a repository of information collected from multiple sources, such as different databases of a company in different places, which stored under a unified schema, over a history of time. Moreover, it is used for decision support and data analysis. It consists of a process of data cleaning, data integration, data transform, data loading, and data refreshing. Data warehouse is used for Online Analytical Processing (OLAP). The major difference between OLTP and OLAP are summarised as follows: OLTP is customer-oriented and it's used by clients, and clerks. But, OLAP is market-oriented and is used for data analysis by managers and analysts. These have different content too, the first one has current data with detail. Furthermore, is easily used for getting decision; however, another one has large amount of historical data, which provides facilities for summarisation, and store information in different levels of granularity. Because of their huge volume, OLAP data are stored on multiple storage media. The main similarity is that both of them are repositories of information, storage large amounts of data.

The most common types of business intelligence software are spreadsheets, reporting and querying software, OLAP, digital dashboards, data mining, data warehousing, decision engineering, process mining, business performance management, and local information systems. Each suits specific needs of a business or individual.

Cognos ReportNet is previous version of Cognos 8. Both versions have report studio tool and that is zero footprint and can be launched from Cognos Portal (Cognos connection). There are lot of difference -- e.g. 1. Tree prompt - available in Cognos 2. Render Variable 3. Dimensional functions for OLAP reporting

no, you are refering to a fact table used to record activity over time. employee records should be in a dimension table, the fact table should have a foreign key to the employees table. The R in rdbms stands for relational. You want to understand the rules of normalization for oltp and denormalized star schema for olap.

For a typical measure there are 3 to 8 dimensions. In a simple case, you may have Market, Product, Time. Telling where it was sold, what was sold, when it was sold. Or, you could have Geography, Channel, Customer, Product, Time In financial applications, you may have a few measure dimensions to select the data you want: scenario, version, account and then add a time dimension, an entity dimension The issue of dimensionality is discussed in detail in The Multidimensional Data Modeling Toolkit, you may want to take a look at that.

Q: Could you please explain the concept of business intelligence?A: Business intelligence is the management and collection of data that is used to help a business in the decision making process. The gathered data can also be used to predict the outcome of various business operations. There are a few key steps in business intelligence, which include: the gathering of data, analysis of that data, a review of the situation, risk evaluation and then using all of this information to make the best decision for the business. This data and analysis can be used to make financial and sales decisions, and also help a company gain an edge over its competitors.Q: What are some of the standard tools used in business intelligence?A: Some of the standard business intelligence tools are:- BusinessObjects- Crystal Reports- Micro Strategy- Microsoft OLAP- Qlik ViewNote: Make sure that the most frequently used solutions are mentioned, as well as new and successful programs. This will demonstrate your interest in the field and knowledge of trends. Both are very important.Q: Describe what Online Analytical Processing is.A: Online analytical processing, or OLAP, is a versatile tool that analyzes data stored within a multidimensional database. It allows the user to isolate pieces of information and view them from many different perspectives. For example: Sales of a particular product in April can be compared to the sales of the same product in September. On the other hand, sales of a particular product can also be compared to other products sold in the area. OLAP software programs can also be used for data mining purposes.Q: Please explain what a universe is in business intelligence.A: A universe is terminology used in the BusinessObjects application. It is actually the semantic layer between the end user and the data warehouse. A universe masks the complex, traditional database structure and replaces it with familiar business terminology. This makes it easier for the end user to understand and use.

—Business intelligence (BI) is a broad category of applications and technologies for gathering, storing, analyzing, and providing access to data to help enterprise users make better business decisions. — —BI applications include the activities of decision support systems, query and reporting, online analytical processing(OLAP),artificial intelligence(AI), Cognose, statistical analysis, forecasting, and data mining. Business analytics consists of —Machine learning, Mathematical learning using models, Neural networks

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No, Normalization is not the only reason for joins. Joins are used to combine related records from 2 relations. They allows to evaluate a join condition between attributes of relations.


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