Olap And Oltp Concepts Pdf

olap and oltp concepts pdf

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Note that this book is meant as a supplement to standard texts about data warehousing. This book focuses on Oracle-specific material and does not reproduce in detail material of a general nature. Two standard texts are:.

The historically introduced separation of online analytical processing OLAP from online transaction processing OLTP is in question considering the current developments of databases. To assess mixed workload systems benchmarking has to evolve along with the database technology. Especially in mixed workload scenarios the question arises of how to layout the database.

A Comparative Study of OLTP and OLAP Technologies

Note that this book is meant as a supplement to standard texts about data warehousing. This book focuses on Oracle-specific material and does not reproduce in detail material of a general nature. Two standard texts are:. A data warehouse is a relational database that is designed for query and analysis rather than for transaction processing. It usually contains historical data derived from transaction data, but can include data from other sources. Data warehouses separate analysis workload from transaction workload and enable an organization to consolidate data from several sources.

This helps in:. In addition to a relational database, a data warehouse environment can include an extraction, transportation, transformation, and loading ETL solution, statistical analysis, reporting, data mining capabilities, client analysis tools, and other applications that manage the process of gathering data, transforming it into useful, actionable information, and delivering it to business users.

A common way of introducing data warehousing is to refer to the characteristics of a data warehouse as set forth by William Inmon:. Data warehouses are designed to help you analyze data.

For example, to learn more about your company's sales data, you can build a data warehouse that concentrates on sales. Using this data warehouse, you can answer questions such as "Who was our best customer for this item last year?

Integration is closely related to subject orientation. Data warehouses must put data from disparate sources into a consistent format.

They must resolve such problems as naming conflicts and inconsistencies among units of measure. When they achieve this, they are said to be integrated. Nonvolatile means that, once entered into the data warehouse, data should not change. This is logical because the purpose of a data warehouse is to enable you to analyze what has occurred. A data warehouse's focus on change over time is what is meant by the term time variant. In order to discover trends and identify hidden patterns and relationships in business, analysts need large amounts of data.

This is very much in contrast to online transaction processing OLTP systems, where performance requirements demand that historical data be moved to an archive. Figure illustrates key differences between an OLTP system and a data warehouse.

One major difference between the types of system is that data warehouses are not usually in third normal form 3NF , a type of data normalization common in OLTP environments. Data warehouses and OLTP systems have very different requirements. Here are some examples of differences between typical data warehouses and OLTP systems:. Data warehouses are designed to accommodate ad hoc queries and data analysis. You might not know the workload of your data warehouse in advance, so a data warehouse should be optimized to perform well for a wide variety of possible query and analytical operations.

OLTP systems support only predefined operations. Your applications might be specifically tuned or designed to support only these operations. A data warehouse is updated on a regular basis by the ETL process run nightly or weekly using bulk data modification techniques.

The end users of a data warehouse do not directly update the data warehouse except when using analytical tools, such as data mining, to make predictions with associated probabilities, assign customers to market segments, and develop customer profiles. In OLTP systems, end users routinely issue individual data modification statements to the database.

The OLTP database is always up to date, and reflects the current state of each business transaction. Data warehouses often use denormalized or partially denormalized schemas such as a star schema to optimize query and analytical performance. A typical data warehouse query scans thousands or millions of rows.

For example, "Find the total sales for all customers last month. A typical OLTP operation accesses only a handful of records. For example, "Retrieve the current order for this customer.

Data warehouses usually store many months or years of data. This is to support historical analysis and reporting. OLTP systems usually store data from only a few weeks or months. The OLTP system stores only historical data as needed to successfully meet the requirements of the current transaction. Data warehouses and their architectures vary depending upon the specifics of an organization's situation.

Three com mon architectures are:. Figure shows a simple architecture for a data warehouse. End users directly access data derived from several source systems through the data warehouse. In Figure , the metadata and raw data of a traditional OLTP system is present, as is an additional type of data, summary data. Summaries are very valuable in data warehouses because they pre-compute long operations in advance. For example, a typical data warehouse query is to retrieve something such as August sales.

A summary in an Oracle database is called a materialized view. You must clean and process your operational data before putting it into the warehouse, as shown in Figure You can do this programmatically, although most data warehouses use a staging area in stead.

A staging area simplifies building summaries and general warehouse management. Figure illustrates this typical architecture. Although the architecture in Figure is quite common, you may want to customize your warehouse's architecture for different groups within your organization. You can do this by adding data marts , which are systems designed for a particular line of business.

Figure illustrates an example where purchasing, sales, and inventories are separated. In this example, a financial analyst might want to analyze historical data for purchases and sales or mine historical data to make predictions about customer behavior. You can extract information from the masses of data stored in a data warehouse by analyzing the data.

The Oracle Database provides several ways to analyze data:. A wide array of statistical functions, including descriptive statistics, hypothesis testing, correlations analysis, test for distribution fit, cross tabs with Chi-square statistics, and analysis of variance ANOVA ; these functions are described in the Oracle Database SQL Language Reference.

Oracle OLAP provides native multidimensional storage and speed-of-thought response times when analyzing data across multiple dimensions.

The database provides rich support for analytics such as time series calculations, forecasting, advanced aggregation with additive and non additive operators, and allocation operators. These capabilities make the Oracle database a complete analytical platform, capable of supporting the entire spectrum of business intelligence and advanced analytical applications.

Oracle OLAP uses a multidimensional data model to perform complex statistical, mathematical, and financial analysis of historical data in real time. By integrating multidimensional objects and analytics into the database, Oracle provides the best of both worlds: the power of multidimensional analysis along with the reliability, availability, security, and scalability of the Oracle database. Cubes and other dimensional objects are first class data objects represented in the Oracle data dictionary.

Data security is administered in the standard way, by granting and revoking privileges to Oracle Database users and roles. The benefits to your organization are significant.

Oracle OLAP offers the power of simplicity. One database, standard administration and security, standard interfaces and development tools.

Oracle OLAP makes it easy to enrich your database and your applications with interesting analytic content. Native SQL access to Oracle multidimensional objects and calculations greatly eases the task of developing dashboards, reports, business intelligence BI and analytical applications of any type compared to systems that offer proprietary interfaces.

You can leverage your existing DBA staff, rather than invest in specialized administration skills. One major administrative advantage of Oracle's embedded OLAP technology is automated cube maintenance. With standalone OLAP servers, the burden of refreshing the cube is left entirely to the administrator.

This can be a complex and potentially error-prone job. The administrator must create procedures to extract the changed data from the relational source, move the data from the source system to the system running the standalone OLAP server, load and rebuild the cube. The DBA must take responsibility for the security of the deltas changed values during this process as well. The database tracks the staleness of the dimensional objects, automatically keeps track of the deltas in the source tables, and automatically applies only the changed values during the refresh process.

The DBA simply schedules the refresh at appropriate intervals, and Oracle Database takes care of everything else. In contrast, with a standalone OLAP server, administrators must manage security twice: once on the relational source system and again on the OLAP server system. Additionally, they must manage the security of data in transit from the relational system to the standalone OLAP system. Business intelligence and analytical applications are dominated by actions such as drilling up and down hierarchies and comparing aggregate values such as period-over-period, share of parent, projections onto future time periods, and a myriad of similar calculations.

Often these actions are essentially random across the entire space of potential hierarchical aggregations. Because Oracle OLAP pre-computes or efficiently computes on the fly all aggregates in the defined multidimensional space, it delivers unmatched performance for typical business intelligence applications. Oracle OLAP queries take advantage of Oracle shared cursors, dramatically reducing memory requirements and increasing performance. All these features add up to reduced costs.

Administrative costs are reduced because existing personnel skills can be leveraged. Moreover, the Oracle database can manage the refresh of dimensional objects, a complex task left to administrators in other systems.

Standard security reduces administration costs as well. Application development costs are reduced because the availability of a large pool of application developers who are SQL knowledgeable, and a large collection of SQL-based development tools means applications can be developed and deployed more quickly.

Hardware costs are reduced by Oracle OLAP's efficient management of aggregations, use of shared cursors, and Oracle RAC, which enables highly scalable systems to be built from low-cost commodity components. The OLAP option automatically generates a set of relational views on cubes, dimensions, and hierarchies. SQL applications query these views to display the information-rich contents of these objects to analysts and decision makers.

You can also create custom views that comply with the structure expected by your applications, using the system-generated views like base tables. Analysts can choose any SQL query and analysis tool for selecting, viewing, and analyzing the data You can use your favorite tool or application, or use one of the tools supplied with Oracle Database, such as Oracle Application Express and Business Intelligence Publisher.

Cube materialized views bring the fast update and fast query capabilities of the OLAP option to applications that query detail relational tables, as well as to applications that query cubes directly. A single cube materialized view can replace many of the relational materialized views of summaries on a fact table, providing uniform response time to all summary data through query rewrite.

Normalization in a Mixed OLTP and OLAP Workload Scenario

Show all documents Decision Support Systems are used by the executives, analysts and high level users to take future business decisions. The complex analysis is made possible on historical data using different information delivery tools , such as OLAP tools [6]. OLAP tools use pre-aggregation mechanism to summarize data for fast evaluation of information. Advances and Research Directions in Data-Warehousing Technology On-Line Analytical Processing OLAP tools are well-suited for complex data analysis, such as multi-dimensional data analysis, and to assist in decision support activities while data min[r].

Defined in many different ways, but not rigorously. A decision support database that is maintained separately from the organizations operational database Support information processing by providing a solid platform of consolidated, historical data for analysis. A data warehouse is a subject-oriented, integrated, time-variant, and nonvolatile collection of data in support of managements decision-making process. Organized around major subjects, such as customer, product, sales. Focusing on the modeling and analysis of data for. April 27, Data Mining: Concepts and Techniques 4. Constructed by integrating multiple, heterogeneous data sources relational databases, flat files, on-line transaction records Data cleaning and data integration techniques are applied.

OLAP is an online system that reports to multidimensional analytical queries like financial reporting, forecasting, etc. It is an online data retrieving and data analysis system. Focus Insert, Update, Delete information from the database. Extract data for analyzing that helps in decision making. Data OLTP and its transactions are the original source of data. Transaction OLTP has short transactions. OLAP has long transactions.

What Are OLAP and OLTP?

Online Analytical Processing, a category of software tools which provide analysis of data for business decisions. OLAP systems allow users to analyze database information from multiple database systems at one time. The primary objective is data analysis and not data processing.

In Online transaction processing OLTP , information systems typically facilitate and manage transaction-oriented applications. The term "transaction" can have two different meanings, both of which might apply: in the realm of computers or database transactions it denotes an atomic change of state, whereas in the realm of business or finance, the term typically denotes an exchange of economic entities as used by, e. OLTP has also been used to refer to processing in which the system responds immediately to user requests.

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OLAP tools

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Online analytical processing

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Difference Between OLTP and OLAP


Algernon B.


was an On-Line Transaction Processing (OLTP) system designed to support on- line transactions and ❖OLAP databases are divided into one or more cubes, and each cube is organized and structuring data around the business concepts​.

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OLAP tools enable users to analyze multidimensional data interactively from multiple perspectives.

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