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What is a Transaction? • When an event in the real world changes the state of the enterprise, a transaction is executed to cause the corresponding change in the database state. • A transaction is an application program with special properties to guarantee it maintains database correctness What is a Transaction Processing System? • A transaction process system (TPS) is an information processing system for business transactions involving the collection, modification and retrieval of all transaction data. • A Transaction Processing System consists of TP monitor, databases, and transactions transactions Transaction Processing System DBMS database DBMS database TP Monitor System Requirements • High Availability: on-line => must be operational while enterprise is functioning • High Reliability: correctly tracks state, does not lose data, controlled concurrency • High Throughput: many users => many transactions/sec • Low Response Time: on-line => users are waiting System Requirements (con’t) • Long Lifetime: complex systems are not easily replaced – Must be designed so they can be easily extended as the needs of the enterprise change • Security: sensitive information must be carefully protected since system is accessible to many users – Authentication, authorization, encryption Roles in Design, Implementation, and Maintenance of a TPS • System Analyst - specifies system using input from customer; provides complete description of functionality from customer’s and user’s point of view • Database Designer - specifies structure of data that will be stored in database • Application Programmer - implements application programs (transactions) that access data and support enterprise rules Roles in Design, Implementation and Maintenance of a TPS (con’t) • Database Administrator - maintains database once system is operational: space allocation, performance optimization, database security • System Administrator - maintains transaction processing system: monitors interconnection of HW and SW modules, deals with failures and congestion Online transaction processing (OLTP) • Online transaction processing, or OLTP, is a class of information systems that facilitate and manage transaction-oriented applications, typically for data entry and retrieval transaction processing. Examples : OLTP • OLTP system is a popular data processing system in today's enterprises. • Example: In a banking System, you withdraw amount from your account. Then Account Number, Withdrawal amount, Available Amount, Balance Amount, Transaction Number etc are operational data elements. Advantages : OLTP • 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. OLAP • OLAP deals with Historical Data or Archival Data. Historical data are those data that are archived over a long period of time. • Online Analytical Processing Server (OLAP) is based on the multidimensional data model. It allows managers, and analysts to get an insight of the information through fast, consistent, and interactive access to information. OLAP Example • If we collect last 10 years data about flight reservation, The data can give us many meaningful information such as the trends in reservation. This may give useful information like peak time of travel, what kinds of people are traveling in various classes (Economy/Business)etc. Types of OLAP Servers We have four types of OLAP servers: • Relational OLAP (ROLAP) • Multidimensional OLAP (MOLAP) • Hybrid OLAP (HOLAP) • Specialized SQL Servers Relational OLAP ROLAP servers are placed between relational back-end server and client front-end tools. To store and manage warehouse data, ROLAP uses relational or extended-relational DBMS. ROLAP includes the following: • Implementation of aggregation navigation logic. • Optimization for each DBMS back end. • Additional tools and services. Multidimensional OLAP • MOLAP uses array-based multidimensional storage engines for multidimensional views of data. With multidimensional data stores, the storage utilization may be low if the data set is sparse. Hybrid OLAP (HOLAP) • Hybrid OLAP is a combination of both ROLAP and MOLAP. It offers higher scalability of ROLAP and faster computation of MOLAP. HOLAP servers allows to store the large data volumes of detailed information. The aggregations are stored separately in MOLAP store. Specialized SQL Servers • Specialized SQL servers provide advanced query language and query processing support for SQL queries over star and schemas in a read-only environment. OLTP vs. OLAP • On-line Transaction Processing (OLTP) – Day-to-day handling of transactions that result from enterprise operation – Maintains correspondence between database state and enterprise state • On-line Analytic Processing (OLAP) – Analysis of information in a database for the purpose of making management decisions 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 multiaccess 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. 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). Data Mart • A data mart is a simple form of a data warehouse that is focused on a single subject (or functional area), such as Sales, Finance, or Marketing. Data marts are often built and controlled by a single department within an organization. Features of Data Mart • A data mart is a miniature data warehouse. • It is just one segment of the data warehouse. • Data marts receive data from a master data warehouse through periodic updates. • A data mart is the access layer of the data warehouse environment that is used to get data out to the users. • A data mart is basically a condensed and more focused version of a data warehouse that reflects the regulations and process specifications of each business unit within an organization. Importance of Data Mart It is easy to access frequently needed data from the database when required by the client. We can give access to group of users to view the Data mart when it is required. Of course performance will be good. It is easy to maintain and to create the data mart. It will be related to specific business. And It is low cost to create a data mart rather than creating data warehouse with a huge space. Data mart vs data warehouse • • • • • • • • • • Data warehouse: Holds multiple subject areas Holds very detailed information Works to integrate all data sources Does not necessarily use a dimensional model but feeds dimensional models. Data mart: Often holds only one subject area- for example, Finance, or Sales May hold more summarized data. Concentrates on integrating information from a given subject area or set of source systems Is built focused on a dimensional model. Types Of Data Mart • Dependent data marts • Independent data marts • Hybrid data marts Dependent data marts • A dependent data mart allows you to unite your organization's data in one data warehouse. This gives you the usual advantages of centralization. Independent data marts • An independent data mart is created without the use of a central data warehouse. This could be desirable for smaller groups within an organization. Hybrid data marts • A hybrid data mart allows you to combine input from sources other than a data warehouse. This could be useful for many situations, especially when you need ad hoc integration, such as after a new group or product is added to the organization. Queries ?