lecture_6_Online Analytical Processing.ppt
- 1. OLAP 1
- 2. OLAP On-Line Analytical Processing (OLAP) is a category of software technology Enables analysts, managers and executives to gain insight into data through fast, consistent, interactive access in a wide variety of possible views of information 2
- 3. Codd’s 12 Guidelines for an OLAP System 1. Multidimensional Conceptual View. Provide a multidimensional data model that is analytical and easy to use. Business users’ view of an enterprise is multidimensional in nature. 2. Transparency. Make the technology, underlying data repository, computing architecture, and the diverse nature of source data totally transparent to users in order to support a true open system approach. 3. Accessibility. Provide access only to the data that is actually needed to perform the specific analysis, presenting a single, coherent, and consistent view to the users. 4. Consistent Reporting Performance. Users must perceive consistent run time, response time, or machine utilization every time a given query is run. 3
- 4. Codd’s 12 Guidelines for an OLAP System 5. Client/Server Architecture. Conform the system to the principles of client/server architecture for optimum performance, flexibility, adaptability, and interoperability. 6. Generic Dimensionality. Ensure that every data dimension is equivalent in both structure and operational capabilities. 7. Dynamic Sparse Matrix Handling. When encountering a sparse matrix, the system must be able to dynamically deduce the distribution of the data and adjust the storage and access to achieve and maintain consistent level of performance. 8. Multiuser Support. Provide concurrent data access, data integrity, and access security. 9. Unrestricted Cross-dimensional Operations. Provide ability for the system to recognize dimensional hierarchies and automatically perform roll-up and drill-down operations within a dimension or across dimensions. 4
- 5. Codd’s 12 Guidelines for an OLAP System 10. Intuitive Data Manipulation. Enable consolidation path reorientation (pivoting), drill-down and roll-up, and other manipulations to be accomplished intuitively and directly via point-and-click and drag-and-drop actions on the cells of the analytical model. Avoid the use of a menu or multiple trips to a user interface. 11. Flexible Reporting. Provide capabilities to the business user to arrange columns, rows, and cells in a manner that facilitates easy manipulation, analysis, and synthesis of information. 12. Unlimited Dimensions and Aggregation Levels. Accommodate at least 15-20 data dimensions within a common analytical model. Each of these generic dimensions must allow a practically unlimited number of user-defined aggregation levels within any given consolidation path. 5
- 6. Characteristics of an OLAP System 1. Let business users have a multidimensional and logical view of the data in the data warehouse 2. Facilitate interactive query and complex analysis for the users 3. Allow users to drill down for greater details or roll up for aggregations of metrics along a single business dimension or across multiple dimensions 4. Provide ability to perform intricate(detailed) calculations and comparisons, and 5. Present results in a number of meaningful ways, including charts and graphs. 6
- 7. Uses and benefits of an OLAP System 1. Increased productivity of business managers, executives, and analysts 2. Inherent flexibility of OLAP systems means that users may be self- sufficient in running their own analysis without IT assistance 3. Benefit for IT developers because using software specifically designed for the system development results in faster delivery of applications 4. Self-sufficiency of users, resulting in reduction in backlog(accumulation) 5. Faster delivery of applications following from the previous benefits 6. More efficient operations through reducing time on query executions and in network traffic 7. Ability to model real-world challenges with business metrics and dimensions 7
- 8. OLAP Models 1. ROLAP stands for relational online analytical processing accesses data directly from relational tables in data warehouses. 2. MOLAP stands for multidimensional online analytical processing. data for analysis is stored in specialized multidimensional databases. Large multidimensional arrays form the storage structures DOLAP stands for desktop online analytical processing. 3. DOLAP is meant to provide portability to users of online analytical processing. In the DOLAP(Desktop Online Analytical Processing) methodology, multidimensional datasets are created and transferred to the desktop machine, requiring only the DOLAP software to exist on that machine. 8
- 9. Architecture of MOLAP model 9 •Pre-calculated and pre-fabricated multidimensional data cubes are stored in multidimensional databases. •The MOLAP engine in the application layer pushes a multidimensional view of the data from the MDDBs to the users. .
- 10. Architecture of ROLAP model 10 •The analytical server in the middle tier application layer creates multidimensional views on the fly. •The multidimensional system at the presentation layer provides a multidimensional view of the data to the users. •When the users issue complex queries based on this multidimensional view, the queries are transformed into complex SQL directed to the relational
- 12. OLAP multidimensional analysis capabilities Drill down and roll-up Slice and Dice or rotation Roll up: rolling up to higher hierarchical levels of aggregation Drilling down to lower levels of detail. A type of multidimensional analysis in which the users can look at page displays representing different versions of the slices in the cube. The users can view the data from many angles, understand the numbers better, and arrive at meaningful conclusions. 12
- 13. Reasons of not feeding data directly from source operation systems into an OLAP system 13 1. An OLAP system needs transformed and integrated data. • The system assumes that the data has been consolidated and cleansed somewhere before it arrives. The disparity among operational systems does not support data integration directly. 2. The operational systems keep historical data only to a limited extent. An OLAP system needs extensive historical data. • Historical data from the operational systems must be combined with archived historical data before it reaches the OLAP system. 3. An OLAP system requires data in multidimensional representations. This calls for summarization in many different ways. • Trying to extract and summarize data from the various operational systems at the same time is untenable. Data must be consolidated before it can be summarized at various levels and in different combinations. 4. Many source systems may require many interfaces for data
- 14. Advantages of ROLAP SCALABLE, ROLAP is considered to be more scalable in handling large data volumes especially models with dimensions and very high cardinality that is millions of members. LOAD TIMES ARE MUCH SHORTER, with a variety of data loading tools available, and the ability to fine tune ETL code to the particular data model; load times are generally shorter than the automated MOLAP loads. The data is stored in a standard relational database and can be accessed by any SQL reporting tool not necessarily an OLAP tool. BY DECOUPLING; the data storage from the multidimensional model, it’s possible to successfully model data that would not otherwise fit into a strict dimensional model. ROLAP TOOLS are better at HANDLING NON_AGGREGATABLE FACTS, of which MOLAP tools tend to suffer from slow performance when querying these elements. 14
- 15. Disadvantages of ROLAP ROLAP tools have slow performance than MOLAP tools The loading of aggregate tables must be managed by custom ETL code, and ROLAP tools don’t help with this task. When the step of creating aggregate tables is skipped, the query performance then suffers because the large detailed tables must be queried. And this can be partially remedied by by adding additional aggregate tables, however it’s still not practical to create aggregate tables for all combinations of dimensions or attributes. ROLAP relies on the general purpose database for querying and caching, therefore several purpose techniques employed by MOLAP tools are not available like special hierarchical indexing. However modern ROLAP tools take advantage of the latest improvements in SQL language like CUBE and ROLLUP, DB2 CUBE VIEWS as well as SQL OLAP EXTENSIONS, these SQL improvements can mitigate the benefit of MOLAP tools. Since ROLAP tools rely on SQL for all their computations, they are not suitable when the model is heavy on calculations which don’t translate well into SQL. Examples include Budgeting, Allocations and Financial reporting. 15
- 16. Advantages of MOLAP Fast query performance due to optimized storage, multidimensional indexing and caching. Smaller on-disk size of data compared to data stored in relational databases due to compression techniques. Automated computations of higher level aggregates of the data It’s very compact for low dimension data sets Array models provide natural indexing Effective data extract achieved through the pre- structuring of aggregated data 16
- 17. Disadvantages of MOLAP The processing step; that is data load can be quite lengthy, especially on large data volumes. This is usually remedied by doing only incremental processing i.e. processing only the data which has changed, usually new data instead of reprocessing the entire data set. MOLAP tools traditionally have difficulty querying models with dimensions with very high cardinality example millions of members Some MOLAP tools have difficulty querying and updating models with more than ten dimensions, this limit differs depending on the complexity and cardinality of the dimension in question. It also depends on the number of facts or measures stored, yet other products can handle hundreds of dimensions. MOLAP approach introduces data redundancy. 17