144x Filetype PPTX File size 0.08 MB Source: www.bietdvg.edu
Introduction Data Warehousing A data Warehousing is a technique for collecting and managing data from varied sources to provide meaningful business insights. It is a blend of technologies and components which allows the strategic use of data. It is electronic storage of a large amount of information by a business which is designed for query and analysis instead of transaction processing. It is a process of transforming data into information and making it available to users in a timely manner to make a difference. Data Warehousing Modeling Data warehouse modeling includes: Top Down/Requirements Driven Approach Fact Tables and Dimension Tables Multidimensional Model/Star Schema Support Roll Up, Drill Down, and Pivot Analysis Time Phased/Temporal Data Operational Logical and Physical Data Models Normalization and Denormalization Model Granularity: Level of Detail OLAP Online analytical processing(OLAP) is an approach to answer multi-dimensional analytical queries swiftly in computing. OLAP is part of the broader category of business intelligence., which also encompasses relational databases, report writing and data mining. Advantages: OLAP is a platform for all types of business includes planning, budgeting, reporting and Analysis. Information and calculations are consistent in an OLAP cube. This is a crucial benefit . Disadvantages: OLAP requires organizing data into a star schema. These schemas are complicated to implement and administer. Transactional data cannot be accessed with OLAP system. The main characteristics of OLAP are as follows: • Multidimensional conceptual view: OLAP systems let business users have a dimensional and logical view of the data in the data warehouse. It helps in carrying slice and dice operations. • Multi-User Support: Since the OLAP techniques are shared, the OLAP operation should provide normal database operations, containing retrieval, update, adequacy control, integrity, and security. • Accessibility: OLAP acts as a mediator between data warehouses and front-end. The OLAP operations should be sitting between data sources (e.g., data warehouses) and an OLAP front-end. • Storing OLAP results: OLAP results are kept separate from data sources. • Uniform documenting performance: Increasing the number of dimensions or database size should not significantly degrade the reporting performance of the OLAP system. • OLAP provides for distinguishing between zero values and missing values so that aggregates are computed correctly. • OLAP system should ignore all missing values and compute correct aggregate values. • OLAP facilitate interactive query and complex analysis for the users. • OLAP allows users to drill down for greater details or roll up for aggregations of metrics along a single business dimension or across multiple dimension. • OLAP provides the ability to perform intricate calculations and comparisons. • OLAP presents results in a number of meaningful ways, including charts and graphs.
no reviews yet
Please Login to review.