Data Warehousing Questions

The main differenc between View and Materialized View states that -

1. View means it stores the SQL statement in the database and let you use it as a table. Everytime you access the view, the SQL statement executes.

2. Materialized view means it stores the results of the SQL in table form in the database. SQL statement only executes once and after that everytime you run the query, the stored result set is used. Advantage include quick query results.

BUS Schema is composed of a master suite of confirmed dimension and standardized definition if facts.

Data mining is a method for comparing large amounts of data for the purpose of finding patterns. Data mining is normally used for models and forecasting. Data mining is the process of correlations, patterns by shifting through large data repositories using pattern recognition techniques.

Data warehousing is the central repository for the data of several business systems in an enterprise. Data from various resources extracted and organized in the data warehouse selectively for analysis and accessibility.

OLTP stands for OnLine Transaction Processing. Applications that supports and manges transactions which involve high volumes of data are supported by OLTP system. OLTP is based on client-server architecture and supports transactions across networks.

OLAP stands for OnLine Analytical Processing. Business data analysis and complex calculations on low volumes of data are performed by OLAP. An insight of data coming from various resources can be gained by a user with the support of OLAP.

- UDAI places the data in the source systems.

- A set of views are defined for providing access the unified view to the clients / customers.

- Zero latency of data can be propagated from the source system.

- The generated consolidated data need not require separate storage space.

- Data history and version management is limited and applied only to the similar type of data.

- Accessing to the user data overloads on the source systems.

Data Mart is a data repository which is served to a community of people who works on knowledge (also known as knowledge workers). The data resource can be from enterprise resources or from a data warehouse.

Data cleaning is also known as data scrubbing. Data cleaning is a process which ensures the set of data is correct and accurate. Data accuracy and consistency, data integration is checked during data cleaning. Data cleaning can be applied for a set of records or multiple sets of data which need to be merged.

Data cleaning is performed by reading all records in a set and verifying their accuracy. Typos and spelling errors are rectified. Mislabeled data if available is labeled and filed. Incomplete or missing entries are completed. Unrecoverable records are purged, for not to take space and inefficient operations.

- Physical Data Integration is all about creating new system that replicates data from the source systems.

- This process is done to manage the data independent of the original system.

- Data Warehouse is the example of Physical Data Integration.

- The benefits of PDI include data version management, combination of data from various sources, like mainframes, flat files, databases.

- A separate system is needed for handling vast data volumes.