Data warehousing tools
Data warehousing is an online analytical processing which have essential features of decision support. It has been increasing in the database industry. The main focus is on commercial products and services which are available with basic database management and offers too many areas. Decision support requires with different needs on database technology compared with traditional transaction applications. It provides data overview with OLAP technologies with an emphasis with new needs and requirements. We explain back end tools for cleaning, extracting and loading data into a data warehouse. It is a multi-dimensional data model with OLAP typical with front end client tools for both data analysis and query. It is a metadata management for managing the warehouse. It helps in surveying the state of art. This paper identifies many research issues that are related to database community that worked for many years. It is just the beginning that is addressed.
This overview is mainly based on tutorial with author presentation at the VLDB conference. It is a collection of decision support with basic technologies that are aimed for enabling the knowledge worker to have better decisions. It has been explosive growth which involved in services and products that are offered with adoption of these technologies by industries. This includes database software, tools and hardware. These technologies are successful with many industries for manufacturing order, financial services, transportation, utilities, telecommunications and healthcare. It presents a data warehousing roadmap technologies. It focuses with special requirements of data warehouse place with management systems.
OLAP operations include drill-down, more than one hierarchy, rollup and pivot. Operational database are tuned in supporting the workloads of OLTP. It is to execute the complex of OLAP queries against the operational databases. It will result in unacceptable performance. It further supports the data that are missing from operational database. For e.g. understanding the trends and making the predictions that require historical data whereas in operational databases can store only the current data. It requires consolidation data from many sources that might include external sources like stock market needs and with operational databases. It contains various data quality or use for representations, formats, data which is reconciled. They support for multi-dimensional models with typical OLAP with special data organization. It is not generally provided with commercial DBMSs targeted to OLTP. It is for all the reasons that are implemented with data warehouse separately when compared to operational database.
It is implemented on extended and standard DBMSs and it is known as relational OLAP servers. These servers are stored with relational databases and support extensions to special classes and SQL servers. The methods will be efficiently implemented on the multi-dimensional data model and operations. All servers are stored directly in special data structures.