handling data in data warehouse technology

1. Handling data in data warehouse technology .

2. Introduction • A data warehouse is a subject-oriented, integrated, time-variant and non-volatile collection of data in support of management's decision making process. • Subject-Oriented: A data warehouse can be used to analyze a particular subject area. • Integrated:A data warehouse integrates data from multiple data sources. • Time-Variant: Historical data is kept in a data warehouse. For example, one can retrieve data from 3 months, 6 months, 12 months, or even older data from a data warehouse. • Non-volatile: Once data is in the data warehouse, it will not change. So, historical data in a data warehouse should never be altered.

3. Data warehouse techniques • Data warehouse dimensional techniques: some dimensional techniques is used to perform the dimension that are • Conformed Dimensions: Used to create data warehouse table. • Slowly Changing Dimensions: Slowly changing dimensions are used when you wish to capture the changing data within the dimension over time. • Hierarchical Dimensions: it make the arrangements of data information in table. • Snowflake Dimension tables: The removing all redundant dimensional attributes into separate dimension tables linked to the main dimensional table are snowflakes.

4. Bitmap indexing techniques • Using Bitmap Indexes in Data Warehouses: • The environments typically have large amounts of data and ad hoc queries, but a low level of concurrent DML transactions. For such applications, bitmap indexing provides: • Reduced response time for large classes of ad hoc queries. • Reduced storage requirements compared to other indexing techniques. • Dramatic performance gains even on hardware with a relatively small number of CPUs or a small amount of memory.

5. Advantages of bitmap indexing techniques • Bitmap indexes are primarily intended for data warehousing applications where users query the data rather than update it. They are not suitable for OLTP applications with large numbers of concurrent transactions modifying the data. • Indexes are more beneficial for high cardinality columns. • It optimize the memory for data storing purpose in data rows and column.

6. latest technology of data warehouse • Self-Service Big Data applications :Applications that simplify data cleaning, data preparation and data exploration tasks is expected to increase. • Traditional Database technology: RDBMS systems have been dominating the database world for decades when structured data formed the major proportion of data in any organization.  generating volumes of data on a daily basis, that the amount of unstructured data is steadily increasing and companies have started realizing the potential insights one can gain from such data.  • Cloud solutions will power Big Data solutions: Applications involving IOT will require a perfect scalable solution for managing huge volumes of Data.

7. Advantages • Scalability: Datawarehouse tools are largely defined scalability because that store large volume of data store in data base maintained. • Access: Most utilize online analytical process (OLAP) protocols.performancemonitoring is important in determining whether you can perform an ETL load at the same time as a data mining procedure. • Integrations: Multiple number of data integrated in single database

8. Thanks for watching this data warehouse technology information. if you have any doubts and queries about this data warehouse course contact our Data Warehouse training in Chennai and our website. Address: 9/84 Ground Floor,Flat No 2, Adyar,Chennai,Tamil Nadu-600020. info@datawaretools.in Contact no: 9176592949