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Is the data normalised in datamart?
In a datamart, data is typically denormalized rather than normalized. This approach is used to optimize query performance and simplify data retrieval for analytical purposes. Denormalization combines data from multiple tables into fewer tables, which can improve read efficiency and speed up reporting. However, the specific design may vary based on the requirements and use cases of the datamart.
Difference between data warehouse and OLTP?
A data warehouse differs from OLTP in that the former handles many large and complex queries regarding various rows of a table while the latter retrieves data from single rows. At the same time, a data warehouse is not real time and supports few users at a time compared to OLTP that can support many concurrent users.
What is the difference between data warehouse testing and conventional testing?
ETL stands for Extract-Transform-Load and it is a process of how data is loaded from the source system to the data warehouse. Data is extracted from an OLTP database, transformed to match the data warehouse schema and loaded into the data warehouse database.Many data warehouses also incorporate data from non-OLTP systems such as text files, legacy systems and spreadsheets. Conventional testing is a sort of testing in which the test engineers will check the developed application or its related parts are working accordingly to the expectations of customer or not, from coding phase of SDLC to the end
What is the difference between OLAP and data mining?
difference between Data Mining and OLAP
How does Data Warehouse differ from Data Mart?
Data marts are combined into a data warehouse cannot be built alone without considering data marts. Both has equal importance to built proper data warehouse.
Is the data normalised in datamart?
In a datamart, data is typically denormalized rather than normalized. This approach is used to optimize query performance and simplify data retrieval for analytical purposes. Denormalization combines data from multiple tables into fewer tables, which can improve read efficiency and speed up reporting. However, the specific design may vary based on the requirements and use cases of the datamart.
What are the key relationships between data warehouse and data mining?
Data warehouse is the database on which we apply data mining.
Difference between data warehouse and OLTP?
A data warehouse differs from OLTP in that the former handles many large and complex queries regarding various rows of a table while the latter retrieves data from single rows. At the same time, a data warehouse is not real time and supports few users at a time compared to OLTP that can support many concurrent users.
What is data where housing and mining?
it's data warehouse....data warehouse: it is a collection of multiple databases or it it is repository of data.data mining it is the process of extracting data from data warehouse.
What is the difference between data warehouse testing and conventional testing?
ETL stands for Extract-Transform-Load and it is a process of how data is loaded from the source system to the data warehouse. Data is extracted from an OLTP database, transformed to match the data warehouse schema and loaded into the data warehouse database.Many data warehouses also incorporate data from non-OLTP systems such as text files, legacy systems and spreadsheets. Conventional testing is a sort of testing in which the test engineers will check the developed application or its related parts are working accordingly to the expectations of customer or not, from coding phase of SDLC to the end
What do you mean by partioning in datawarehouse?
Data warehouse is a large repository of data. The data may or may not be of any use. Partitioning in Data warehouse can be done by forming clusters and then forming groups.Partitioning in datawarehouse can be done by forming clusters. Partitioning can be done on the basis of relation between the data.
What is the difference between OLAP and data mining?
difference between Data Mining and OLAP
What are the differences data warehouse between data mining?
Data warehouse is a technology that aggregates structured data from one or more sources so that it can be compared and analyzed rather than transaction processing, whereas Data mining is the process of analyzing unknown patterns of data.
What do you mean by data warehouse?
Data warehouse is a house where current as well as historical data can be stored.
What is the difference between allocation and search data structure?
difference between serch data structure and allocation data structure
How does Data Warehouse differ from Data Mart?
Data marts are combined into a data warehouse cannot be built alone without considering data marts. Both has equal importance to built proper data warehouse.
What is metadata what is its use in data warehouse architecture?
Metadata is data about data that provides information such as the structure, format, and characteristics of the data stored in a data warehouse. It is used in data warehouse architecture to facilitate data integration, data governance, and data lineage. Metadata helps users understand and manage the data in the data warehouse efficiently.
