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Granularity refers to the level of detail or summarization in the units of in the data warehouse (Inmon, WH 2002). For example, one of the dimension might be a date/time dimension which could be at the year, month, quarter, period, week, day, hour, minute, second, hundredths of seconds level of granularity. High granularity means that the data is at or near the transaction level, which has more detail. Low granularity means that the data is aggregated, which has less detail.
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Data granularity in a data warehouse?
Granularity refers to the level of detail of the data stored fact tables in a data warehouse. High granularity refers to data that is at or near the transaction level. Data that is at the transaction level is usually referred to as atomic level data. Low granularity refers to data that is summarized or aggregated, usually from the atomic level data. Summarized data can be lightly summarized as in daily or weekly summaries or highly summarized data such as yearly averages and totals.
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 are benefits of data warehouse?
One of the biggest benefits is that you can archive your data to a data warehouse. This can keep your main "production" database smaller which can provide some performance benefits. Also you can use the data warehouse to run complex queries and data-mining without adverse effects on the performance of your "production" application.
What is a smaller version of a data warehouse?
What are the three most common forms of data warehouses? is a smaller form of a data warehouse that is often used by a single department or function. An independent data mart is a tiny warehouse that is built for a strategic business unit (SBU) or a department, but it does not have a central data source (EDW). To learn more about data science please visit- Learnbay.co
Difference between data warehouse and datamart?
A data warehouse has multiple functional areas whereby a centralized organizational unit is responsible for implementing it. On the contrary, data marts focus on particular functional areas hence are simple forms of a data warehouse.
Data granularity in a data warehouse?
Granularity refers to the level of detail of the data stored fact tables in a data warehouse. High granularity refers to data that is at or near the transaction level. Data that is at the transaction level is usually referred to as atomic level data. Low granularity refers to data that is summarized or aggregated, usually from the atomic level data. Summarized data can be lightly summarized as in daily or weekly summaries or highly summarized data such as yearly averages and totals.
What is the data granularity?
Data granularity refers to the level of detail present in a dataset. It describes the extent to which data is broken down into smaller parts, such as individual data points or intervals. A dataset with high granularity contains more detailed information, while a dataset with low granularity contains broader, summarized data.
What is granularity in sql database?
lowest level of data
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 factors affect selection of granularity size for data item?
There are several factors guiding granularity selection: 1) overhead - the more granular the more objects and methods in supporting code, 2) regulatory - there may be compliance mandates specifying what granularity will be maintained in the data, 3) industry practice - your granularity should generally match that of cloud and third-party products if your system will be integrating with these services. For example, do not combine first and last name as a single data item if every one else manages these as two separate data items.
What do you mean by data warehouse?
Data warehouse is a house where current as well as historical data can be stored.
What are the key relationships between data warehouse and data mining?
Data warehouse is the database on which we apply data mining.
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.
Every data structure in data warehouse contains time element?
Every data structure in the data warehouse contains the time element. Why?
What are benefits of data warehouse?
One of the biggest benefits is that you can archive your data to a data warehouse. This can keep your main "production" database smaller which can provide some performance benefits. Also you can use the data warehouse to run complex queries and data-mining without adverse effects on the performance of your "production" application.
What makes the best data warehouse architecture?
A data warehouse architecture is similar to various relational database systems. What makes the best architecture is the organization of the warehouse itself and the data it consist of.
