Search results
Results from the WOW.Com Content Network
In tableau software, data blending is a technique to combine data from multiple data sources in the data visualization. [17] A key differentiator is the granularity of the data join. When blending data into a single data set, this would use a SQL database join, which would usually join at the most granular level, using an ID field where ...
An inner join (or join) requires each row in the two joined tables to have matching column values, and is a commonly used join operation in applications but should not be assumed to be the best choice in all situations. Inner join creates a new result table by combining column values of two tables (A and B) based upon the join-predicate.
Google Fusion Tables is a service for data management, integration and collaboration. You can easily upload data sets from CSV, KML and spreadsheets, and visualize the data using a variety of tools. Users can merge data from multiple tables and conduct detailed discussions about the data (on rows, columns and even cells).
Joining data from multiple sources (e.g., lookup, merge) and deduplicating the data; Aggregating (for example, rollup – summarizing multiple rows of data – total sales for each store, and for each region, etc.) Generating surrogate-key values; Transposing or pivoting (turning multiple columns into multiple rows or vice versa)
Data fusion is the process of integrating multiple data sources to produce more consistent, accurate, and useful information than that provided by any individual data source. Data fusion processes are often categorized as low, intermediate, or high, depending on the processing stage at which fusion takes place. [ 1 ]
Ontology-based data integration involves the use of one or more ontologies to effectively combine data or information from multiple heterogeneous sources. [1] It is one of the multiple data integration approaches and may be classified as Global-As-View (GAV). [ 2 ]
Data warehouses serve to combine data from many different operational source systems into one logical data model, which can then be subsequently fed into a business intelligence system for reporting and analytics. Each operational source system may have its own method of identifying the same entities used in the logical data model, so record ...
UNION can be useful in data warehouse applications where tables are not perfectly normalized. [2] A simple example would be a database having tables sales2005 and sales2006 that have identical structures but are separated because of performance considerations. A UNION query could combine results from both tables.