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Data cleansing or data cleaning is the process of identifying and correcting (or removing) corrupt, inaccurate, or irrelevant records from a dataset, table, or database.It involves detecting incomplete, incorrect, or inaccurate parts of the data and then replacing, modifying, or deleting the affected data. [1]
Airtable – a spreadsheet-database hybrid, with the features of a database but applied to a spreadsheet. Coda; EditGrid – access, collaborate and share spreadsheets online, with API support; discontinued since 2014; Google Sheets – as part of Google Workspace; iRows – closed since 31 December 2006; JotSpot Tracker – acquired by Google Inc.
In computer programming, create, read, update, and delete (CRUD) are the four basic operations (actions) of persistent storage. [1] CRUD is also sometimes used to describe user interface conventions that facilitate viewing, searching, and changing information using computer-based forms and reports.
Data validation is intended to provide certain well-defined guarantees for fitness and consistency of data in an application or automated system. Data validation rules can be defined and designed using various methodologies, and be deployed in various contexts. [1]
The data gathered by DAM is used to analyze and report on database activity, support breach investigations, and alert on anomalies. DAM is typically performed continuously and in real-time. Database activity monitoring and prevention (DAMP) is an extension to DAM that goes beyond monitoring and alerting to also block unauthorized activities.
The problem of database repair is a question about relational databases which has been studied in database theory, and which is a particular kind of data cleansing. The problem asks about how we can "repair" an input relational database in order to make it satisfy integrity constraints .
Since the early 1990s, the operational database software market has been largely taken over by SQL engines. In 2014, the operational DBMS market (formerly OLTP) was evolving dramatically, with new, innovative entrants and incumbents supporting the growing use of unstructured data and NoSQL DBMS engines, as well as XML databases and NewSQL databases.
Spreadsheet risk is the risk associated with deriving a materially incorrect value from a spreadsheet application that will be utilized in making a related (usually numerically based) decision. Examples include the valuation of an asset , the determination of financial accounts , the calculation of medicinal doses, or the size of a load-bearing ...