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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 ]
Extract, transform, load (ETL) is a three-phase computing process where data is extracted from an input source, transformed (including cleaning), and loaded into an output data container. The data can be collected from one or more sources and it can also be output to one or more destinations.
To securely delete the metadata of a PDF file, it is important to linearize the PDF file afterwards, otherwise changes are reversible and the metadata can be recovered. [5] [6] Metadata removal tools are also commonly used to reduce the overall sizes of files, particularly image files posted on the Web.
In the Print/export section select Download as PDF. The rendering engine starts and a dialog appears to show the rendering progress. When rendering is complete, the dialog shows "The document file has been generated. Download the file to your computer." Click the download link to open the PDF in your selected PDF viewer.
Linearized PDF files (also called "optimized" or "web optimized" PDF files) are constructed in a manner that enables them to be read in a Web browser plugin without waiting for the entire file to download, since all objects required for the first page to display are optimally organized at the start of the file. [27]
Advisory actions typically allow data to be entered unchanged but sends a message to the source actor indicating those validation issues that were encountered. This is most suitable for non-interactive system, for systems where the change is not business critical, for cleansing steps of existing data and for verification steps of an entry process.
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Dirty data, also known as rogue data, [1] are inaccurate, incomplete or inconsistent data, especially in a computer system or database. [ 2 ] Dirty data can contain such mistakes as spelling or punctuation errors, incorrect data associated with a field, incomplete or outdated data, or even data that has been duplicated in the database.