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The Sheetster administration console allows for the design of relational data objects using a visual query designer and object-relational mapper. The RESTful API accepts data from a variety of sources, and outputs data-mapped spreadsheet objects in a variety of formats including JSON, XML, and as Excel-compatible XLS.
A chart created with data from a Microsoft Excel spreadsheet that only saves the chart. To save the chart and spreadsheet save as .XLS. XLC is not supported in Excel 2007 or in any newer versions of Excel. Dialog .xld: Used in older versions of Excel. Archive .xlk: A backup of an Excel Spreadsheet Add-in (DLL) .xll
OpenRefine is an open-source desktop application for data cleanup and transformation to other formats, an activity commonly known as data wrangling. [3] It is similar to spreadsheet applications, and can handle spreadsheet file formats such as CSV, but it behaves more like a database.
There are files and folders on your personal computer that you genuinely don't need. Here's how to clean out unnecessary junk and free up storage.
Excel pivot tables include the feature to directly query an online analytical processing (OLAP) server for retrieving data instead of getting the data from an Excel spreadsheet. On this configuration, a pivot table is a simple client of an OLAP server.
Besides differences in the schema, there are several other differences between the earlier Office XML schema formats and Office Open XML. Whereas the data in Office Open XML documents is stored in multiple parts and compressed in a ZIP file conforming to the Open Packaging Conventions, Microsoft Office XML formats are stored as plain single monolithic XML files (making them quite large ...
Local storage may refer to: Data storage that is directly attached to a compute device; A network-attached storage device accessed via a local network, rather than a wide area network; Local storage, a JavaScript web storage facility allowing websites to store data on a user's device
Data cleansing may also involve harmonization (or normalization) of data, which is the process of bringing together data of "varying file formats, naming conventions, and columns", [2] and transforming it into one cohesive data set; a simple example is the expansion of abbreviations ("st, rd, etc." to "street, road, etcetera").