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  2. FlatBuffers - Wikipedia

    en.wikipedia.org/wiki/FlatBuffers

    This makes accessing data in these formats much faster than data in formats requiring more extensive processing, such as JSON, CSV, and in many cases Protocol Buffers. Compared to other serialization formats however, the handling of FlatBuffers requires usually more code, and some operations are not possible (like some mutation operations).

  3. Data wrangling - Wikipedia

    en.wikipedia.org/wiki/Data_wrangling

    Data wrangling typically follows a set of general steps which begin with extracting the data in a raw form from the data source, "munging" the raw data (e.g. sorting) or parsing the data into predefined data structures, and finally depositing the resulting content into a data sink for storage and future use. [1]

  4. Associative array - Wikipedia

    en.wikipedia.org/wiki/Associative_array

    This is most commonly implemented in the underlying object model, like .Net or Cocoa, which includes standard functions that convert the internal data into text. The program can create a complete text representation of any group of objects by calling these methods, which are almost always already implemented in the base associative array class.

  5. CBOR - Wikipedia

    en.wikipedia.org/wiki/CBOR

    Concise Binary Object Representation (CBOR) is a binary data serialization format loosely based on JSON authored by Carsten Bormann and Paul Hoffman. [a] Like JSON it allows the transmission of data objects that contain name–value pairs, but in a more concise manner.

  6. Machine-readable medium and data - Wikipedia

    en.wikipedia.org/wiki/Machine-readable_medium...

    Machine-readable data may be classified into two groups: human-readable data that is marked up so that it can also be read by machines (e.g. microformats, RDFa, HTML), and data file formats intended principally for processing by machines (CSV, RDF, XML, JSON). These formats are only machine readable if the data contained within them is formally ...

  7. Extract, transform, load - Wikipedia

    en.wikipedia.org/wiki/Extract,_transform,_load

    A common use case for ETL tools include converting CSV files to formats readable by relational databases. A typical translation of millions of records is facilitated by ETL tools that enable users to input csv-like data feeds/files and import them into a database with as little code as possible.

  8. Serialization - Wikipedia

    en.wikipedia.org/wiki/Serialization

    Flow diagram. In computing, serialization (or serialisation, also referred to as pickling in Python) is the process of translating a data structure or object state into a format that can be stored (e.g. files in secondary storage devices, data buffers in primary storage devices) or transmitted (e.g. data streams over computer networks) and reconstructed later (possibly in a different computer ...

  9. Comma-separated values - Wikipedia

    en.wikipedia.org/wiki/Comma-separated_values

    Comma-separated values (CSV) is a text file format that uses commas to separate values, and newlines to separate records. A CSV file stores tabular data (numbers and text) in plain text, where each line of the file typically represents one data record. Each record consists of the same number of fields, and these are separated by commas in the ...