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Data orientation refers to how tabular data is represented in a linear memory model such as in-disk or in-memory.The two most common representations are column-oriented (columnar format) and row-oriented (row format). [1] [2] The choice of data orientation is a trade-off and an architectural decision in databases, query engines, and numerical ...
ISO 8601 is an international standard covering the worldwide exchange and communication of date and time-related data.It is maintained by the International Organization for Standardization (ISO) and was first published in 1988, with updates in 1991, 2000, 2004, and 2019, and an amendment in 2022. [1]
The format dd.mm.yyyy using dots (which denote ordinal numbering) is the traditional German date format, [65] and continues to be the most commonly used. In 1996, the international format yyyy-mm-dd was made the official date format in standardized contexts such as government, education, engineering and sciences.
Arbitrary-length heterogenous arrays with end-marker Arbitrary-length key/value pairs with end-marker Structured Data eXchange Formats (SDXF) Big-endian signed 24-bit or 32-bit integer Big-endian IEEE double Either UTF-8 or ISO 8859-1 encoded List of elements with identical ID and size, preceded by array header with int16 length
Standard format: 1- or 2-digit day, the spelled-out month, and 4-digit year (e.g. 4 February 2023) Civilian format: spelled out month, 1-or 2-digit day, a comma, and the 4-digit year (e.g. February 4, 2023). [12] Date Time Group format, used most often in operation orders. This format uses DDHHMMZMONYY, with DD being the two-digit day, HHMM ...
Trino is an open-source distributed SQL query engine designed to query large data sets distributed over one or more heterogeneous data sources. [1] Trino can query data lakes that contain open column-oriented data file formats like ORC or Parquet [2] [3] residing on different storage systems like HDFS, AWS S3, Google Cloud Storage, or Azure Blob Storage [4] using the Hive [2] and Iceberg [3 ...
Pandas is built around data structures called Series and DataFrames. Data for these collections can be imported from various file formats such as comma-separated values, JSON, Parquet, SQL database tables or queries, and Microsoft Excel. [8] A Series is a 1-dimensional data structure built on top of NumPy's array.
In-memory data structures: One can use hash tables and two-dimensional arrays in memory in conjunction with attribute-grouping metadata to pivot data, one group at a time. This data is written to disk as a flat delimited file, with the internal names for each attribute in the first row: this format can be readily bulk-imported into a relational ...