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 a architectural decision in databases, query engines, and numerical simulations.[1]
As a result of these tradeoffs, row-oriented formats are more commonly used in Online transaction processing (OLTP)
and column-oriented formats are more commonly used in Online analytical processing (OLAP).[2]
Examples of column-oriented formats include Apache ORC,[3] Apache Parquet,[4] Apache Arrow,[5] formats used by BigQuery, Amazon Redshift and Snowflake.
Predominant examples of row-oriented formats include CSV, formats used in most relational databases, in-memory format of Apache Spark, and Apache Avro.[6]
^ ab"Column-stores vs. row-stores: how different are they really?". SIGMOD '08: Proceedings of the 2008 ACM SIGMOD international conference on Management of data. doi:10.1145/1376616.1376712.
^ ab"Compacting Transactional Data in Hybrid OLTP&OLAP Databases". Proceedings of the VLDB Endowment. doi:10.14778/2350229.2350258.
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