Native Object Store Terminology - Teradata VantageCloud Lake

Lake - Manage and Move Data

Deployment
VantageCloud
Edition
Lake
Product
Teradata VantageCloud Lake
Release Number
Published
February 2025
ft:locale
en-US
ft:lastEdition
2025-05-16
dita:mapPath
atx1683670417382.ditamap
dita:ditavalPath
pny1626732985837.ditaval
dita:id
atx1683670417382
Files and Objects
Files and objects can be used interchangeably to describe the components of external object storage. Each file or object contains records in which the data itself is held.
Foreign Table
Foreign tables carry information about the location of the external object storage and other definitional information, and are the main vehicle within Analytics Database for reading external data. A foreign table may support access to the entire external object storage, or a subset of an external object storage.
Key and Path
A key in external object storage data is the unique identifier for an object and may contain multiple logical levels.

Path names are identifiers or pointers to an object; a path name is the entire key.

A path prefix is a subset of the path. For example, if the path of an object is /td-usgs/bucket/a/b/c/d/object1, then a path prefix can be any of the following, amongst others:
  • a/b/c
  • a/b
  • a/b/c/d/object1
Object and Objects stores
Objects are the discrete units that compose external object storage. Objects can be organized with shared names called prefixes.

Every object has a unique key or path. However, objects may be identified by or share a common path prefix. For example, /a/b/c/d can contain hundreds of objects.

External object storage is a collection of related objects, with all participating objects located in the same bucket or container and organized in a hierarchy.
Records and Rows
Row is a relational concept that refers to part of a table.
Record is a logical grouping of values within an object.
For CSV and JSON formatted data, the database either converts each record into a row or convert a record with an array at the top level into a set of rows.
For Parquet formatted data, fields are grouped together (similar to columnar). Then individual field values are converted to a Teradata column value.