Data ingestion terminology and acronyms

Data that has been loaded and structured so that it can be efficiently queried, reported on, or modeled for analysis.

A destination system, such as a data warehouse or data lake, where ingested data is stored for access, reporting, and analysis.

A load behavior that adds incoming records to the destination without replacing existing records.

An initial or historical load of existing source data into the destination.

A Celigo data ingestion pipeline that moves operational data from source applications into analytical destinations, such as Snowflake or NetSuite Analytics Warehouse.

A Celigo resource that stores the authentication and access details required to connect to a source or destination application.

The process of exporting data from source systems, such as applications, data stores, or files, and loading it into an analytics destination, such as a data warehouse or data lake.

The process of programmatically moving, combining, restructuring, or harmonizing data from one or more systems for use in other systems, reporting, analysis, or operations.

A centralized analytics destination that stores large volumes of raw or lightly processed data for later analysis, transformation, or modeling.

A Celigo tool that loads a single locally stored file into an application on demand while the user is logged in.

A transformation that hides, replaces, or obscures sensitive values before or during loading.

A classification that defines the kind of value a field or column can store, such as text, number, date, or Boolean.

The process of matching source field data types to compatible destination column data types.

A centralized analytics destination designed to store structured data for reporting, querying, and analysis.

The data warehouse or analytics destination into which a sync loads source data.

The schema created in the target database to contain the tables generated and maintained by a sync.

A record of sync events, such as detected schema drift or operational activity.

A lightweight, protocol-agnostic ingestion method utilizing universal HTTP transport mechanisms to systematically stream raw data structures from over 600+ SaaS applications directly into a data store landing zone. (See Extract sync source data from objects vs. exports.)

To retrieve data from a source system for use in a sync, flow, or other integration process.

A data integration pattern in which data is extracted from a source, loaded into a destination in native or near-native form, and transformed after loading.

A data integration pattern in which data is extracted from a source, transformed before loading, and then loaded into a target system.

A modeling approach that converts nested or hierarchical data into a simpler table structure.

The policies, controls, and practices used to manage data access, structure, change handling, and operational oversight.

Data organized in nested parent-child structures, such as records that contain related subrecords or arrays.

A sync run that loads source data beginning from a selected historical date.

A Celigo organizational container that groups related syncs or integrations. For Celigo sync, a workspace contains syncs that use the same destination application.

An analytics architecture that combines characteristics of a data lake and a data warehouse to support flexible storage and analytical workloads.

To write extracted data into a destination system, such as a data warehouse table.

A load behavior that updates matching destination records and inserts new records.

Information that describes data, such as object names, field names, data types, relationships, and other structural characteristics.

A modeling approach that organizes related data into separate, structured tables to support analysis.

The premier Celigo ingestion method where the platform natively reads source system metadata (from applications such as NetSuite, Salesforce, and Shopify) to automatically discover objects, accurately map data types, manage structural changes, and build analytics-ready target tables out-of-the-box without requiring manual database configuration.

A scheduled check for new or changed source data to ingest during an incremental sync.

A field or set of fields used to uniquely identify records in a table or object.

A load behavior that overwrites existing destination data with incoming source data.

An access control model that grants permissions based on assigned user roles.

The Celigo platform execution environment that runs flows and syncs.

A logical structure in a destination database that contains the tables created for a sync.

A change in source or destination structure, such as added or removed objects, added or removed columns, or changed data types.

The application, database, file source, or other system from which a sync exports data.

Metadata supplied by a source application that describes available objects, fields, data types, and related structure.

A configured Celigo sync resource that defines a source application, destination application, selected data, destination schema, settings, and schedule for ingesting data.

To change the structure, format, value, or representation of data so that it meets destination or analytics requirements.

An operational database write behavior that combines “Update” and “Insert.” It scans incoming records against designated primary keys; if a matching key is identified in the target table, the row is updated with new metrics, and if no match exists, a new row is cleanly inserted. Often used interchangeably with “Merge.”