Glossary

Version 24.3.9110


Glossary

Version 24.3.9110


The following table provides a set of standard terms used in the product’s documentation:

Term Definition
APIScript An XML-based language included with CData Sync that you can use to write custom processing logic. APIScript makes it easy to call external processes, enabling users to integrate Sync with other business processes.
Capture Deletes: A feature that captures deleted records. This feature automatically retrieves a list of deleted records from the source by calling the API or by using the Change Tracking feature.
Change Tracking: A form of incremental update that Sync uses when no Last Modified column is available. Change Tracking records information whenever data manipulation language (DML) statements modify rows in the tracked table. Sync queries the internal change-tracking table for records that change, and it updates the destination table accordingly.
Column Mapping: A method to control how each column in the data source maps to a column in the destination.
Connection: A configured instance of a connector. Connections can include configured settings like username, password, and so on.
Connector: A pointer or link between two data structures (for example, databases and data warehouses). CData Sync comes pre-installed with a select list of popular source and destination data connections. In addition, you can download many more connectors from the CData website.
Extract, Load, Transform (ELT): A method of data transformation in which data is extracted and loaded to a destination in its entirety. Then, the data is transformed in the destination.
Extract, Transform, Load (ETL): A method of data transformation in which the data is transformed between extraction and loading to the destination.
Formatters: Formatters that support modifying or formatting values within a script. You can use value formatters to perform string, date, and math operations on values.
Incremental Check Column: A column that works with most data sources to selectively update only records that have been modified since the last run, using the table’s Last Modified column. Updates by incremental check column can work using two different data types: DateTime incremental check columns and iInteger-Based incremental check columns.
Job: A unit of execution that include the source connection, the destination connection, and a set of one or more tasks that represent data flows for different tables.
Job History: A table that contains the results of every job that has been executed in the past. In addition to the job history, the execution details of a job are logged to multiple files. You can configure logs to various levels of verbosity, which can be helpful in tracking connectivity errors or troubleshooting other problems in job execution.
Query: A query is a request for a database’s data.
Replicate Interval: The time period into which data is split when it is retrieved during the initial load. This interval is used to batch the updates so that if a failure occurs or the replication is interrupted, the next attempt can continue where the last run left off. By default, Sync uses 180 days, but you can adjust this number to be larger or smaller depending on the amount of data you have and how spread out the data is in terms of time.
Replicate Start Value: An option that enables users to set the minimum start date and int value to begin replicating data. This feature is useful when CData Sync does not detect the Min Date/ Int value automatically. The accepted date format is (yyyy-MM-dd).
REPLICATE Query: CData Sync manages all transformations in a declarative fashion, using a special purpose SQL command: REPLICATE. The REPLICATE command lets you define the data that is selected, the transformations that are applied, and map the data to a destination table.
Task: A unit of work that controls the data flow from a source into a destination table. In a standard replication job, all source tables and views are available to be added to the job as replication tasks.
Transformations: A way of transforming, cleansing, or aggregating data in way that makes it easy for reporting or data analysis. In CData Sync, transformations are special types of jobs that execute in the destination after data is transferred. These jobs can be composed of any number of SQL statements.