Some common terms have specific meanings in the context of DataChannel documentation.


When we talk about your account, we mean your DataChannel account. You can manage your account using your DataChannel dashboard. When we need to talk about a source or destination account, we call it out by name (for example, your Jira account or your BigQuery account). See also DataChannel dashboard.


Alerts appear on your DataChannel dashboard to give you important information about your DataChannel account. There are two types of alerts: tasks and warnings. Tasks inform you about actions you must take to fix your connectors or transformations. Warnings inform you that something is wrong but is not disrupting your syncs. See also task and warning.


A DataChannel connector is a data pipeline that moves data from your source to your destination. For example, a Salesforce connector moves your data from Salesforce to your destination. Sometimes you might have multiple connectors of the same type. For example, you might have multiple Google Sheets connectors, each moving data from a different Google Sheet.


The cursor is the marker that lets us know where the last DataChannel sync left off in your source data. When we start the next sync, we use the cursor to decide where to begin syncing again. You could think of the cursor as a metaphorical bookmark in your data that lets us start reading where we left off.

The cursor takes different forms depending on the source. For example, in Salesforce, it refers to the last updated timestamp on a particular endpoint, and in PostgreSQL, it refers to an entry in the database’s Write-ahead Logs.


Previously known as data warehouse DataChannel connectors replicate your source data to a destination system. DataChannel currently supports two destination types - data warehouses and data lakes. See our documentation for a list of supported destinations.


Your DataChannel dashboard is the web-based control center for your DataChannel account. Your dashboard provides a comprehensive overview of your account details, including all your connectors and billing information. From your dashboard, you can create and edit connectors, manage your destination, add transformations, add and delete users, upload new schema files, review logs, view alerts, and much more. The best way to learn about the dashboard is to explore it yourself. Your view of the dashboard varies depending on your user permissions. Log in to your DataChannel account to access your DataChannel dashboard.

Incremental sync

Also known as incremental update

Incremental syncs update only new or modified data. After the initial sync, DataChannel connectors sync most tables using incremental updates. We use a variety of mechanisms to capture the changes in the source data, depending on how the source provides change data. During incremental syncs, DataChannel maintains an internal set of progress cursors, which let us track the exact point where our last successful sync left off. Incremental syncs are efficient because they update only the changed data, instead of re-importing whole tables.

Initial sync

Also known as historical sync

During the initial data sync for a connector, DataChannel connects to your source and copies the entire contents of every table that you’ve selected to sync. We sometimes refer to this as the historical sync because during the initial sync, we sync all your data, including data that is old. How long the initial sync takes depends on the amount of data and the limitations of your source. For example, some sources only allow a limited number of API calls. The initial sync does not count towards your monthly active rows.


When DataChannel normalizes data, we organize the data into tables and columns in a way that reduces data redundancy and stores it logically. Normalization divides larger tables into smaller tables and links them using relationships, according to specific rules.


A full re-sync completely overwrites the data in your destination with new data from your source. A table re-sync lets you overwrite the data in a specific table so that you can fix data integrity issues in selected tables without re-syncing the entire connector. Normally, DataChannel uses incremental updates to sync data from your source to your destination, so we only sync data that has changed. However, sometimes the data in your destination and your source get out of sync. Then you need to overwrite existing data in your destination to make it consistent with the source, and a re-sync lets you do that. Read more about our re-sync feature. See also incremental sync.


A database schema defines how the data is organized in a database. It contains the different tables, their fields, and the relationship between tables. When you create a new DataChannel connector, you choose the name that the schema will have and DataChannel creates it in your destination. For most sources, each connector results in one schema in your destination. Database sources are the exception because a single database connector can replicate multiple schemas. Read more about how database connectors handle multiple source schemas.


A source is any database, application, file storage service, event tracking service, or function from which you wish to sync your data. A DataChannel connector connects to your source and moves data from it to your destination. You can have multiple connectors for one source.


A task is a type of alert in your DataChannel dashboard that tells you about an action you must take to fix your connectors or transformations. We also generate a notification email to let you know about the task. DataChannel creates a task when the problem with your connector or transformation is caused by something that’s on your side. For example, if you have set insufficient permissions in your source and DataChannel can’t sync your data, we generate a task that tells you about the problem and what permissions you must set. See also alert and warning.


Transformations are SQL scripts that are executed on your data based on specific events or conditions. Transformations map incoming data into a specific shape that is easier or faster to use in the next part of your data pipeline. DataChannel uses the term "transformation" to refer to two different types of reshaping:

Pre-load transformations: DataChannel performs some minor transformations on your data before we load it into your destination.

Post-load transformations: DataChannel offers a feature called Transformations, which supports custom transformations in the destination after your data is loaded. Read our Transformations documentation to learn how to execute your SQL scripts in your destination.


Warnings are a type of alert in your DataChannel dashboard that tell you about a problem with your connector that you may need to fix but that is not disrupting your data syncs. For example, a warning might tell you that we were unable to sync specific tables or columns because you are still using a column that has been deprecated by your application’s API. See also alert and task.

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