Gmail is Google’s email service. This connector extracts mailbox metadata and full message content via the Gmail API so you can analyze labels, headers, and message structure in your catalog.

Configuring Gmail as a Source

In the Sources tab, click on the “Add source” button located on the top right of your screen. Then, select the Gmail option from the list of connectors. Click Next and you’ll be prompted to add your access.

1. Add account access

Authorize Nekt to access your Gmail data. Click the Google Authorization button and sign in with the Google account whose mailbox you want to sync. Grant the scopes required for reading messages and labels. The following configurations are available:

Start date: Only messages received on or after this date are included. The connector uses Gmail search (after:YYYY/MM/DD) based on this value.
Include SPAM and Trash: When enabled, messages in Spam and Trash are included in the extraction. When disabled, they follow Gmail’s default visibility for listings.
Enable email attachments stream: (Default: false) When enabled, the source adds an email attachments stream that downloads every message’s attachments and uploads them to a Nekt volume. This makes extractions significantly slower and heavier: each attachment is downloaded from Gmail and re-uploaded to Nekt. Only enable this if you need the email files stored in a Nekt volume.
Attachments Volume: Nekt volume to upload email attachments to. Required when the email attachments stream is enabled.

Once you’re done, click Next.

2. Select streams

Choose which data streams you want to sync. For faster extractions, select only the streams that are relevant to your analysis. You can select entire groups of streams or pick specific ones.

Tip: The stream can be found more easily by typing its name.

Select the streams and click Next.

3. Configure data streams

Customize how you want your data to appear in your catalog. Select the desired layer where the data will be placed, a folder to organize it inside the layer, a name for each table (which will effectively contain the fetched data) and the type of sync.

Layer: choose between the existing layers on your catalog. This is where you will find your new extracted tables as the extraction runs successfully.
Folder: a folder can be created inside the selected layer to group all tables being created from this new data source.
Table name: we suggest a name, but feel free to customize it. You have the option to add a prefix to all tables at once and make this process faster!
Sync Type: you can choose between INCREMENTAL and FULL_TABLE.
- Incremental (recommended for Email): each run brings new or updated messages based on the replication key (internalDate). Fits keeping a growing history of mail in the catalog.
- Full table (typical for Label): each run replaces the table with the current set of labels and their counts from Gmail.

Once you are done configuring, click Next.

4. Configure data source

Describe your data source for easy identification within your organization, not exceeding 140 characters. To define your Trigger, consider how often you want data to be extracted from this source. This decision usually depends on how frequently you need the new table data updated (every day, once a week, or only at specific times). Optionally, you can define some additional settings:

Configure Delta Log Retention and determine for how long we should store old states of this table as it gets updated. Read more about this resource here.
Determine when to execute an Additional Full Sync. This will complement the incremental data extractions, ensuring that your data is completely synchronized with your source every once in a while.

Once you are ready, click Next to finalize the setup.

5. Check your new source

You can view your new source on the Sources page. If needed, manually trigger the source extraction by clicking on the arrow button. Once executed, your data will appear in your Catalog.

For you to be able to see it on your Catalog, you need at least one successful source run.

Streams and Fields

Below you’ll find all available data streams from Gmail and their corresponding fields:

Full Gmail messages for the authenticated user. The connector lists message IDs, then fetches each message resource so rows include headers, MIME structure, and nested parts. Sync is incremental on internalDate (message receive time).Key fields:

Field	Type	Description
`id`	String	Unique identifier of the record.
`threadId`	String	Identifier of the thread this message belongs to.
`labelIds`	Array	Identifiers of labels applied to the message.
`snippet`	String	Short excerpt of the message text.
`internalDate`	DateTime	Timestamp when the message was received by Gmail.
`historyId`	String	Identifier of the last history record that modified this message.
`sizeEstimate`	Integer	Estimated size of the message in bytes.
`payload`	Object	Parsed email structure including headers, body, and nested parts.
`payload.partId`	String	Identifier of the MIME message part.
`payload.mimeType`	String	MIME type of the message part.
`payload.filename`	String	Original filename of the attachment if present.
`payload.headers`	Array	RFC 2822 headers on this message part.
`payload.headers[].name`	String	Name of the email header field.
`payload.headers[].value`	String	Value of the email header field.
`payload.body`	Object	Body of this message part.
`payload.body.size`	Integer	Size of the body in bytes.
`payload.parts`	Array	Child MIME parts for multipart messages.
`payload.parts[].partId`	String	Identifier of the MIME message part.
`payload.parts[].mimeType`	String	MIME type of the message part.
`payload.parts[].filename`	String	Original filename of the attachment if present.
`payload.parts[].headers`	Array	RFC 2822 headers on this message part.
`payload.parts[].headers[].name`	String	Name of the email header field.
`payload.parts[].headers[].value`	String	Value of the email header field.
`payload.parts[].body`	Object	Body of this message part.
`payload.parts[].body.size`	Integer	Size of the body in bytes.
`payload.parts[].body.data`	String	Body data as a base64url-encoded string before decoding.
`payload.parts[].body.attachmentId`	String	Identifier of the attachment when fetched separately from the API.

Email Attachments

Downloads email attachments and uploads them to a Nekt volume. This stream is only active when “Enable email attachments stream” is enabled in the source configuration. It makes one API call per attachment to fetch its bytes and emits one metadata record per file.Key fields:

Field	Type	Description
`message_id`	String	ID of the message the attachment belongs to.
`internalDate`	DateTime	Timestamp when the message was received by Gmail.
`attachment_id`	String	Gmail identifier of the attachment.
`file_name`	String	Original filename of the attachment.
`unique_file_name`	String	Collision-free, filesystem-safe volume key for the attachment.

Label

All labels in the mailbox (system and user-defined), including visibility settings and aggregate counts.Key fields:

Field	Type	Description
`id`	String	Unique identifier of the record.
`name`	String	Name of the record.
`messageListVisibility`	String	Whether the label is shown in the message list.
`labelListVisibility`	String	Whether the label is shown in the label list.
`type`	String	Type classification of the record.
`messagesTotal`	Integer	Total number of messages with this label.
`messagesUnread`	Integer	Number of unread messages with this label.
`threadsTotal`	Integer	Total number of threads with this label.
`threadsUnread`	Integer	Number of unread threads with this label.
`color`	Object	Display colors for the label in the Gmail UI.
`color.textColor`	String	Hex color for the label text.
`color.backgroundColor`	String	Hex color for the label background.

Data Model

Use Cases for Data Analysis

Join messages to label names. Resolve labelIds on each email to human-readable names using the Label table.

Example SQL (Trino / AWS Athena)

SELECT
  e.id AS message_id,
  e.threadId,
  e.internalDate,
  l.name AS label_name
FROM nekt_raw.gmail_email e
CROSS JOIN nekt_raw.gmail_label l
WHERE contains(e.labelIds, l.id)
ORDER BY e.internalDate DESC
LIMIT 100;

Replace schema and table names (nekt_raw, gmail_email, gmail_label) with the layer and table names you configured for the source. On BigQuery, use JOIN LATERAL UNNEST(e.labelIds) AS label_id (or equivalent) instead of contains.

Implementation Notes

Extraction Behavior

Email uses the Gmail messages.list query with maxResults: 500 per page and messages.get per message, so runtime grows with mailbox volume.
The Start date filter applies to the list query; ensure OAuth and API quotas are sufficient for initial backfills.
Label is a single API call (users.labels.list); it does not use incremental state.
The Email Attachments stream extracts each message’s attachment parts and makes a separate API call per attachment to fetch its bytes before uploading to the Nekt volume. Because this process is heavy, it should only be enabled when file extraction is explicitly required.

Payload and Timestamps

internalDate is normalized to Unix time in seconds in post-processing (API returns milliseconds).
Top-level payload.body in the schema exposes size only; inline and attachment content typically appears under payload.parts with optional decoded data. Only text body parts are decoded inline; file attachments carry an attachmentId and are fetched separately.
Threading and deduplication are based on threadId and id as returned by Gmail.

Skills for agents

Download Gmail skills file

Gmail connector documentation as plain markdown, for use in AI agent contexts.

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Gmail as a data source

Configuring Gmail as a Source

1. Add account access

2. Select streams

3. Configure data streams

4. Configure data source

5. Check your new source

Streams and Fields

Data Model

Use Cases for Data Analysis

Implementation Notes

Extraction Behavior

Payload and Timestamps

Skills for agents

Download Gmail skills file

​Configuring Gmail as a Source

​1. Add account access

​2. Select streams

​3. Configure data streams

​4. Configure data source

​5. Check your new source

​Streams and Fields

​Data Model

​Use Cases for Data Analysis

​Implementation Notes

​Extraction Behavior

​Payload and Timestamps

​Skills for agents

Download Gmail skills file

Configuring Gmail as a Source

1. Add account access

2. Select streams

3. Configure data streams

4. Configure data source

5. Check your new source

Streams and Fields

Data Model

Use Cases for Data Analysis

Implementation Notes

Extraction Behavior

Payload and Timestamps

Skills for agents