You can directly import data from Intercom to Treasure Data.


  • Basic knowledge of Treasure Data, including the TD Toolbelt

  • Basic knowledge of Intercom

Use the TD Console to Create Your Connection

Create a New Connection

Go to Integrations Hub > Catalog and search and select Intercom.

Select Create. You are creating an authenticated connection.

The following dialog opens.

Access to Intercom requires OAuth2 authentication.

Select Click here to connect to your Intercom account.

Enter your credentials to sign into Intercom.

After you grant access to Treasure Data you are redirected back to TD Console. Choose the Intercom connector again, then choose the OAuth Authenticate method. You will see an OAuth connection with your account name in the dropdown list. Choose the account you want to use and then proceed to create the connection..

Name your new Google Drive Connection. Select Done.

Previously for this data connector, App id and API Key was used for authentication. However, Intercom started their OAuth flow and Intercom API keys were deprecated.

If you are using Google Sign-In to log into Intercom, make sure that you are already logged in Intercom before starting the OAuth flow. Intercom requires password logins, not Google Sign-In, through the OAuth flow.

Update an existing API key-based connection to OAuth.

Initiate the OAuth flow as you did previously, even if you have been using API keys. OAuth is prioritized over API keys, if both are specified.

Transfer Your Data to Treasure Data

After creating the authenticated connection, you are automatically taken to the Authentications tab. Look for the connection you created and select New Source.

Import From Users and Conversations:

From Source select users or conversations.


  • Incremental: Use when importing data based on a schedule. Use to import only the newest user or conversation created since the last run.

Import From Tags and Segments:

From Source choose tags or segments

Data Preview 

You can see a preview of your data before running the import by selecting Generate Preview.

Data shown in the data preview is approximated from your source. It is not the actual data that is imported.

  1. Select Next.
    Data preview is optional and you can safely skip to the next page of the dialog if you want.

  2. To preview your data, select Generate Preview. Optionally, select Next

  3. Verify that the data looks approximately like you expect it to.

  4. Select Next.

Choose the Target Database and Table

Choose an existing source or create a new database and table.

Create a new database and give your database a name. Complete similar steps to Create new table.

Select whether to append records to an existing table or replace your existing table.

If you want to set a different partition key seed rather than use the default key, you can specify one using the popup menu.

Optionally Schedule the Job

You can use Scheduled Jobs.

1. Navigate to Data Workbench > Queries.
2. Create a new query or select an existing query.
3. Next to Schedule, select None.

4. In the drop-down, select one of the following schedule options.

Drop-down ValueDescription
Custom cron...

Review Custom cron... details.

@daily (midnight)Run once a day at midnight (00:00 am) in the specified time zone.
@hourly (:00)Run every hour at 00 minutes.
NoneNo schedule.

Custom cron... Details

Cron Value


0 * * * *

Run once an hour

0 0 * * *

Run once a day at midnight

0 0 1 * *

Run once a month at midnight on the morning of the first day of the month


Create a job that has no scheduled run time.

 *    *    *    *    *
 -    -    -    -    -
 |    |    |    |    |
 |    |    |    |    +----- day of week (0 - 6) (Sunday=0)
 |    |    |    +---------- month (1 - 12)
 |    |    +--------------- day of month (1 - 31)
 |    +-------------------- hour (0 - 23)
 +------------------------- min (0 - 59)

The following named entries can be used:

  • Day of Week: sun, mon, tue, wed, thu, fri, sat

  • Month: jan, feb, mar, apr, may, jun, jul, aug, sep, oct, nov, dec

A single space is required between each field. The values for each field can be composed of:

Field ValueExampleExample Description

a single value, within the limits displayed above for each field.

a wildcard ‘*’ to indicate no restriction based on the field. 

‘0 0 1 * *’ configures the schedule to run at midnight (00:00) on the first day of each month.
a range ‘2-5’, indicating the range of accepted values for the field.‘0 0 1-10 * *’ configures the schedule to run at midnight (00:00) on the first 10 days of each month.
a list of comma-separated values ‘2,3,4,5’, indicating the list of accepted values for the field.

0 0 1,11,21 * *’

configures the schedule to run at midnight (00:00) every 1st, 11th, and 21st day of each month.
a periodicity indicator ‘*/5’ to express how often based on the field’s valid range of values a schedule is allowed to run.

‘30 */2 1 * *’

configures the schedule to run on the 1st of every month, every 2 hours starting at 00:30. ‘0 0 */5 * *’ configures the schedule to run at midnight (00:00) every 5 days starting on the 5th of each month.
a comma-separated list of any of the above except the ‘*’ wildcard is also supported ‘2,*/5,8-10’‘0 0 5,*/10,25 * *’configures the schedule to run at midnight (00:00) every 5th, 10th, 20th, and 25th day of each month.
5.  (Optional) If you enabled the Delay execution, you can delay the start time of a query.

Execute the Query

Save the query with a name and run, or just run the query. Upon successful completion of the query, the query result is automatically imported to the specified container destination.

Scheduled jobs that continuously fail due to configuration errors may be disabled on the system side after several notifications.


Name your Transfer and select Done to start.

Use Command Line

Install ‘td’ command v0.11.9 or later

You can install the newest TD Toolbelt.

$ td --version

Create Configuration File

Prepare configuration file (for eg: load.yml) as shown in the following example, with your Intercom account access information to:

import Users

  type: intercom 
  access_token: xxxxxxx 
  target: users
  incremental: false
 mode: append

import Conversations

  type: intercom 
  access_token: xxxxxxx 
  target: conversations
  incremental: false
 mode: append

import Segments

  type: intercom 
  access_token: xxxxxxx 
  target: segments
 mode: append

import Tags

  type: intercom
  access_token: xxxxxxx
  target: tags
  mode: append 

Access Token

The preceding example dumps Intercom’s users objects. Here access_token is a valid access token achieved from Intercom. Using the OAuth flow through TD Console is recommended. Your Personal Access Token can be used for access_token instead of the OAuth flow.


You can select which data needs to be fetched from store as target option.

Preview Data (Optional)

You can preview data to be imported using the command td connector:preview.

$ td connector:preview load.yml
| id:string | user_id:string | email:string             | ...
| "1"       | "33"           | ""           |
| "2"       | "34"           | ""           |
| "3"       | "35"           | ""           |
| "4"       | "36"           | ""           |
| "6"       | "37"           | ""           |

Execute Load Job

Submit the load job. It may take a couple of hours depending on the data size. Users need to specify the database and table where their data are stored.

It is recommended to specify --time-column option, since Treasure Data’s storage is partitioned by time. If the option is not given, the Data Connector will choose the first long or timestamp column as the partitioning time. The type of the column specified by --time-column must be either of long and timestamp type.

If your data doesn’t have a time column you may add it using add_time filter option. More details at add_time filter plugin

$ td connector:issue load.yml --database td_sample_db --table td_sample_table --time-column created_at

The preceding command assumes you have already created database(td_sample_db) and table(td_sample_table). If the database or the table do not exist in TD this command will not succeed, so create the database and table manually or use --auto-create-table option with td connector:issue command to auto-create the database and table:

$ td connector:issue load.yml --database td_sample_db --table td_sample_table --time-column created_at --auto-create-table

You can assign Time Format column to the "Partitioning Key" by "--time-column" option.

Scheduled Execution

You can schedule a periodic Data Connector execution for periodic Intercom import. We configure our scheduler carefully to ensure high availability. By using this feature, you no longer need a cron daemon on your local data center.

Create the Schedule

A new schedule can be created using the td connector:create command. The name of the schedule, cron-style schedule, the database and table where their data will be stored, and the Data Connector configuration file are required.

$ td connector:create \
    daily_intercom_import \
    "10 0 * * *" \
    td_sample_db \
    td_sample_table \

The `cron` parameter also accepts these three options: `@hourly`, `@daily` and `@monthly`.

By default, schedule is setup in UTC timezone. You can set the schedule in a timezone using -t or --timezone option. The `--timezone` option only supports extended timezone formats like 'Asia/Tokyo', 'America/Los_Angeles' etc. Timezone abbreviations like PST, CST are *not* supported and may lead to unexpected schedules.

List the Schedules

You can see the list of scheduled entries by td connector:list.

$ td connector:list
| Name                  | Cron         | Timezone | Delay | Database     | Table           | Config                     |
| daily_intercom_import | 10 0 * * *   | UTC      | 0     | td_sample_db | td_sample_table | {"type"=>"intercom", ... } |

Show the Setting and History of Schedules

td connector:show shows the execution setting of a schedule entry.

% td connector:show daily_intercom_import
Name     : daily_intercom_import
Cron     : 10 0 * * *
Timezone : UTC
Delay    : 0
Database : td_sample_db
Table    : td_sample_table

td connector:history shows the execution history of a schedule entry. To investigate the results of each individual execution, use td job <jobid>.

% td connector:history daily_intercom_import
| JobID  | Status  | Records | Database     | Table           | Priority | Started                   | Duration |
| 578066 | success | 10000   | td_sample_db | td_sample_table | 0        | 2015-04-18 00:10:05 +0000 | 160      |
| 577968 | success | 10000   | td_sample_db | td_sample_table | 0        | 2015-04-17 00:10:07 +0000 | 161      |
| 577914 | success | 10000   | td_sample_db | td_sample_table | 0        | 2015-04-16 00:10:03 +0000 | 152      |
| 577872 | success | 10000   | td_sample_db | td_sample_table | 0        | 2015-04-15 00:10:04 +0000 | 163      |
| 577810 | success | 10000   | td_sample_db | td_sample_table | 0        | 2015-04-14 00:10:04 +0000 | 164      |
| 577766 | success | 10000   | td_sample_db | td_sample_table | 0        | 2015-04-13 00:10:04 +0000 | 155      |
| 577710 | success | 10000   | td_sample_db | td_sample_table | 0        | 2015-04-12 00:10:05 +0000 | 156      |
| 577610 | success | 10000   | td_sample_db | td_sample_table | 0        | 2015-04-11 00:10:04 +0000 | 157      |
8 rows in set

Delete the Schedule

td connector:delete will remove the schedule.

$ td connector:delete daily_intercom_import

Modes for Out Plugin

You can specify file import mode in the out section of the load.yml file.

The out: section controls how data is imported into a Treasure Data table.
For example, you may choose to append data or replace data in an existing table in Treasure Data.





Records are appended to the target table.

  mode: append

Always Replace

Replaces data in the target table. Any manual schema changes made to the target table remain intact.

  mode: replace

Replace on new data

Replaces data in the target table only when there is new data to import.

  mode: replace_on_new_data

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