Salesforce Pardot B2B is a marketing automation tool by Salesforce designed for organizations that primarily focus on business-to-business (B2B) sales and marketing automation. This email marketing system offers automated support for the longer timespan and multi-step decisions associated with B2B sales and helps marketers create more leads, generate more pipelines, and empower sales to close more deals.

The Salesforce Pardot input connector automates the end-to-end solution for our customers, which is ingesting data from Pardot into the Treasure Data database and activating a custom segment back to Pardot.

This topic includes:

Prerequisites

• Basic Knowledge of Treasure Data.

• Basic knowledge of Salesforce Pardot.

• A working Salesforce Pardot account.

• Your Business Unit ID.

• You have the ability to create your own Salesforce Pardot connected app, or you have permission to grant access to the Treasure Data app to access your Pardot data.

Requirements and Limitations

• The imported values for timestamp columns are saved as string types in the Treasure Data database; the timezone is not available.
• Use Export API for importing large amounts of data and does not support incremental loading.

Static IP Address of Treasure Data

The static IP address of Treasure Data is the access point and source of the linkage for this Integration. To determine the static IP address, contact your Customer Success representative or Technical support.

Import from Salesforce Pardot via TD Console

Create Authentication

Create a Source

Create a Connection

The Create Source page displays with the Source Table tab selected.

Identify a Source Table

Select Next.

Config the Data Settings

The Data Settings allows you to fine-tune the HTTP request to Salesforce Pardot. Update your designed config settings and select Next.

Preview Your Data

You can see a preview of your data before running the import. The data that displays in the data preview is approximated from your source and no filter is applied. It is not the actual data that is imported.

1. Select Next.
   Data preview is optional; 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 meets your expectations.
4. Select Next.

Define Your Data Placement

Select the target database and table where you want your data placed, and then indicate how often the import should run.

1. Select Data Placement.
2. Select a Database > Selectan existing or Create New Database. Optionally, enter a database name.
3. Select a Table> Select an existing or Create New Table. Optionally, type a table name.
4. Choose the method for importing the data.
   • Append (default): Data import results are appended to the table.
     If the table does not exist, it will be created.
   • Always Replace: Replaces the entire content of an existing table with the result output of the query. If the table does not exist, a new table is created. 
   • Replace on New Data: Only replace the entire content of an existing table with the result output when there is new data.
5. Select the Timestamp-based Partition Key column.
   If you want to set a different partition key seed than the default key, you can specify the long or timestamp column as the partitioning time. As a default time column, it uses upload_time with the add_time filter.
6. Select the Timezone for your data storage.
7. Choose when and how often you want to run this query:
   • Run once:
     • Select Off.
     • Select Scheduling Timezone.
     • Select Create & Run Now.
   • Repeat the query:
     • Select On.
     • Select the Schedule. The UI provides these four options: @hourly, @daily, and @monthly or custom cron.
     • You can also select Delay Transfer and add a delay of execution time.
     • Select Scheduling Timezone.
     • Select Create & Run Now.

To see the results of your transfer, go to Data Workbench > Databases.

Import from Salesforce Pardot via Workflow

You can import data from Salesforce Pardot by using td_load>: operator of workflow. If you have already created a SOURCE, you can run it; if you don't want to create a SOURCE, you can import it using a yml file.

Using a Source or YML File 

Using a Source

+load:
  td_load>: unique_id_of_your_source
  database: ${td.dest_db}
  Table: ${td.dest_table}

Using a yml file

+load:
  td_load>: config/daily_load.yml
  database: ${td.dest_db}
  Table: ${td.dest_table}

Import from Salesforce Pardotvia CLI (Toolbelt)

in:
  type: salesforce_pardot
  client_id: xxxx
  client_secret: yyyy
  refresh_token: zzzz
  data_object: prospect
  business_unit: 0Uv4W00000111111
  filter_by: 'created_at'
  incremental: true
  #  use_export_api: true
  start_time: "2020-12-13 03:20:00"
  end_time: "2021-12-20 03:30:00"
out:
  mode: append

$ td connector:preview load.yml
+-------+---------+----------+---------------------+
| id    | company | customer | created_at          |
+-------+---------+----------+---------------------+
| 11200 | AA Inc. |    David | 2015-03-31 06:12:37 |
| 20313 | BB Imc. |      Tom | 2015-04-01 01:00:07 |
| 32132 | CC Inc. | Fernando | 2015-04-01 10:33:41 |
| 40133 | DD Inc. |    Cesar | 2015-04-02 05:12:32 |
| 93133 | EE Inc. |     Jake | 2015-04-02 14:11:13 |
+-------+---------+----------+---------------------+

Execute Load Job

Submit the load job.
It might take a couple of hours depending on the size of the data. Be sure to specify the Treasure Data database and table where the data should be stored.

Treasure Data also recommends specifying --time-column option because Treasure Data’s storage is partitioned by time (see data partitioning). If this option is not provided, the data connector chooses 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 can add a time column by using add_time filter option. For more details see add_time filter plugin.

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

The connector:issue command assumes that you have already created a database(td_sample_db)and a table(td_sample_table). If the database or the table does not exist in TD, this command fails. 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

The data connector does not sort records on the server-side. To use time-based partitioning effectively, sort records in files beforehand.

If you have a field called time, you don’t have to specify the --time-column option.

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

Import Modes

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.

Scheduling Executions

Create a Schedule Using the TD Toolbelt

A new schedule can be created using the td connector:create command.

$ td connector:create daily_import "10 0 * * *" \
    td_sample_db td_sample_table load.yml

Treasure Data also recommends that you specify the --time-column option, because Treasure Data’s storage is partitioned by time (see also data partitioning).

$ td connector:create daily_import "10 0 * * *" \
    td_sample_db td_sample_table load.yml \
    --time-column created_at

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

By default, the schedule is set up in the UTC timezone. You can set the schedule in a timezone using -t or --timezone option. `--timezone` option supports only extended timezone formats like 'Asia/Tokyo', 'America/Los_Angeles', etc.

List All Schedules

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

$ td connector:list
+--------------+--------------+----------+-------+--------------+-----------------+------------------------- ------+
| Name         | Cron         | Timezone | Delay | Database     | Table           | Config                         |
+--------------+--------------+----------+-------+--------------+-----------------+--------------------------------+
| daily_import | 10 0 * * *   | UTC      | 0     | td_sample_db | td_sample_table | {"in"=>{"type"=>"..","acces... |
+--------------+--------------+----------+-------+--------------+-----------------+--------------------------------+

Show Schedule Settings and History

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

% td connector:show daily_import
Name     : daily_import
Cron     : 10 0 * * *
Timezone : UTC
Delay    : 0
Database : td_sample_db
Table    : td_sample_table
Config
---
in:
  type: salesforce_pardot
  client_id: xxxx
  client_secret: yyyy
  refresh_token: zzzz
  data_object: prospect
  business_unit: 0Uv4W00000111111
  filter_by: 'created_at'
  incremental: true
  ...

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

% td connector:history daily_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 Schedule

td connector:delete removes the schedule.

$ td connector:delete daily_import

FAQs for the Salesforce Pardot Integration

How can I schedule an incremental import for a large data set?

As recommended, when importing a large dataset from Pardot you should use the Export API. However, export API doesn't support incremental import. To solve this issue:

• Use Export API to import to a recent date. For example, today by specifying the Filter By and End Time will import all data to the End Time value.
• Modify the job config, uncheck the Use Export API checkbox, set the Start Time equals the End Time from the previous execution, and set enable the Incremental Loading.