# Sansan Data Hub Import Integration

Sansan Data Hub helps you organize and integrate customer data within your company and transform it into optimal data for marketing purposes. With this integration, you can import Sansan Data Hub content into Treasure Data.

## Prerequisites

- Basic Knowledge of Treasure Data™.
- Basic knowledge of Sansan Data Hub


## Requirements and Limitations

- Only supports fetching data that is earlier than 90 days


## Static IP Address of Treasure Data Integration

If your security policy requires IP whitelisting, you must add Treasure Data's IP addresses to your allowlist to ensure a successful connection.

Please find the complete list of static IP addresses, organized by region, at the following link:
[https://api-docs.treasuredata.com/en/overview/ip-addresses-integrations-result-workers/](https://api-docs.treasuredata.com/en/overview/ip-addresses-integrations-result-workers/)

## Import from Sansan Data Hub via TD Console

### Create Authentication

1. Perform the following steps to create a new authentication with a set of credentials.
2. Select **Integrations Hub**.
![](/assets/image2021-9-30_14-38-2.ca92fa4ab9277dca95973c6bd413fc662a3f0d04b57d58f7a8c952a29f28bbec.5f7e1b55.png)
3. Select **Catalog**.
4. Search for your Integration in the Catalog; hover your mouse over the icon and select **Create Authentication**.![](/assets/screenshot-2023-10-12-at-13.15.34.723e30e18ab9da9d49655d4b8a1f6bbba6b6bd75a73547e49eb389bde9f93574.5f7e1b55.png)
5. Ensure that the **Credentials** tab is selected and then enter credential information for the integration.


### New Authentication Fields

![](/assets/screenshot-2023-09-14-at-17.27.04.7c3148934a6302945a257d9ee189e2207dce3ebe4c4d6cb4ab6a12906869bf38.5f7e1b55.png)

| Parameter | Description |
|  --- | --- |
| Client ID | Your account client ID |
| Client Secret | Your account client Secret |


1. Enter a name for your authentication, and select **Done**.


### Create a Source

1. Open TD Console.
2. Navigate to **Integrations Hub** > **Authentications**.
3. Locate your new authentication and select **New Source**.


| Parameter | Description |
|  --- | --- |
| Data Transfer Name | You can define the name of your transfer. |
| Authentication | The authentication name that is used to a transfer. |


### Conneciton

1. Type a source name in the Data Transfer Name field.
2. Select **Next**.


![](/assets/screenshot_2023-10-12_at_10_12_29.834b075689dfdb7a00493b742a5d4c7dc1524e5c54a5832e8f97095d02abc476.5f7e1b55.png)

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

### Source Table

1. Edit the parameters
![](/assets/screenshot-2023-09-22-at-06.52.59.4cd48a243d0b666644509f5d35cfdcecc31339473a571854fad00c30c989f785.5f7e1b55.png)


The customer has to complete output settings on the Configuration Sheet (Change Feed API) and submit to Sansan before data integration. Sansan returns the configuration sheet, including feedID and how to get a client ID and Client Secret, etc. You need to the set client ID and client secret to authentication on the Integration hub and set the feedID according to the configuration sheet that they want to import.  There are five types of feedIDs on each configuration sheet:   - 会社（法人・組織）単位 (Organization Unit) - 拠点単位 (Branch/Location Unit) - 人物単位 (Individual Unit) - 名刺単位 (Name Card Unit) - 取込CSV (Import CSV)

| Parameter | Required | Description |
|  --- | --- | --- |
| Feed ID | Yes | ID of the object to be imported |
| Start Date | Yes | Start date |
| End Date | No | End date |
| Incremental | No | Enable job to run in incremental mode |


1. Select **Next**.


### Data Preview

You can see a [preview](/products/customer-data-platform/integration-hub/batch/import/previewing-your-source-data) of your data before running the import by selecting Generate Preview. Data preview is optional and you can safely skip to the next page of the dialog if you choose to.

1. Select **Next**. The Data Preview page opens.
2. If you want to preview your data, select **Generate Preview**.
3. Verify the data.


### Data Placement

For data placement, select the target database and table where you want your data placed and indicate how often the import should run.

1. Select **Next.** Under Storage, you will create a new or select an existing database and create a new or select an existing table for where you want to place the imported data.
2. Select a **Database** > **Select an existing** or **Create New Database**.
3. Optionally, type a database name.
4. Select a **Table**> **Select an existing** or **Create New Table**.
5. Optionally, type a table name.
6. 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.
7. 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.
8. Select the **Timezone** for your data storage.
9. Under **Schedule**, you can choose when and how often you want to run this query.


#### Run once

1. Select **Off**.
2. Select **Scheduling Timezone**.
3. Select **Create & Run Now**.


#### Repeat Regularly

1. Select **On**.
2. Select the **Schedule**. The UI provides these four options: *@hourly*, *@daily* and *@monthly* or custom *cron*.
3. You can also select **Delay Transfer** and add a delay of execution time.
4. Select **Scheduling Timezone**.
5. Select **Create & Run Now**.


After your transfer has run, you can see the results of your transfer in **Data Workbench** > **Databases.**

## Import from Sansan Data Hub via Workflow

You can import data from Sansan Data Hub 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

1. Identify your source.
2. To obtain a unique ID, open the Source list and then filter by Sansan Data Hub.
3. Open the menu and select "Copy Unique ID".
![](/assets/image2021-10-12_12-26-58.09d9b84b0f1f752c7c95b0bc1c2d8e8b7302e5b91c6a3cb5f01309dadf53a604.5f7e1b55.png)
4. Define a workflow task using td_load> operator.



```yaml
+load:
   td_load>: unique_id_of_your_source
   database: ${td.dest_db}
   table: ${td.dest_table}
```

1. Run a workflow.


### Using a yml file

1. Identify your yml file. If you need to create the yml file, review [Amazon S3 Import Integration Using CLI](/int/amazon-s3-import-integration-v2#using-the-cli-to-configure-the-connector) for reference.
2. Define a workflow task using td_load> operator.



```yaml
+load:
   td_load>: unique_id_of_your_source
   database: ${td.dest_db}
   table: ${td.dest_table}
```

1. Run a workflow


### Parameters Reference

| Name | Description | Value | Default Value | Required |
|  --- | --- | --- | --- | --- |
| client_id | client id |  |  | true |
| client_secret | client_secret |  |  | true |
| feed_id | id of feed want to fetch data |  |  | true |
| start_date | start date to fetch data |  |  | true |
| end_date | end date to fetch data |  |  | false |
| incremental | enable to run in incremental mode |  | false | false |
| columns | columns name and datatype |  |  | true |


### Sample Workflow Code

Visit [Treasure Boxes](https://github.com/treasure-data/treasure-boxes/tree/master/td_load/s3) for sample workflow code.

## Import from Sansan Data Hub via CLI (Toolbelt)

Before setting up the connector, install the most current [TD Toolbelt](https://toolbelt.treasuredata.com/).

### Create Seed Configuration File (seed.yml)


```yaml
in:  
  type: sansan_datahub
  client_id: ***
  client_secret: ***
  feed_id: feed_id
  start_date: "2023-07-22T00:00:00.000Z"
  end_date: "2023-08-22T00:00:00.000Z"
  incremental: false
out:
  mode: append
```

### Parameters Reference

| Name | Description | Value | Default Value | Required |
|  --- | --- | --- | --- | --- |
| client_id | client id |  |  | true |
| client_secret | client_secret |  |  | true |
| feed_id | id of feed want to fetch data |  |  | true |
| start_date | start date to fetch data |  |  | true |
| end_date | end date to fetch data |  |  | false |
| incremental | enable to run in incremental mode |  | false | false |
| columns | columns name and datatype |  |  | true |


The data connector imports all files that match the specified prefix.

#### Example

path_prefix: path/to/sample_ –> path/to/sample_201501.csv.gz, path/to/sample_201502.csv.gz, …, path/to/sample_201505.csv.gz

### Generate load.yml

Use *connector:guess*. This command automatically reads the source files and uses logic to guess the file format and its field/columns.


```
$ td connector:guess seed.yml -o load.yml
```

You can open the load.yml to review the file format definitions including file formats, encodings, column names, and types.

#### Example


```yaml
in:  
  type: sansan_datahub
  client_id: ***
  client_secret: ***
  feed_id: feed_id
  start_date: "2023-07-22T00:00:00.000Z"
  end_date: "2023-08-22T00:00:00.000Z"
  incremental: false
  columns:
   - {name: tdb.latestIncomeAccountingTerm, type: string}
   - {name: nta.addressInside_postalCode, type: long}
   ...
out:
  mode: append
```

To preview the data, use the *td connector:preview* command.


```
$ 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 |
+-------+---------+----------+---------------------+
```

The guess command requires more than 3 rows and 2 columns in the source data file because the command assesses the column definition using sample rows from the source data.

If the system detects your column name or column type unexpectedly, modify the load.yml file and preview again.

### 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](https://docs.treasuredata.com/smart/project-product-documentation/data-partitioning-in-treasure-data)). 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](https://docs.treasuredata.com/smart/project-product-documentation/add_time-filter-function).


```
$ 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.

| **Mode** | **Description** | **Examples** |
|  --- | --- | --- |
| Append | Records are appended to the target table. | in:    ...  out:    mode: append |
| Always Replace | Replaces data in the target table.  Any manual schema changes made to the target table remain intact. | in:    ...  out:    mode: replace |
| Replace on new data | Replaces data in the target table only when there is new data to import. | in:    ...  out:    mode: replace_on_new_data |


## Scheduling Executions

You can schedule periodic data connector execution for incremental file import. Treasure Data configures our scheduler carefully to ensure high availability.

For the scheduled import, you can import all files that match the specified prefix and one of these fields by condition:

- If use_modified_time is disabled, the last path is saved for the next execution. On the second and subsequent runs, the connector only imports files that come after the last path in alphabetical order.
- Otherwise, the time that the job is executed is saved for the next execution. On the second and subsequent runs, the connector only imports files that were modified after that execution time in alphabetical order.


### 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](https://docs.treasuredata.com/smart/project-product-documentation/data-partitioning-in-treasure-data)).


```
$ 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. Timezone abbreviations like PST, CST are *not* supported and might lead to unexpected schedules.

### 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"=>"s3",       "access_key_id"... |
+--------------+--------------+----------+-------+--------------+-----------------+--------------------------------+
```

### 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: sansan_datahub
  client_id: ***
  client_secret: ***
  feed_id: feed_id
  start_date: "2023-07-22T00:00:00.000Z"
  end_date: "2023-08-22T00:00:00.000Z"
  incremental: false
  columns:
   - {name: tdb.latestIncomeAccountingTerm, type: string}
   - {name: nta.addressInside_postalCode, type: long}
   ...
```

*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
```