# Create a Sync from BigQuery to Batch Profile attributes

## Before you start

To create a BigQuery → Batch sync, you’ll need:

* Access to the **Batch dashboard**
* A **BigQuery table or view** containing one row per profile
* A **Google Cloud service account key (JSON)** to grant Batch read access
* A table (or view) that follows the **Cloud Sync input format** (see below)

***

## 1) Prepare your BigQuery table

Cloud Sync expects your BigQuery source (table or view) to include:

1. A **profile identifier** (to know which profile to update)
2. A **cursor field** (to know what changed since the last run)
3. Any number of **attribute columns** (sent to Batch as profile attributes)

***

### 1.1 One row per profile

Your source must contain **one row per profile**.\
Each row is interpreted as an update to a single Batch profile.

***

### 1.2 Required columns

Your table (or view) must include:

| Column            | Required | Description                      |
| ----------------- | -------: | -------------------------------- |
| `custom_id`       |        ✅ | The profile identifier in Batch  |
| `last_updated_at` |        ✅ | Cursor used for incremental sync |

**Important:** `last_updated_at` must be updated **every time any synced attribute changes**, otherwise updates may not be picked up by the next run.

***

### 1.3 Attribute naming rules (BigQuery-compatible)

Cloud Sync reads BigQuery columns and converts them into Batch profile attributes.

However, **BigQuery column names cannot contain characters like `$`, `(`, or `)`**. That means you can’t use the exact Profile API formats such as:

* `url(avatar)`
* `date(birthday)`
* `$email_address`

✅ Instead, Cloud Sync relies on **prefixes** in column names to represent typed or native fields.

#### Supported prefixes

| Prefix    | Meaning                                   | Example                |
| --------- | ----------------------------------------- | ---------------------- |
| `date__`  | Date attribute                            | `date__birthday`       |
| `url__`   | URL attribute                             | `url__avatar`          |
| `batch__` | Native profile fields (instead of `$...`) | `batch__email_address` |

***

### 1.4 Example schema (e-commerce)

Here’s a table format you can use as a reference:

```sql
create table customer_data.batch_profiles (
    custom_id string,
    last_updated_at timestamp,

    -- Native profile fields
    batch__email_address string,

    -- Standard attributes
    plan string,
    country string,
    lifetime_value float64,
    is_vip bool,

    -- Typed attributes
    url__avatar string,
    date__birthday string,
    date__last_purchase string
);
```

**How this maps in Batch:**

* `custom_id` identifies the profile
* `batch__email_address` updates the profile’s native email field
* `plan`, `country`, `lifetime_value`, `is_vip` become attributes
* `url__avatar` is interpreted as a URL attribute
* `date__birthday` and `date__last_purchase` are interpreted as date attributes

***

### 1.5 Using a View

If your raw table doesn’t match the expected naming or format, create a **BigQuery View** that converts your schema into the correct conventions.

Example:

```sql
create or replace view customer_data.batch_profiles_view as
select
  cast(u.user_id as string) as custom_id,
  greatest(u.updated_at, o.last_order_updated_at) as last_updated_at,

  u.email as batch__email_address,
  u.country,
  u.plan,

  u.avatar_url as url__avatar,
  format_date('%Y-%m-%d', u.birth_date) as date__birthday,

  o.last_order_date as date__last_purchase,
  o.total_spent as lifetime_value,
  u.is_vip
from raw.users u
left join raw.user_orders_summary o
  on u.user_id = o.user_id;
```

This approach lets you:

* rename fields with the correct prefixes (`batch__`, `date__`, `url__`)
* compute a reliable `last_updated_at`
* ensure you always expose **one row per profile**

***

### 1.6 Handling nulls

If a column value is `NULL`, Batch interprets it as **attribute removal** for that profile.

If you don’t want an attribute removed:

* ensure your view returns a non-null value, or
* exclude the column from the sync entirely.

***

### 1.7 Attributes limits and constraints

When syncing data from BigQuery to Batch, all attributes sent through Cloud Sync must respect the same limits and constraints as the Batch Profile API. See Profile API documentation, [the attributes object](/getting-started/features/customer-engagement-platform.md).

## 2) Create a Service Account key in Google Cloud

Batch uses a **Service Account Key (JSON)** to securely access your BigQuery dataset.

1. Go to **Google Cloud Console → IAM & Admin → Service Accounts**
2. Create a service account (or reuse an existing one)
3. Generate a **JSON key**
4. Grant the service account:
   * `roles/bigquery.jobUser`
   * Dataset-level permission: **BigQuery Data Editor** on the dataset containing your source table/view

***

## 3) Create the Sync in the Batch dashboard

Cloud Sync is configured from the dashboard via a dedicated **Sync module**.

1. Open the **Batch dashboard**
2. Go to **Data → Cloud Sync**
3. Click **Create Sync**
4. Select **BigQuery** as the source

***

### 3.1 Configure your BigQuery connection

Enter:

* **Dataset**
* **Table or View**
* Upload your **Service Account Key (JSON)**

Batch validates the connection before continuing.

***

### 3.2 Configure profile mapping

Cloud Sync applies a simple mapping model:

* `custom_id` → identifies which Batch profile to update
* all other columns → mapped to profile attributes
* `last_updated_at` → used only for incremental sync logic

***

## 4) How incremental sync works

Cloud Sync uses **incremental processing**, which means it does not re-import your full dataset at every run. Instead, it fetches only the rows that changed since the last successful sync.

***

### 4.1 The `last_updated_at` cursor

Batch stores the last successful cursor value internally.

At each run, Batch fetches only rows where:

* `last_updated_at` is greater than the last stored cursor

This makes sync runs faster, more scalable, and more cost-efficient.

***

### 4.2 Inserts, updates, and deletes

Incremental syncs naturally capture:

* ✅ inserts
* ✅ updates

They do **not** automatically capture:

* ❌ deletes

If you need deletions reflected in Batch, rely on a different pipelines or implement soft deletes by setting all attributes to null in the BigQuery view when a profile is deleted.

***

### 4.3 Best practices for reliable incremental syncs

To avoid missing changes:

* Ensure `last_updated_at` updates **every time a synced column changes**
* Avoid timestamps that only reflect partial updates
* Use a View if you need computed fields or type conversions
* Partition or cluster on `last_updated_at` for large datasets

***

## 5) Test and enable your Sync

Before enabling the schedule:

1. Run a **test sync**
2. Verify:
   * Profiles are created or updated correctly
   * `batch__`, `date__`, and `url__` fields are interpreted correctly
   * Null values behave as expected (null → attribute removal)

Once enabled, Batch automatically handles:

* batching
* retries


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