Advanced integrations

How it works

This integration forwards incoming data from data sources to your storage bucket independently of your workspace. This means that you can send data to your storage bucket even if you don’t store that data in Customer.io. It also means that this integration does not have access to automation, broadcast, or journey information from your workspace. If you need that data, you should use a standard Data Warehouse integration instead.

Rather than streaming data to storage buckets in real time, like with most other data-out integrations, our data warehouse and storage integrations send data to your storage buckets in bulk at regular, 10-minute intervals. When we load data, we insert and update events, people, and groups, in JSON, CSV, or parquet files that we upload to your storage bucket. You can then ingest those files into the data warehouse or database of your choice.

These integrations only create new files in your storage bucket; they’ll never overwrite or append an existing file, so you can delete or remove files from your storage bucket after you ingest them into their ultimate destination—your data warehouse or database.

Exported files

Our data warehouse and cloud storage integrations generate parquet, JSON, or CSV files that we load in a storage bucket you specify. The data we send (the files we generate in your storage bucket) are based on the Actions you enable.

Each sync generates new files for each data type in your storage bucket. Files are named in the format <integration id>.<action id>.<current position>.<type>.

• The integration ID and action ID are unique identifiers generated by Customer.io. You’ll see them with the first sync.
• current position is an incrementing number beginning at 1 that indicates the order of syncs. So your first sync is 1, the next one is 2, etc.
• type is the type of call—identify, track, page, screen, alias, or group.

So, if your file is called 2184.13699.1.track.json, it’s the first sync file for the track call type.

Sync frequency

Unlike other integrations where we send data in real time, these kinds of integrations attempt to send data to your storage bucket every 10 minutes—though actual sync intervals and processing times may vary. When syncing large data sets, or when you have a high volume of concurrent sync operations, it can take a little longer to process and export data.

Each sync file contains data from the previous sync interval. For example, if the last sync occurred at 12:00 PM, the next sync will only send data from 12:00 PM to 12:09:59 PM.

Handling objects and arrays in CSV and Parquet files

Our incoming integrations pass nested objects and arrays into calls as properties and traits, but CSVs and Parquet files don’t have a concept of objects or arrays. So we stringify or flatten properties and traits in CSVs and Parquet files to preserve your data without significantly manipulating it.

{
  "received_at": "2019-08-24T14:15:22Z",
  "id": "a7280cfea0f6d",
  "user_id": "97980cfea0067",
  "anonymous_id": "d19b0cfeb606a",
  "sent_at": "2019-08-24T14:15:22Z",
  "traits": {
    "name": "Cool Person",
    "email": "cool.person@example.com",
    "likes_baseball": true
  },
  "context": {
    ...
  }
}

received_at,id,user_id,anonymous_id,sent_at,traits,context
2019-08-24T14:15:22Z,a7280cfea0f6d,97980cfea0067,d19b0cfeb606a,2019-08-24T14:15:22Z,"{\"name\": \"Cool Person\", \"email\": \"cool.person@example.com\", \"likes_baseball\": true}", "{...}"

Schemas

When we load data into your storage buckets, we create and update files to match the shape of your incoming data. Note that we flatten or stringify nested objects and arrays according to the rules above.

Identifies schema

Identifies files contain identify calls sent into Customer.io. The context and traits in the schema below are objects in JSON. In CSV and parquet files, these columns contain stringified objects.

Groups schema

Groups files contain group calls made from your data-in integrations. If your integration outputs CSV or parquet files, the context and traits columns contain stringified objects.

Page schema

Pages contains entries for the page calls your integrations send into Customer.io. If your integration outputs CSV or parquet files, the context and properties columns contain stringified objects. If your integration outputs JSON files, the context and properties columns contain objects.

Screen schema

Screens files contain entries for the screen calls sent to Customer.io. If your integration outputs CSV or parquet files, the context and properties columns contain stringified objects. If your integration outputs JSON files, the context and properties columns contain objects.

Track Schema

Tracks contains entries for the track calls you send to Customer.io. It shows information about the events your users perform.

If your integration outputs CSV or parquet files, the context and properties columns contain stringified objects. If your integration outputs JSON files, the context and properties columns contain objects.

Alias

The Alias schema contains entries for the alias calls you send to Customer.io. It shows information about the users you merge, with each entry showing a user’s new user_id and their previous_id.

Timestamps

We associate four timestamps with every incoming call to Customer.io: timestamp, original_timestamp, sent_at and received_at. All four timestamps pass through to your warehouse, and it may help to understand the purpose of each.

In general, you should use timestamp when you query for historical events and received_at for all other queries based on time.

timestamp is the UTC-converted timestamp set by the Customer.io library. If you import historical events using a server-side library, this is the timestamp you’ll want to reference in your queries.

original_timestamp is the original timestamp set on data that comes into Customer.io. This timestamp can be affected by device clock skew. You can override this value by manually passing a timestamp in your incoming calls, which we map to the original_timestamp. Generally, this timestamp should be ignored in favor of the timestamp column.

sent_at is a UTC timestamp set when you send calls to Customer.io. This timestamp can also be affected by device clock skew.

received_at is a UTC timestamp set by Customer.io when we receive a payload. All tables use received_at as the sort key.

id

Each row in your database has an id which is equivalent to the messageId that our libraries pass in incoming calls. This is a unique identifier associated with the row.

Sort Key

All tables use received_at as the sort key. Amazon Redshift stores your data on disk in sorted order according to the sort key. The Redshift query optimizer uses sort order when it determines optimal query plans.