Branch Metrics lets businesses generate deep links they can use to track conversions and user engagement on web and mobile transactions. It provides a business analytics dashboard to surface user behavior data.

When it was released in 2013, Amazon Redshift was the first cloud data warehouse. It uses defined schemas, columnar data storage, and massively parallel processing (MPP) architecture to provide a base for analytics reporting.

Branch exposes data for things like install, open, clicks, and web session start through webhooks to user-defined HTTP POST callbacks. You can add a webhook through the Branch dashboard.

Branch exchanges data in JSON format. Here’s an example of the data returned for a clicks endpoint:

POST
User-agent: Branch Metrics API
Content-Type: application/json
{
    click_id: a unique identifier,
    event: 'click',
    event_timestamp: 'link click timestamp',
    os: 'iOS' | 'Android',
    os_version: 'the OS version',
    metadata: {
        ip: 'click IP',
        userAgent: 'click UA',
        browser: 'browser',
        browser_version: 'browser version',
        brand: 'phone brand',
        model: 'phone model',
        os: 'browser OS',
        os_version: 'OS version'
    },
    query: { any query parameters appended to the link },
    link_data: { link data dictionary - see below }
}

// link data dictionary example
{
    branch_id: 'unique identifier for unique link',
    date_ms: 'link creation date with millisecond',
    date_sec: 'link creation date with second',
    date: 'link creation date',
    domain: 'domain label',
    data: {
        +url: the Branch link,
        ... other deep link data
    },
    campaign: 'campaign label',
    feature: 'feature label',
    channel: 'channel label'
    tags: [tags array],
    stage: 'stage label',
}

If you don’t already have a data structure in which to store the data you retrieve, you’ll have to create a schema for your data tables. Then, for each value in the response, you’ll need to identify a predefined datatype (INTEGER, DATETIME, etc.) and build a table that can receive them. Branch's documentation should tell you what fields are provided by each endpoint, along with their corresponding datatypes.

Complicating things is the fact that the records retrieved from the source may not always be "flat" – some of the objects may actually be lists. This means you’ll likely have to create additional tables to capture the unpredictable cardinality in each record.

Once you've identified the columns you want to insert, you can use Redshift's CREATE TABLE statement to define a table to receive all of the data.

With a table built, you might be tempted to migrate your data (especially if there isn't much of it) by using INSERT statements to add data to your Redshift table row by row. Not so fast! Redshift isn't optimized for inserting data one row at a time. If you have a high volume of data to be inserted, you should load the data into Amazon S3 and use the COPY command to load it into Redshift.

Once you’ve set up the webhooks you want and have begun collecting data, you can relax – as long as everything continues to work correctly. You’ll have to keep an eye out for any changes to Branch’s webhooks implementation.

Redshift is great, but sometimes you need to optimize for different things when you're choosing a data warehouse. Some folks choose to go with Google BigQuery, PostgreSQL, Snowflake, or Microsoft Azure SQL Data Warehouse, which are RDBMSes that use similar SQL syntax, or Panoply, which works with Redshift instances. Others choose a data lake, like Amazon S3 or Delta Lake on Databricks. If you're interested in seeing the relevant steps for loading data into one of these platforms, check out To BigQuery, To Postgres, To Snowflake, To Panoply, To Azure Synapse Analytics, To S3, and To Delta Lake.