Branch to Metabase

This page provides you with instructions on how to extract data from Branch and analyze it in Metabase. (If the mechanics of extracting data from Branch seem too complex or difficult to maintain, check out Stitch, which can do all the heavy lifting for you in just a few clicks.)

What is Branch?

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.

What is Metabase?

Metabase provides a visual query builder that lets users generate simple charts and dashboards, and supports SQL for gathering data for more complex business intelligence visualizations. It runs as a JAR file, and its developers make it available in a Docker container and on Heroku and AWS. Metabase is free of cost and open source, licensed under the AGPL.

Getting data out of Branch

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.

Sample Branch data

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',
}

Preparing Branch data

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.

Loading data into Metabase

Metabase works with data in databases; you can't use it as a front end for a SaaS application without replicating the data to a data warehouse first. Out of the box Metabase supports 15 database sources, and you can download 10 additional third-party database drivers, or write your own. Once you specify the source, you must specify a host name and port, database name, and username and password to get access to the data.

Using data in Metabase

Metabase supports three kinds of queries: simple, custom, and SQL. Users create simple queries entirely through a visual drag-and-drop interface. Custom queries use a notebook-style editor that lets users select, filter, summarize, and otherwise customize the presentation of the data. The SQL editor lets users type or paste in SQL queries.

Keeping Branch data up to date

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.

From Branch to your data warehouse: An easier solution

As mentioned earlier, the best practice for analyzing Branch data in Metabase is to store that data inside a data warehousing platform alongside data from your other databases and third-party sources. You can find instructions for doing these extractions for leading warehouses on our sister sites Branch to Redshift, Branch to BigQuery, Branch to Azure Synapse Analytics, Branch to PostgreSQL, Branch to Panoply, and Branch to Snowflake.

Easier yet, however, is using a solution that does all that work for you. Products like Stitch were built to move data automatically, making it easy to integrate Branch with Metabase. With just a few clicks, Stitch starts extracting your Branch data, structuring it in a way that's optimized for analysis, and inserting that data into a data warehouse that can be easily accessed and analyzed by Metabase.