Bronto to Looker

This page provides you with instructions on how to extract data from Bronto and analyze it in Looker. (If the mechanics of extracting data from Bronto 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 Bronto?

Oracle Bronto is an ecommerce email marketing platform. It integrates ecommerce and point-of-sale data with operational platforms, enabling brands to maximize the value of customer data and deliver relevant, personal messages.

What is Looker?

Looker is a powerful, modern business intelligence platform that has become the new standard for how modern enterprises analyze their data. From large corporations to agile startups, savvy companies can leverage Looker's analysis capabilities to monitor the health of their businesses and make more data-driven decisions.

Looker is differentiated from other BI and analysis platforms for a number of reasons. Most notable is the use of LookML, a proprietary language for describing dimensions, aggregates, calculations, and data relationships in a SQL database. LookML enables organizations to abstract the query logic behind their analyses from the content of their reports, making their analytics easy to manage, evolve, and scale.

Getting data out of Bronto

You can use Bronto's API to get Bronto data into your data warehouse. The API was originally designed using the SOAP API protocol, but a new REST API lets you access and work with product and order data.

Bronto's API offers numerous endpoints that can provide information on orders, products, and campaigns. Using methods outlined in the API documentation, you can retrieve the data you need. For example, to get a list of all transactions for a given order object, you could GET /orders/{orderId}.

Sample Bronto data

The Bronto REST API returns JSON-formatted data. Here's an example of the kind of response you might see when querying an objects endpoint.

{
    emailAddress:validly formatted email address
    contactId:string
    orderDate:ISO-8601 datetime
    status:PENDING | PROCESSED
    hasTracking:boolean
    trackingCookieName:string
    trackingCookieValue:string
    deliveryId:string
    customerOrderId:string
    discountAmount:number
    grandTotal:number
    lineItems:[
      {
        name:string
        other:string
        sku:string
        category:string
        imageUrl:string
        productUrl:string
        quantity:number
        salePrice:number
        totalPrice:number
        unitPrice:number
        description:string
        position:number
      }
    ]
    originIp:IPv4 or IPv6 address
    messageId:string
    originUserAgent:string
    shippingAmount:number
    shippingDate:ISO-8601 datetime
    shippingDetails:string
    shippingTrackingUrl:string
    subtotal:number
    taxAmount:number
    cartId:UUID
    createdDate:ISO-8601 datetime
    updatedDate:ISO-8601 datetime
    currency:ISO-4217 currency code
    states: {
      processed:boolean
      shipped:boolean
    }
    orderId:UUID
}

Loading data into Looker

To perform its analyses, Looker connects to your company's database or data warehouse, where the data you want to analyze is stored. Some popular data warehouses include Amazon Redshift, Google BigQuery, and Snowflake.

Looker's documentation offers instructions on how to configure and connect your data warehouse. In most cases, it's simply a matter of creating and copying access credentials, which may include a username, password, and server information. You can then move data from your various data sources into your data warehouse for Looker to use.

Analyzing data in Looker

Once your data warehouse is connected to Looker, you can build constructs known as explores, each of which is a SQL view containing a specific set of data for analysis. An example might be "orders" or "customers."

Once you've selected any given explore, you can filter data based on any column available in the view, group data based on certain fields in the view (known as dimensions), calculate outputs such as sums and counts (known as measures), and pick a visualization type such as a bar chart, pie chart, map, or bubble chart.

Beyond this simple use case, Looker offers a broad universe of functionality that allows you to conduct analyses and share them with your organization. You can get started with this walkthrough in Looker's documentation.

Keeping Bronto data up to date

Now what? You've built a script that pulls data from Bronto and loads it into your data warehouse, but what happens tomorrow when you have new transactions?

The key is to build your script in such a way that it can identify incremental updates to your data. Thankfully, Bronto's API results include fields like createdDate that allow you to identify records that are new since your last update (or since the newest record you've copied). Once you've take new data into account, you can set your script up as a cron job or continuous loop to keep pulling down new data as it appears.

From Bronto to your data warehouse: An easier solution

As mentioned earlier, the best practice for analyzing Bronto data in Looker 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 Bronto to Redshift, Bronto to BigQuery, Bronto to Azure SQL Data Warehouse, Bronto to PostgreSQL, Bronto to Panoply, and Bronto to Snowflake.

Easier yet, however, is using a solution that does all that work for you. Products like Stitch were built to move data from Bronto to Looker automatically. With just a few clicks, Stitch starts extracting your Bronto data via the API, structuring it in a way that is optimized for analysis, and inserting that data into a data warehouse that can be easily accessed and analyzed by Looker.