Bronto to Azure SQL Data Warehouse

This page provides you with instructions on how to extract data from Bronto and load it into Azure SQL Data Warehouse. (If this manual process sounds onerous, 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 Azure SQL Data Warehouse?

Azure SQL Data Warehouse is a cloud-based petabyte-scale columnar database service with controls to manage compute and storage resources independently. It offers encryption of data at rest and dynamic data masking to mask sensitive data on the fly, and it integrates with Azure Active Directory. It can replicate to read-only databases in different geographic regions for load balancing and fault tolerance.

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 Azure SQL Data Warehouse

SQL Data Warehouse provides a multi-step process for loading data. After extracting the data from its source, you can move it to Azure Blob storage or Azure Data Lake Store. You can then use one of three utilities to load the data:

  • AZCopy uses the public internet.
  • Azure ExpressRoute routes the data through a dedicated private connection to Azure, bypassing the public internet by using a VPN or point-to-point Ethernet network.
  • The Azure Data Factory (ADF) cloud service has a gateway that you can install on your local server, then use to create a pipeline to move data to Azure Storage.

From Azure Storage you can load the data into SQL Data Warehouse staging tables by using Microsoft's PolyBase technology. You can run any transformations you need while the data is in staging, then insert it into production tables. Microsoft offers documentation for the whole process.

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.

Other data warehouse options

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

Easier and faster alternatives

If all this sounds a bit overwhelming, don’t be alarmed. If you have all the skills necessary to go through this process, chances are building and maintaining a script like this isn’t a very high-leverage use of your time.

Thankfully, products like Stitch were built to move data from Bronto to Azure SQL Data Warehouse 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 your Azure SQL Data Warehouse data warehouse.