Bronto to Postgres

This page provides you with instructions on how to extract data from Bronto and load it into PostgreSQL. (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 PostgreSQL?

PostgreSQL, known by most simply as Postgres, is a hugely popular object-relational database management system (ORDBMS). It labels itself as "the world's most advanced open source database," and for good reason. The platform, despite being available for free via an open source license, offers enterprise-grade features including a strong emphasis on extensibility and standards compliance.

It runs on all major operating systems, including Linux, Unix, and Windows. It is fully ACID-compliant, has full support for foreign keys, joins, views, triggers, and stored procedures (in multiple languages). Postgres is often the best tool for the job as a back-end database for web systems and software tools, and cloud-based deployments are offered by most major cloud vendors. Its syntax also forms the basis for querying Amazon Redshift, which makes migration between the two systems relatively painless and makes Postgres a good "first step" for developers who may later expand into Redshift's data warehouse platform.

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 Postgres

Once you have identified all of the columns you will want to insert, you can use the CREATE TABLE statement in Postgres to create a table that can receive all of this data. Then, Postgres offers a number of methods for loading in data, and the best method varies depending on the quantity of data you have and the regularity with which you plan to load it.

For simple, day-to-day data insertion, running INSERT queries against the database directly are the standard SQL method for getting data added. Documentation on INSERT queries and their bretheren can be found in the Postgres documentation here.

For bulk insertions of data, which you will likely want to conduct if you have a high volume of data to load, other tools exist as well. This is where the COPY command becomes quite useful, as it allows you to load large sets of data into Postgres without needing to run a series of INSERT statements. Documentation can be found here.

The Postgres documentation also provides a helpful overall guide for conducting fast data inserts, populating your database, and avoiding common pitfalls in the process. You can find it here.

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

PostgreSQL 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, 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. If you're interested in seeing the relevant steps for loading data into one of these platforms, check out To Redshift, To BigQuery, To Snowflake, To Panoply, To Azure SQL Data Warehouse, and To S3.

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 PostgreSQL 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 PostgreSQL data warehouse.