Responsys to QuickSight

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

Oracle Responsys, a component of Oracle Marketing Cloud, lets organizations manage and orchestrate marketing campaigns and interactions with customers across email, mobile, social, display, and the web. Responsys provides cross-channel orchestration of customer touchpoints using the medium(s) customers prefer.

What is QuickSight?

Amazon QuickSight is the AWS business intelligence tool for creating dashboards and visualizations. Users are charged per session only for the time when they access dashboards or reports. QuickSight supports a variety of data sources, such as individual databases (Amazon Aurora, MariaDB, and Microsoft SQL Server), data warehouses (Amazon Redshift and Snowflake), and SaaS sources (Adobe Analytics, GitHub, and Salesforce), along with several common standard file formats.

Getting data out of Responsys

Responsys has a REST API that you can use to get at information stored in the platform. For example, to retrieve an email or push campaign schedule, you would call GET /rest/api/v1.3/campaigns/{campaignName}/schedule/{scheduleId}.

Sample Responsys data

Here's an example of the kind of response you might see with a query like the one above.

{
    "id": 1491,
    "scheduleType": "ONCE",
    "scheduledTime": "2019-01-25 06:00 AM",
    "launchOptions": {
        "proofLaunch": true,
        "proofLaunchEmail": "someemail@a.com",
        "proofLaunchType": "LAUNCH_TO_ADDRESS",
        "recipientLimit": 3,
        "samplingNthSelection": 1,
        "samplingNthOffset": 1,
        "samplingNthInterval": 1,
        "progressEmailAddresses": [
            "email1@a.com",
            "email2@a.com"
        ],
        "progressChunk": "CHUNK_10K",
        "links": [
            {
                "rel": "self",
                "href": "/rest/api/v1.3/campaigns/test/schedule/1491",
                "method": "POST"
            },
            {
                "rel": "createSchedule",
                "href": "/rest/api/v1.3/campaigns/test/schedule",
                "method": "GET"
            },
            {
                "rel": "updateSchedule",
                "href": "rest/api/v1.3/campaigns/test/schedule/1491",
                "method": "PUT"
            },
            {
                "rel": "deleteSchedule",
                "href": "rest/api/v1.3/campaigns/test/schedule/1491",
                "method": "DELETE"
            }
        ]
    }
}

Preparing Responsys 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. Responsys'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. In these cases you'll likely have to create additional tables to capture the unpredictable cardinality in each record.

Loading data into QuickSight

You must replicate data from your SaaS applications to a data warehouse (such as Redshift) before you can report on it using QuickSight. Once you specify a data source you want to connect to, you must specify a host name and port, database name, and username and password to get access to the data. You then choose the schema you want to work with, and a table within that schema. You can add additional tables by specifying them as new datasets from the main QuickSight page.

Using data in QuickSight

QuickSights provides both a visual report builder and the ability to use SQL to select, join, and sort data. QuickSight lets you combine visualizations into dashboards that you can share with others, and automatically generate and send reports via email.

Keeping Responsys data up to date

At this point you've coded up a script or written a program to get the data you want and successfully moved it into your data warehouse. But how will you load new or updated data? It's not a good idea to replicate all of your data each time you have updated records. That process would be painfully slow and resource-intensive.

Responsys lacks key fields that a script could use to bookmark its progression as it looks for updated data. However, you can create .csv or .txt files as part of a Responsys Connect data export job and use a date/time prefix or suffix in the file names. You could then set up your script as a cron job or continuous loop to get new data as it's exported from Responsys.

From Responsys to your data warehouse: An easier solution

As mentioned earlier, the best practice for analyzing Responsys data in Amazon QuickSight 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 Responsys to Redshift, Responsys to BigQuery, Responsys to Azure SQL Data Warehouse, Responsys to PostgreSQL, Responsys to Panoply, and Responsys 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 Responsys with Amazon QuickSight. With just a few clicks, Stitch starts extracting your Responsys 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 Amazon QuickSight.