SparkPost to Looker

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

SparkPost is a cloud-based transactional email delivery service that lets businesses send email via SMTP or programmatically via an API, and track the performance of their messaging using an analytics dashboard.

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 SparkPost

SparkPost provides multiple APIs for developers, and supports the use of webhooks to transfer data from its service. For our purposes, the Metrics API might be the most interesting. To use it to get a deliverability metrics summary, for example, you could call GET /api/v1/metrics/deliverability. A dozen optional parameters are available to limit and select the data to be returned.

Sample SparkPost data

The SparkPost API returns data in JSON format. For example, the result of a call to retrieve a deliverability metrics summary might look like this:

{
  "results": [
    {
      "count_targeted": 34432,
      "count_injected": 32323,
      "count_rejected": 2343,
      "count_sent": 34344
    }
  ],
  "links": [
    {
      "href": "/api/v1/metrics/deliverability",
      "rel": "deliverability",
      "method": "GET"
    }
  ]
}

Preparing SparkPost 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. SparkPost's API 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 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 SparkPost 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.

Instead, identify key fields that your script can use to bookmark its progression through the data and use to pick up where it left off as it looks for updated data. Auto-incrementing fields such as updated_at or created_at work best for this. When you've built in this functionality, you can set up your script as a cron job or continuous loop to get new data as it appears in SparkPost.

And remember, as with any code, once you write it, you have to maintain it. If SparkPost modifies its API, or the API sends a field with a datatype your code doesn't recognize, you may have to modify the script. If your users want slightly different information, you definitely will have to.

From SparkPost to your data warehouse: An easier solution

As mentioned earlier, the best practice for analyzing SparkPost 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 SparkPost to Redshift, SparkPost to BigQuery, and SparkPost to Snowflake.

Easier yet, however, is using a solution that does all that work for you. Products like Stitch were built to solve this problem automatically. With just a few clicks, Stitch starts extracting your SparkPost 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.