SendGrid to BigQuery

This page provides you with instructions on how to extract data from SendGrid and load it into Google BigQuery. (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 SendGrid?

SendGrid provides a customer communication platform for transactional and marketing email. It allows companies to send email without having to maintain their own email servers.

What is Google BigQuery?

Google BigQuery is a data warehouse that delivers super-fast results from SQL queries, which it accomplishes using a powerful engine dubbed Dremel. With BigQuery, there's no spinning up (and down) clusters of machines as you work with your data. With that said, it's clear why some claim that BigQuery prioritizes querying over administration. It's super fast, and that's the reason why most folks use it.

Getting data out of SendGrid

SendGrid gives customers a number of ways to export data out of its system. It offers Web, SMTP, and SendGrid APIs, and also supports two kinds of webhooks: The Event Webhook POSTs when an email event occurs, such as a bounce or an unsubscribe. The Inbound Email Parse Webhook receives emails and then POSTs their constituent parameters (subject, body, and attachments).

Suppose you wanted a list of all bounced email. You could use the Web API to call GET /v3/suppression/bounces and specify optional parameters for things like start and end times.

Sample SendGrid data

SendGrid’s API returns JSON-format data. The data returned for a "bounced email" call might look like this:

[
  {
    "created": 1443651125,
    "email": "testemail1@test.com",
    "reason": "550 5.1.1 The email account that you tried to reach does not exist. Please try double-checking the recipient's email address for typos or unnecessary spaces. Learn more at  https://support.google.com/mail/answer/6596 o186si2389584ioe.63 - gsmtp ",
    "status": "5.1.1"
  },
  {
    "created": 1433800303,
    "email": "testemail2@testing.com",
    "reason": "550 5.1.1 : Recipient address rejected: User unknown in virtual alias table ",
    "status": "5.1.1"
  }
]

Preparing SendGrid 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. SendGrid'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. This means you'll likely have to create additional tables to capture the unpredictable cardinality in each record.

Loading data into Google BigQuery

Google Cloud Platform offers a helpful guide for loading data into BigQuery. You can use the bq command-line tool to upload the files to your awaiting datasets, adding the correct schema and data type information along the way. The bq load command is your friend here. You can find the syntax in the bq command-line tool quickstart guide. Iterate through this process as many times as it takes to load all of your tables into BigQuery.

Keeping SendGrid 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 SendGrid.

And remember, as with any code, once you write it, you have to maintain it. If SendGrid 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.

Other data warehouse options

BigQuery 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, PostgreSQL, or Snowflake, which are RDBMSes that use similar SQL syntax, or Panoply, which works with Redshift instances. If you're interested in seeing the relevant steps for loading data into one of these platforms, check out To Redshift, 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 solve this problem automatically. With just a few clicks, Stitch starts extracting your SendGrid data via the API, structuring it in a way that is optimized for analysis, and inserting that data into your Google BigQuery data warehouse.