How to load data from Mandrill to Google BigQuery

Blendo Team

There’s a continually growing usage of services like Mandrill. Mandrill  is a transactional email API for Mailchimp Users and it is ideal for sending data-driven emails, including targeted and personalized one-to-one messages to your customers. But that traffic creates more data. How can you analyse the data you generate with Mandrill as part of your transactional email campaigns, on BigQuery? This guide is going to provide you with a clear picture about how to load data from Mandrill to Google BigQuery.  Alternatively, you can use products like Blendo that can handle this kind of problems automatically for you.

ETL your Mandrill data into your Data warehouse

We will use Mandrill’s API to access and extract e-mail related data and load it into Google BigQuery for further analysis. You will need to write the code to get the data and make sure that this process will run every time new data are generated.

What is Mandrill?

Mandrill is a transactional email API for Mailchimp Users. Although in the past, Mandrill was perceived as a different product than Mailchimp, right now it is offered as a Mailchimp plugin. Mandrill is reliable, powerful and ideal for sending data-driven emails, including targeted and personalised one-to-one messages to your customers. You might wonder what are the differences between Mandrill and Mailchimp, as both of them handle the delivery of emails to your customers. Some key differences between the two are the following:

  • Mandrill is designed for developers. If you are not comfortable with writing code then it would be better if you find someone before you start using it. On the other hand, Mailchimp is designed mainly for marketeers so no technical skills are required to use it.
  • Mandrill focuses more on transactional e-mails, which are different than the promotional/campaign based emails that marketeers send using Mailchimp. Transactional e-mails are more tailor made for cases like one to one messages to your customers, like resetting passwords, welcoming them etc.
  • Mailchimp offers richer reporting, but with Mandrill it is easier to have access to all the raw events related to your e-mails as they happen, so you can run your own analytics if you wish. In general, raw data from Mandrill are much more accessible than in Mailchimp.
  • If you wish to run complicated campaigns with Mandrill you will have to implement the logic behind running them, with Mailchimp you can do that using the drag-n-drop environment that it offers.
How to load data from Mandrill to Google BigQuery

How to load data from Mandrill to Redshift

In general, you can perceive the two services as complementary, Mailchimp allows your marketing department to easily and fast execute their marketing strategies, while with Mandrill you have access to a very flexible and rich environment where you can build complex products on top of e-mail services, but it requires the involvement of your R&D team.

Mandrill, as an email API, exposes all its functionality through it. So apart from being possible to execute a number of operations like sending emails based on specific events, we can also use this API to pull out data or setup our infrastructure in such a way that Mandrill will send to us data over time they are generated. In this article, we will see in detail the different ways of pulling data from Mandrill that can be useful for running our analytics.

What is Google BigQuery?

BigQuery is the data warehousing solution of Google. It’s part of the Google Cloud Platform and it also speaks SQL like Redshift does. Queries are executed against append-only tables using the processing power of Google’s infrastructure.

How to Load data from Mandrill to Google BigQuery

How to Load data from Mandrill to Google BigQuery

It is also fully managed and is offered as a service over the cloud. You can interact with it through its web UI, using a command line tool while a variety of client libraries exist so you can interact with it through your application.

How to Extract my data from Mandrill?

There are two main methods to get our data from Mandrill, the first one is to pull data out from it and the second one is to ask Mandrill to push data to us whenever something of importance happen. We will see the difference between these two solutions and how we can access data from both.

In order to pull data from Mandrill, we need to access its HTTP API. As a Web API following the RESTful architecture principles, it can be accessed through HTTP. As a RESTful API, interacting with it can be achieved by using tools like CURL or Postman or by using http clients for your favourite language or framework. A few suggestions:

Mandrill maintains a number of officially supported clients or SDKs that you can use with your favourite language to access it without having to mess with the raw underlying HTTP calls. These are the following:

There are also a number of unofficial clients that you can use if you prefer. The complete list can be found here.

In this post, we will consider the more generic case of accessing the HTTP endpoints directly for our examples, but of course, you are free to use the client of your choice for your project.

Mandrill API Authentication

In order to use the Mandrill API, you first have to generate an API key through your MandrillApp account. When you have created the key you can use it to access the API. You can actually have multiple keys per account something that adds versatility to the platform. In most cases with the Mandrill API, you make a POST call to access an endpoint with a JSON body containing the access key.

Mandrill rate limiting

API rate limiting with Mandrill is a bit of a more complicated matter than in most cases of APIs out there. The reason is that Mandrill is mainly an SMTP as a service platform, in most cases when we make a call to its API we do it in order to send an e-mail to someone, so rate limiting in the typical sense that we find it in web APIs does not apply in Mandrill. What is actually happening, is that every Mandrill account has a reputation and an hourly quota, the main reason that rate limiting is a bit more complicated in Mandrill is because they need to take special care of pointing out and handling potential spammers. So the hourly quota is affected by your reputation, if for example you have poor reputation then Mandrill will reduce the number of e-mails and consequently the API calls that you can do on a per hour basis, on the contrary if you have an excellent reputation you will be able to make more calls. Free accounts can send up to 25 emails per hour. If you want to find your hourly quota and reputation you will have to check your Dashboard in MandrillApp.

Endpoints and available resources

Mandrill exposes the following endpoints:

  • Users: Information about your account, for example here you can validate that your API key is valid.
  • This endpoint is used to send messages through the Mandrill API.
  • Information and operation about user defined tags.
  • Rejects. Manage your email rejection list.
  • Whitelists. Manage your rejection whitelists.
  • Senders. Manage senders associated with your Mandrill account.
  • Get information about the URLs that are included in your e-mails.
  • Manage e-mail templates.
  • Webhooks. Manage webhooks for your account.
  • Subaccounts. Manage subaccounts.
  • Information about domains that have been configured for inbound delivery.
  • Run export jobs to retrieve data from your Mandrill account.
  • IPs. Information and operations about your dedicated IPs.
  • Information and operations about your custom metadata fields indexed for the account.

The above endpoints define the complete set of operations that we perform with Mandrill, in our case we care mainly about what data we can export so we will work with the export endpoint. Export jobs can be executing for the following data:

  • Export your rejection blacklist.
  • Export your rejection whitelist.
  • Export your activity history.

We assume that you would like to export your activity data. In order to do that you need to perform a POST request to the following endpoint:

/exports/activity.json

Keep in mind that the base url might change depending on the warehouse where your application is hosted. For this reason we will mention only the endpoints and you will have to prepend the base url for your case.

The body that we should post to the above end-point should look like this.

{
    "key": "example key",
    "notify_email": "notify_email@example.com",
    "date_from": "2013-01-01 12:53:01",
    "date_to": "2013-01-06 13:42:18",
    "tags": [
        "example-tag"
    ],
    "senders": [
        "test@example.com"
    ],
    "states": [
        "sent"
    ],
    "api_keys": [
        "ONzNrsmbtNXoIKyfPmjnig"
    ]
}

We need to provide our api key, and we can also define a date range from which the API will collect data for. If we want we can filter even more the data we will get back by requesting specific tags or senders and states.

The results will include fields about:

  • Date
  • Email address
  • Sender
  • Subject
  • Status
  • Tags
  • Opens
  • Clicks
  • bounce details

When the export job finishes, the data will be available through a URL in a gziped format. Keep in mind that you will have to poll the Exports endpoint to figure out when the job is finished and get the exact url from which you will get the data. To do that you need to perform a POST request to the following end-point:

/exports/info.json

The body of the POST request should be a JSON document containing your api-key. You will get back a result like the following:

{
    "id": "2013-01-01 12:20:28.13842",
    "created_at": "2013-01-01 12:30:28",
    "type": "activity",
    "finished_at": "2013-01-01 12:35:52",
    "state": "working",
    "result_url": "http://exports.mandrillapp.com/example/export.zip"
}

As you can see from the response, we get a URL from which we can fetch the data and information about the completion or not of the job, if the state of the job is “complete” then we can safely download the data and further process it.

Another way of getting data from the Mandrill API is to ask it to push events to our system every time something of importance to happen. To do that, we need to setup web hooks on our system and provide the URLs to Mandrill. The platform will POST data in JSON format to these URLs every time an event is triggered. The good thing about this mechanism is that we can have the data as soon as possible in our system for analysis.

 Every Mandrill webhook uses the same general data format, regardless of the event type. The webhook request is a standard POST request with a single parameter (currently) – mandrill_events.

There are three types of webhooks that Mandrill currently POSTs: Message webhooks (such as when a message is sent, opened, clicked, rejected, deferred, or bounced), Sync webhooks, and Inbound webhooks.

The mandrill_events parameter contains a JSON-encoded array of webhook events, up to a maximum of 1000 events. Each element in the array is a single event, such as an open, click, or blacklist sync event. For examples of each type of event and a description of the keys, select the type of events you’ll be processing:

Message events (send, deferral, hard-bounce, soft-bounce, open, click, spam, unsub, reject)

Sync events (whitelist of blacklist sync)

Inbound messages

For more information about Webhooks, you can check here.

 

How can I prepare my data to be sent from Mandrill to Google BigQuery?

Before you load your data into BigQuery, you should make sure that it is presented in a format supported by it, so for example if the API you pull data from returns XML you have to first transform it into a serialisation that BigQuery understands. Currently two data formats are supported:

You also need to make sure that the data types you are using are the ones supported by BigQuery, which are the following:

  • STRING
  • INTEGER
  • FLOAT
  • BOOLEAN
  • RECORD
  • TIMESTAMP

for more information please check the Preparing Data for BigQuery page on the documentation.

Load Data from Mandrill to Google BigQuery

If you want to load data from Mandrill to Google BigQuery, you have to use one of the following supported data sources.

  1. Google Cloud Storage
  2. Sent data directly to BigQuery with a POST request
  3. Google Cloud Datastore Backup
  4. Streaming insert
  5. App Engine log files
  6. Cloud Storage logs

From the above list of sources, 5 and 6 are not applicable in our case.

For Google Cloud Storage, you first have to load your data into it, there are a few options on how to do this, for example you can use the console directly as it is described here and do not forget to follow the best practices. Another option is to post your data through the JSON API, as we see again APIs play an important role in both the extraction but also the loading of data into our data warehouse.. In it’s simplest case it’s just a matter of one HTTP POST request using a tool like CURL or Postman. It should look like the following example.

POST /upload/storage/v1/b/myBucket/o?uploadType=media&name=myObject HTTP/1.1 Host: www.googleapis.com Content-Type: application/text Content-Length: number_of_bytes_in_file Authorization: Bearer your_auth_token your Mandrill data

 

and if everything went ok, you should get something like the following as a response from the server:

HTTP/1.1 200 Content-Type: application/json { "name": "myObject" }

 

Working with Curl or Postman, is good only for testing, if you would like to automate the process of loading your data into Google Bigquery, you should write some code to send your data to Google Cloud Storage. In case you are developing on the Google App Engine you can use the library that is available for the languages that are supported by it:

  1. Python
  2. Java
  3. PHP
  4. Go

If you are using one of the above languages and you are not coding for the Google App Engine, you can use it to access the Cloud Storage from your environment. Interacting such a feature rich product like Google Cloud Storage can become quite complicated depending on your use case, for more details on the different options that exist you can check Google Cloud Storage documentation. If you are looking for a less engaged and more neutral way of using Cloud Storage, you can consider a solution like Blendo.

After you have loaded your data into Google Cloud Storage, you have to create a Load Job for BigQuery to actually load the data into it, this Job should point to the source data in Cloud Storage that have to be imported, this happens by providing source URIs that point to the appropriate objects.

The previous method described, used a POST request to the Google Cloud Storage API for storing the data there and then load it into BigQuery. Another way to go is to do a direct HTTP POST request to BigQuery with the data you would like to query. This approach is similar to how we loaded the data to Google Cloud Storage through the JSON API, but it uses the appropriate end-points of BigQuery to load the data there directly. The way to interact with it is quite similar, for more information can be found on the Google BigQuery API Reference and on the page that describes how to load data into BigQuery using POST. You can interact with it using the HTTP client library of the language or framework of your choice, a few options are:

 

What is the best way to load data from Mandrill to Google BigQuery? Which are the possible alternatives?

So far we just scraped the surface of what can be done with Google BigQuery and how to load data into it. The way to proceed relies heavily on the data you want to load, from which service they are coming from and the requirements of your use case. Things can get even more complicated if you want to integrate data coming from different sources. A possible alternative, instead of writing, hosting and maintaining a flexible data infrastructure, is to use a product like Blendo that can handle this kind of problems automatically for you.

Blendo integrates with multiple sources or services like databases, CRM, email campaigns, analytics and more. Quickly and safely move all your data from Mandrill to Google BigQuery and start generating insights from your data.