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.

Delta Lake is an open source storage layer that sits on top of existing data lake file storage, such AWS S3, Azure Data Lake Storage, or HDFS. It uses versioned Apache Parquet files to store data, and a transaction log to keep track of commits, to provide capabilities like ACID transactions, data versioning, and audit history.

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.

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"
  }
]

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.

To create a Delta table, you can use existing Apache Spark SQL code and change the format from parquet, csv, or json to delta. Once you have a Delta table, you can write data into it using Apache Spark's Structured Streaming API. The Delta Lake transaction log guarantees exactly-once processing, even when there are other streams or batch queries running concurrently against the table. By default, streams run in append mode, which adds new records to the table. Databricks provides quickstart documentation that explains the whole process.

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.

Delta Lake on Databricks 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, PostgreSQL, or Snowflake, 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 Postgres, To Snowflake, To Panoply, and To S3.