Sendwithus offers a platform for marketers to manage and optimize transactional emails. It lets organizations segment messages for more personal interaction, and automate email distribution.

Amazon S3 (Simple Storage Service) provides cloud-based object storage through a web service interface. You can use S3 to store and retrieve any amount of data, at any time, from anywhere on the web. S3 objects, which may be structured in any way, are stored in resources called buckets.

Sendwithus lets users get data out of its systems both via webhooks and by using a REST API.

You can set up webhooks when you specify Delivery Settings in the Sendwithus dashboard.

The API lets you get information about logs, snippets, templates, and other elements. If, for example, you wanted a list of snippets, you could call GET /snippets. This call has several optional parameters that let you select or limit the responses.

The Sendwithus API returns data in JSON format. For example, the result of a call to list snippets might look like this:

[
    {
        "object": "snippet",
        "id": "snp_Q33jTLFc2ayG9KrF2Vcm4F",
        "name": "My First Snippet",
        "body": "<h1>Welcome!</h1>",
        "created": 5858858124,
        "modified": 5938868250
    }
]

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. The Sendwithus 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 upload files you must first create an S3 bucket. Once you have a bucket you can add an object to it. An object can be any kind of file: a text file, data file, photo, or anything else. You can optionally compress or encrypt the files before you load them.

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 Sendwithus.

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

S3 is great, but sometimes you want a more structured repository that can serve as a basis for BI reports and data analytics — in short, a data warehouse. Some folks choose to go with Amazon Redshift, Google BigQuery, PostgreSQL, Snowflake, Microsoft Azure Synapse Analytics, or Panoply, which are RDBMSes that use similar SQL syntax. 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 Azure Synapse Analytics, and To Panoply.