AfterShip is a tracking service platform that helps businesses track shipments. AfterShip supports more than 400 carriers, and offers a free tier to businesses that make no more than 100 shipments per month.

Panoply can spin up a new Amazon Redshift instance in just a few clicks. Panoply's managed data warehouse service uses machine learning and natural language processing (NLP) to learn, model, and automate data management activities from source to analysis. It can import data with no schema, no modeling, and no configuration, and lets you use analysis, SQL, and visualization tools just as you would if you were creating a Redshift data warehouse on your own.

AfterShip provides a REST API that lets you extract information from its system. If, for example, you wanted to retrieve a list of trackings, you could call GET /trackings.

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

{
    "meta": {
        "code": 200
    },
    "data": {
        "page": 1,
        "limit": 100,
        "count": 3,
        "keyword": "",
        "slug": "",
        "origin": [],
        "destination": [],
        "tag": "",
        "fields": "",
        "created_at_min": "2017-03-27T07:36:14+00:00",
        "created_at_max": "2017-06-25T07:36:14+00:00",
        "trackings": [
            {
                "id": "53aa7b5c415a670000000021",
                "created_at": "2017-06-25T07:33:48+00:00",
                "updated_at": "2017-06-25T07:33:55+00:00",
                "tracking_number": "123456789",
                "tracking_account_number": null,
                "tracking_postal_code": null,
                "tracking_ship_date": null,
                "slug": "dhl",
                "active": false,
                "custom_fields": {
                    "product_price": "USD19.99",
                    "product_name": "iPhone Case"
                },
                "customer_name": null,
                "destination_country_iso3": null,
                "emails": [
                    "email@yourdomain.com",
                    "another_email@yourdomain.com"
                ],
                "expected_delivery": null,
                "note": null,
                "order_id": "ID 1234",
                "order_id_path": "http://www.aftership.com/order_id=1234",
                "origin_country_iso3": null,
                "shipment_package_count": 0,
                "shipment_type": null,
                "signed_by": "raul",
                "smses": [],
                "source": "api",
                "tag": "Delivered",
                "title": "Title Name",
                "tracked_count": 1,
                "unique_token": "xy_fej9Llg",
                "checkpoints": [
                    {
                        "slug": "dhl",
                        "city": null,
                        "created_at": "2017-06-25T07:33:53+00:00",
                        "country_name": "VALENCIA - SPAIN",
                        "message": "Awaiting collection by recipient as requested",
                        "country_iso3": null,
                        "tag": "InTransit",
                        "checkpoint_time": "2017-05-12T12:02:00",
                        "coordinates": [],
                        "state": null,
                        "zip": null
                    }
                ]
            }
        ]
    }
}

When you've identified all of the columns you want to insert, use the Reshift CREATE TABLE statement to create a table in your data warehouse to receive all the data.

Once you have a table built, it may seem like the easiest way to replicate your data (especially if there isn't much of it) is to build INSERT statements to add data to your Redshift table row by row. If you have any experience with SQL, this probably will be your first inclination. Think again! Redshift isn't optimized for inserting data one row at a time. If you have a high volume of data to be inserted, you should load the data into Amazon S3 and then use the COPY command to load it into Redshift.

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 AfterShip.

And remember, as with any code, once you write it, you have to maintain it. If AfterShip modifies its API, or 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.

Panoply 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, Snowflake, or Microsoft Azure Synapse Analytics, which are RDBMSes that use similar SQL syntax. Others choose a data lake, like Amazon S3 or Delta Lake on Databricks. 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 SQL Data Warehouse, To S3, and To Delta Lake.