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.

Google Data Studio is a simple dashboard and reporting tool. It's free and easy to use, but it lacks the sophisticated features of higher-end reporting software. Many of the connectors it supports are for Google products, but third parties have written partner connectors to a wide variety of data sources. Its drag-and-drop report editor lets users create about 15 types of charts.

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
                    }
                ]
            }
        ]
    }
}

Google Data Studio uses what it calls "connectors" to gain access to data. Data Studio comes bundled with 17 connectors, mostly to pull in data from other Google products. It also supports connectors to MySQL and PostgreSQL databases, and offers 200 connectors to other data sources built and supported by partners.

Google Data Studio provides a graphical canvas onto which users drag and drop datasets. Users can set dimensions and metrics, specify sorting and filtering, and tailor the way reports and charts are displayed.

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.