AfterShip to BigQuery

This page provides you with instructions on how to extract data from AfterShip and load it into Google BigQuery. (If this manual process sounds onerous, check out Stitch, which can do all the heavy lifting for you in just a few clicks.)

What is Aftership?

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.

What is Google BigQuery?

Google BigQuery is a data warehouse that delivers super-fast results from SQL queries, which it accomplishes using a powerful engine dubbed Dremel. With BigQuery, there's no spinning up (and down) clusters of machines as you work with your data. With that said, it's clear why some claim that BigQuery prioritizes querying over administration. It's super fast, and that's the reason why most folks use it.

Getting data out of AfterShip

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.

Sample AfterShip data

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
                    }
                ]
            }
        ]
    }
}

Loading data into Google BigQuery

Google Cloud Platform provides an introduction to loading data into BigQuery. Use the bq tool, and in particular the bq load command, to upload data. Its syntax is documented in the Quickstart guide for bq. You can supply the table or partition schema, or, for supported data formats, you can use schema auto-detection. Iterate through this process as many times as it takes to load all of your tables and table data into BigQuery.

Keeping AfterShip data up to date

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.

Other data warehouse options

BigQuery 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, PostgreSQL, or Snowflake, which are RDBMSes that use similar SQL syntax, or Panoply, which works with Redshift instances. If you're interested in seeing the relevant steps for loading data into one of these platforms, check out To Redshift, To Postgres, To Snowflake, and To Panoply.

Easier and faster alternatives

If all this sounds a bit overwhelming, don’t be alarmed. If you have all the skills necessary to go through this process, chances are building and maintaining a script like this isn’t a very high-leverage use of your time.

Thankfully, products like Stitch were built to solve this problem automatically. With just a few clicks, Stitch starts extracting your AfterShip data via the API, structuring it in a way that is optimized for analysis, and inserting that data into your Google BigQuery data warehouse.