ShipHawk Data Integration with BigQuery

With Portable, integrate ShipHawk data with your BigQuery warehouse in minutes. Access your eCommerce shipping solution data from BigQuery without having to manage cumbersome ETL scripts.

The Two Paths to Connect ShipHawk to Google BigQuery

There are two ways to sync data from ShipHawk into your data warehouse for analytics.

Method 1: Manually Developing a Custom Data Pipeline Yourself

Write code from scratch or use an open-source framework to build an integration between ShipHawk and BigQuery.

Method 2: Automating the ETL Process with a No-Code Solution

Leverage a pre-built connector from a cloud-hosted solution like Portable.

Two Paths To Connect ShipHawk To Google BigQuery
Two Paths To Connect ShipHawk To Google BigQuery

How to Create Value with ShipHawk Data

Teams connect ShipHawk to their data warehouse to build dashboards and generate value for their business. Let’s dig into the capabilities ShipHawk exposes via their API, outline insights you can build with the data, and summarize the most common analytics environments that teams are using to process their ShipHawk data.

Extract: What Data Can You Extract from the ShipHawk API?

ShipHawk is a eCommerce shipping solution used for selecting carriers, processing parcels and LTL shipments, and optimizing warehouse processes.

To help clients power downstream analytics, ShipHawk offers an application programming interface (API) for clients to extract data on business entities. Here are a few example entities you can extract from the API:

  • Orders
  • Shipments
  • Rates
  • Addresses
  • Batches
  • Etc.

You can visit the ShipHawk API Documentation to explore the entire catalog of available API resources and the complete schema definition for each. As an example, here are some of the details for the orders endpoint in the ShipHawk API documentation.

ShipHawk API
ShipHawk API

As you think about the data you will need for analytics, don’t forget that Portable offers no-code integrations to other similar applications like Shippo, ShipStation, and EasyPost that can be useful for comparison purposes.

Regardless of the SaaS solution you use, it’s important to find a eCommerce shipping solution with robust data available for analytics.

Load: Which Destinations Are Best for Your ShipHawk ETL Pipeline?

To turn raw data from ShipHawk into dashboards, most companies centralize information into a data warehouse or data lake. For Portable clients, the most common ETL pipelines are:

  1. ShipHawk to Snowflake Integration
  2. ShipHawk to Google BigQuery Integration
  3. ShipHawk to Amazon Redshift Integration
  4. ShipHawk to PostgreSQL Integration
Common Data Warehouses
Common Data Warehouses

Once you have a destination to load the data, it’s common to combine ShipHawk data with information from other enterprise applications like Jira, Mailchimp, HubSpot, Zendesk, and Klaviyo.

From there, you can build cross-functional dashboards in a visualization tool like Power BI, Tableau, Looker, or Retool.

Develop: Which Dashboards Should You Build with ShipHawk Data?

Now that you have identified the data you want to extract, the next step is to plan out the dashboards you can build with the data.

As a process, you want to consume raw data, overlay SQL logic, and build a dashboard to either 1) increase revenue or 2) decrease costs.

Replicating ShipHawk data into your cloud data warehouse can unlock a wide array of opportunities to power analytics, automate workflows, and develop products. The use cases are endless.

Now that we have a clear sense of the insights we can create, let’s compare the process of developing a custom ShipHawk integration with the benefits of using a no-code ETL solution like Portable.

Method 1: Building a Custom ShipHawk ETL Pipeline

To build your own ShipHawk integration, there are three steps:

  1. Navigate the ShipHawk API documentation
  2. Make your first API request
  3. Turn an API request into a complete data pipeline

Let’s walk through the process in more detail.

How to Interpret ShipHawk’s API Documentation

When reading API documentation, there are a handful of key concepts to consider.


There are many common authentication mechanisms. OAuth 2.0 (Auth Code and Client Credentials), API Keys, JWT Tokens, Personal Access Tokens, Basic Authentication, etc. For ShipHawk, it’s important to identify the authentication mechanism and how best to incorporate the necessary credentials into your API requests.

ShipHawk uses API keys for authentication. The API key can either be passed as a query parameter called api_key or in the x-api-key header parameter.

ShipHawk Authentication Overview
ShipHawk Authentication Overview


It’s important to identify the ShipHawk API endpoints you want to use for analytics. Most APIs offer a combination of GET, POST, PUT, and DELETE request methods; however, for analytics, GET requests are typically the most useful. At times, POST requests can be used to extract data as well.

For ShipHawk, the orders endpoint is a great place to get started.

Request Parameters

For each API endpoint you would like to use for analytics, you need to understand the method (GET, POST, PUT, or DELETE) and the URL, but there are other considerations to take into account as well. You should look out for pagination mechanics, query parameters, and parameters that are added to the request path.

Some API endpoints offer pagination. For instance, when listing orders, the API responses include page and per_page parameters that can be used for pagination.

ShipHawk Pagination Overview
ShipHawk Pagination Overview

Some API endpoints require unique identifiers from a previous API response to be included in the URL path.

For instance, to list all shipments for an order, you need an order ID that is returned from another endpoint.

ShipHawk API Request Parameters
ShipHawk API Request Parameters

How Do You Call the ShipHawk API? (Tutorial)

  1. Follow the instructions above to read the ShipHawk API documentation
  2. Identify and collect your credentials for authentication
  3. Pick the API resource you want to pull data from
  4. Configure the necessary parameters, method, and URL to make your first request (e.g. with curl or Postman)
  5. Add your credentials and make your first API call . Here is an example request using curl (without real credentials):
curl --request GET \
  --url \
  --header 'x-api-key: {api_key}' \
  --header 'Content-Type: application/json'

How Do You Maintain a Custom ShipHawk to BigQuery ETL Pipeline?

Making a call to the ShipHawk API is just the beginning of maintaining a complete custom ETL pipeline.

Here is a getting-started guide to building a production-grade pipeline for ShipHawk:

  • For each API endpoint, define schemas (which fields exist and the type for each)
  • Process the API response and parse the data (typically parsing JSON or XML)
  • Handle and replicate nested objects and custom fields
  • Identify which ShipHawk fields are primary keys and which keys are required vs. optional
  • Version control your changes in a git-based workflow (using GitHub, GitLab, etc.)
  • Handle code dependencies in your toolchain and the upgrades that come with each
  • Monitor the health of the upstream API, and —when things go wrong— troubleshoot via the status page, reach out to support, and open tickets
  • Handle error codes (HTTP error codes like 400s, 500s, etc.)
  • Manage and respect rate limits imposed by the server

We won’t go into detail on all of the items above, but rate limits are a great example of the complexity found in a production-grade data pipeline.

For rate limits, the ShipHawk API documentation doesn't reference an explicit rate limit; however, it's always important to respectively manage API calls to not overload a server.

If you don’t respect rate limits, and if you can’t handle server responses (like 429 errors with a Retry-After header), your pipeline can break, and analytics can become out-of-date.

What Are the Drawbacks of Building the ShipHawk ETL Pipeline Yourself?

You can probably tell at this point that there is a lot of work that goes into building and maintaining an ETL pipeline from ShipHawk to your data warehouse.

If you want less development work, faster insights, and no ongoing responsibilities, you should consider a cloud-hosted ETL solution.

Let’s walk through the setup process for a no-code ETL solution and its benefits.

Method 2: Using a No-Code ShipHawk ETL Solution

No-code ETL solutions are simple. Vendors specialize in building and maintaining data pipelines on your behalf. Instead of starting from scratch for each integration. Companies like Portable create connector templates that can be leveraged by hundreds or thousands of clients.

Step-By-Step Tutorial for Configuring Your ShipHawk ETL Pipeline

Off-the-shelf ETL tools offer a no-code setup process. Here are the instructions to connect ShipHawk to your cloud data warehouse with Portable.

  1. Create an account (no credit card required)
  2. Add a source —search for and select ShipHawk
  3. Authenticate with ShipHawk using the instructions in the Portable console
  4. Select BigQuery and authenticate
  5. Set up a flow connecting ShipHawk to your analytics environment
  6. Run your flow to replicate data from ShipHawk to your warehouse
  7. Use the dropdown to set your data flow to run on a cadence

What Are the Benefits of Using Portable for ShipHawk ETL?

No-Code Simplicity

Start moving ShipHawk data in minutes. Save yourself the headaches of reading API documentation, writing code, and worrying about maintenance. Leave the hassle to us.

Easy to Understand Pricing

With predictable, fixed-cost pricing per data flow, you know exactly how much your ShipHawk integration will cost every month.

Fast Development Speeds

Access lightning-fast connector development. Portable can build new integrations on-demand in hours or days.

Hands-On Support

APIs change. Schemas evolve. ShipHawk will have maintenance issues and errors. With Portable, we will do everything in our power to make your life easier.

Unlimited Data Volumes

You can move as much data from ShipHawk to Google BigQuery as you want without worrying about usage credits or overages. Instead of analyzing your ETL costs, you should be analyzing your data.

Free to Get Started

Sign up and get started for free. You don’t need a credit card to manually trigger a data sync, so you can try all of our connectors before paying a dime.

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