Beekeeper
to
BigQuery

Beekeeper Data Integration with BigQuery

With Portable, integrate Beekeeper data with your BigQuery warehouse in minutes. Access your all-in-one frontline success platform data from BigQuery without having to manage cumbersome ETL scripts.

The Two Paths to Connect Beekeeper to Google BigQuery

There are two ways to sync data from Beekeeper 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 Beekeeper 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.

How to Create Value with Beekeeper Data

Teams connect Beekeeper to their data warehouse to build dashboards and generate value for their business. Let’s dig into the capabilities Beekeeper 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 Beekeeper data.

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

Beekeeper is a all-in-one frontline success platform used for managing shift schedule, paystubs, onboarding, training, tasks and safety checklist for employees that are on the field instead of the office.

To help clients power downstream analytics, Beekeeper 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:

  • Artifacts
  • Comments
  • Configuration
  • Conversations
  • Custom Fields
  • Files
  • Forms
  • Groups
  • Login
  • Logout
  • Navigation Extensions
  • On/Off resource
  • Organisational Units
  • Payslips
  • Posts
  • Profiles
  • Get suggestions for mentions
  • Get profile of the given user
  • Get a list of profiles
  • Status
  • Streams
  • Sync
  • Tokens
  • Users
  • Webhooks
  • Schemas

You can visit the Beekeeper API Documentation to explore the entire catalog of available API resources and the complete schema definition for each.

As you think about the data you will need for analytics, don’t forget that Portable offers no-code integrations to other similar applications.

Regardless of the SaaS solution you use, it’s important to find a all-in-one frontline success platform with robust data available for analytics.

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

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

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

Once you have a destination to load the data, it’s common to combine Beekeeper 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 Beekeeper 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 Beekeeper 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 Beekeeper integration with the benefits of using a no-code ETL solution like Portable.

Method 1: Building a Custom Beekeeper ETL Pipeline

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

  1. Navigate the Beekeeper 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 Beekeeper’s API Documentation

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

Authentication

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 Beekeeper, it’s important to identify the authentication mechanism and how best to incorporate the necessary credentials into your API requests.

Beekeeper allows an access token to be generated by calling the /login endpoint with Basic Authentication of username and password.

Resources

It’s important to identify the Beekeeper 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 Beekeeper, the Get profiles 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.

Beekeeper uses limit and offset parameters for pagination.

How Do You Call the Beekeeper API? (Tutorial)

  1. Follow the instructions above to read the Beekeeper 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 https://stoplight.io/mocks/beekeeper/beekeeper-api/606193/profiles       
--header 'Authorization: {Bearer_token}'       
--header 'Content-Type: application/json'

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

Making a call to the Beekeeper 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 Beekeeper:

  • 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 Beekeeper 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.

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 Beekeeper 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 Beekeeper 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 Beekeeper 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 Beekeeper ETL Pipeline

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

  1. Create an account (no credit card required)
  2. Add a source —search for and select Beekeeper
  3. Authenticate with Beekeeper using the instructions in the Portable console
  4. Select BigQuery and authenticate
  5. Set up a flow connecting Beekeeper to your analytics environment
  6. Run your flow to replicate data from Beekeeper 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 Beekeeper ETL?

No-Code Simplicity

Start moving Beekeeper 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 Beekeeper 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. Beekeeper 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 Beekeeper 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.

Stop waiting for your data.Start using Portable today.

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