With Portable, integrate Wrike data with your BigQuery warehouse in minutes. Access your work management solution data from BigQuery without having to manage cumbersome ETL scripts.
The Two Paths to Connect Wrike to Google BigQuery
There are two ways to sync data from Wrike 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 Wrike 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 Wrike Data
Teams connect Wrike to their data warehouse to build dashboards and generate value for their business. Let’s dig into the capabilities Wrike 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 Wrike data.
Extract: What Data Can You Extract from the Wrike API?
Wrike is a work management solution used for cross-departmental collaboration and automation.
To help clients power downstream analytics, Wrike 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:
- Contacts
- Users
- Groups
- Workflows
- Tasks
- Dependencies
- Etc.
You can visit the Wrike 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 contacts endpoint in the Wrike API documentation.
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 Trello, Monday.com, and ClickUp that can be useful for comparison purposes.
Regardless of the SaaS solution you use, it’s important to find a work management solution with robust data available for analytics.
Load: Which Destinations Are Best for Your Wrike ETL Pipeline?
To turn raw data from Wrike into dashboards, most companies centralize information into a data warehouse or data lake. For Portable clients, the most common ETL pipelines are:
- Wrike to Snowflake Integration
- Wrike to Google BigQuery Integration
- Wrike to Amazon Redshift Integration
- Wrike to PostgreSQL Integration
Once you have a destination to load the data, it’s common to combine Wrike 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 Wrike 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 Wrike 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 Wrike integration with the benefits of using a no-code ETL solution like Portable.
Method 1: Building a Custom Wrike ETL Pipeline
To build your own Wrike integration, there are three steps:
- Navigate the Wrike API documentation
- Make your first API request
- Turn an API request into a complete data pipeline
Let’s walk through the process in more detail.
How to Interpret Wrike’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 Wrike, it’s important to identify the authentication mechanism and how best to incorporate the necessary credentials into your API requests.
Wrike uses OAuth 2.0 Auth Code for authentication. Developers need to create an application with a client ID and client secret, redirect users to an authentication URL, and generate a token. Once a bearer token is generated, it is added to the API header for every request.
Resources
It’s important to identify the Wrike 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 Wrike, the contacts 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, the tasks endpoint uses pageSize and pageToken parameters where pageToken is returned from the prior request.
Some endpoints require parameters to be included in the path or query parameters such as when you want to return comments in a folder.
How Do You Call the Wrike API? (Tutorial)
- Follow the instructions above to read the Wrike API documentation
- Identify and collect your credentials for authentication
- Pick the API resource you want to pull data from
- Configure the necessary parameters, method, and URL to make your first request (e.g. with curl or Postman)
- Add your credentials and make your first API call . Here is an example request using curl (without real credentials):
curl -g -X GET \
-H 'Authorization: bearer 123456' \
'https://www.wrike.com/api/v4/contacts'
How Do You Maintain a Custom Wrike to BigQuery ETL Pipeline?
Making a call to the Wrike 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 Wrike:
- 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 Wrike 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 Wrike API documentation doesn't reference an explicit rate limit; however, in the community forums, employees at Wrike have mentioned a rate limt of 100 requests per minute.
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 Wrike 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 Wrike 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 Wrike 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 Wrike ETL Pipeline
Off-the-shelf ETL tools offer a no-code setup process. Here are the instructions to connect Wrike to your cloud data warehouse with Portable.
- Create an account (no credit card required)
- Add a source —search for and select Wrike
- Authenticate with Wrike using the instructions in the Portable console
- Select BigQuery and authenticate
- Set up a flow connecting Wrike to your analytics environment
- Run your flow to replicate data from Wrike to your warehouse
- Use the dropdown to set your data flow to run on a cadence
What Are the Benefits of Using Portable for Wrike ETL?
No-Code Simplicity
Start moving Wrike 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 Wrike 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. Wrike 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 Wrike 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.