Pivotal Tracker
to
PostgreSQL

Connect Pivotal Tracker to PostgreSQL with No Code

With Portable, you can sync Pivotal Tracker data into your PostgreSQL warehouse in minutes. Access all of your Agile Project Management data from PostgreSQL without having to manage cumbersome ETL scripts.

The Two Paths To Connect Pivotal Tracker To Your Data Warehouse

There are two ways to sync data from Pivotal Tracker 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 Pivotal Tracker and your warehouse.

Method 2: Automating The ETL Process With A No-Code Solution

Leverage a prebuilt connector from a cloud-hosted solution like Portable.

Two Paths To Connect Pivotal Tracker To Your Data Warehouse
Two Paths To Connect Pivotal Tracker To Your Data Warehouse

How To Create Value With Pivotal Tracker Data

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

Extract: What Data Can You Extract From The Pivotal Tracker API?

Pivotal Tracker is an agile project management solution used for creating a shared view of team priorities.

To help clients power downstream analytics, Pivotal Tracker 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.

  • Accounts
  • Projects
  • Epics
  • Projects
  • Comments
  • Releases
  • Stories
  • Etc.

You can visit the Pivotal Tracker 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 projects endpoint in the Pivotal Tracker API documentation.

Pivotal Tracker projects API
Pivotal Tracker projects 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 Trello, Jira, and Monday that can be useful for comparison purposes.

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

Load: Which Destinations Are Best For A Pivotal Tracker ETL Pipeline?

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

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

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

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 Pivotal Tracker 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.

Here are three project analytics dashboards you should consider as a starting point.

  1. Open Tasks (By Assignee) - If your team is analyzing tasks, it's easy to build a quick summary of open tasks for each assignee to help to keep everyone in the loop.
  2. Overdue Tasks (By Project) - Every once in a while, tasks are delayed. Setting up alerts or notifications when tasks are overdue within a project can help your team stay on track.
  3. Average Time To Complete Tasks (By Month Created) - For operational teams and support teams, the completion time for tasks can be a great metric to help streamline processes. By showing trend data of the average time to complete tasks, you can better understand how your operational initiatives are working and consistently strive to support clients in real-time.

Beyond the dashboards above, replicating Pivotal Tracker 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 Pivotal Tracker integration with the benefits of using a no-code ETL solution like Portable.

Method 1: Building A Custom Pivotal Tracker ETL Pipeline

To build your own Pivotal Tracker integration, there are three steps:

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

Pivotal Tracker uses per-user API tokens for authentication. Once an API token is generated, it is attached to every request as the X-TrackerToken parameter.

Pivotal Tracker Authentication Overview
Pivotal Tracker Authentication Overview

Resources

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

For Pivotal Tracker, the projects 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 people, the endpoint uses limit and offset parameters for pagination.

Pivotal Tracker Pagination Overview
Pivotal Tracker Pagination Overview

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

For instance, to get all reviews, you need a project ID and story ID which are returned from other endpoints.

Pivotal Tracker API Request Parameters
Pivotal Tracker API Request Parameters

How Do You Call The Pivotal Tracker API? (Tutorial)

  1. Follow the instructions above to read the Pivotal Tracker 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 (Either 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 -X GET \
-H ""X-TrackerToken: TOKEN"" \
""https://www.pivotaltracker.com/services/v5/projects""

How Do You Maintain A Custom Pivotal Tracker ETL Pipeline?

Making a call to the Pivotal Tracker 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 Pivotal Tracker:

  • 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 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 Pivotal Tracker documentation doesn't reference an explicit threshold for requests over a time frame; however, the documentation does mention the idea of a rate limit. It's most likely that rate limits are communicated via HTTP responses.

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 A Pivotal Tracker 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 Pivotal Tracker 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 Pivotal Tracker ETL Solution

No-code ETL solutions are simple. Vendors are specialized 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 A Pivotal Tracker ETL Pipeline

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

  1. Create an account (no credit card required)
  2. Add a source - Search for and select Pivotal Tracker
  3. Authenticate with Pivotal Tracker using the instructions in the Portable console
  4. Select your warehouse (Snowflake, BigQuery, Redshift, or PostgreSQL) and authenticate
  5. Set up a flow connecting Pivotal Tracker to your analytics environment
  6. Run your flow to replicate data from Pivotal Tracker 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 Pivotal Tracker ETL?

No-Code Simplicity

Start moving Pivotal Tracker 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 Pivotal Tracker 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. Pivotal Tracker 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 Pivotal Tracker data 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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