TL;DR

This can be done by building a data pipeline manually, usually a Python script (you can leverage a tool as Apache Airflow for this). This process can take more than a full week of development. Or it can be done in minutes on Airbyte in three easy steps:

1. set up Pivotal Tracker as a source connector (using Auth, or usually an API key)
2. set up BigQuery as a destination connector
3. define which data you want to transfer and how frequently

You can choose to self-host the pipeline using Airbyte Open Source or have it managed for you with Airbyte Cloud.

This tutorial’s purpose is to show you how.

What is Pivotal Tracker

What is BigQuery

Prerequisites

1. A Pivotal Tracker account to transfer your customer data automatically from.
2. A BigQuery account.
3. An active Airbyte Cloud account, or you can also choose to use Airbyte Open Source locally. You can follow the instructions to set up Airbyte on your system using docker-compose.

Airbyte is an open-source data integration platform that consolidates and streamlines the process of extracting and loading data from multiple data sources to data warehouses. It offers pre-built connectors, including Pivotal Tracker and BigQuery, for seamless data migration.

When using Airbyte to move data from Pivotal Tracker to BigQuery, it extracts data from Pivotal Tracker using the source connector, converts it into a format BigQuery can ingest using the provided schema, and then loads it into BigQuery via the destination connector. This allows businesses to leverage their Pivotal Tracker data for advanced analytics and insights within BigQuery, simplifying the ETL process and saving significant time and resources.

Step 1: Set up Pivotal Tracker as a source connector

Step 2: Set up BigQuery as a destination connector

Step 3: Set up a connection to sync your Pivotal Tracker data to BigQuery

Once you've successfully connected Pivotal Tracker as a data source and BigQuery as a destination in Airbyte, you can set up a data pipeline between them with the following steps:

1. Create a new connection: On the Airbyte dashboard, navigate to the 'Connections' tab and click the '+ New Connection' button.
2. Choose your source: Select Pivotal Tracker from the dropdown list of your configured sources.
3. Select your destination: Choose BigQuery from the dropdown list of your configured destinations.
4. Configure your sync: Define the frequency of your data syncs based on your business needs. Airbyte allows both manual and automatic scheduling for your data refreshes.
5. Select the data to sync: Choose the specific Pivotal Tracker objects you want to import data from towards BigQuery. You can sync all data or select specific tables and fields.
6. Select the sync mode for your streams: Choose between full refreshes or incremental syncs (with deduplication if you want), and this for all streams or at the stream level. Incremental is only available for streams that have a primary cursor.
7. Test your connection: Click the 'Test Connection' button to make sure that your setup works. If the connection test is successful, save your configuration.
8. Start the sync: If the test passes, click 'Set Up Connection'. Airbyte will start moving data from Pivotal Tracker to BigQuery according to your settings.

Remember, Airbyte keeps your data in sync at the frequency you determine, ensuring your BigQuery data warehouse is always up-to-date with your Pivotal Tracker data.

Use Cases to transfer your Pivotal Tracker data to BigQuery

Integrating data from Pivotal Tracker to BigQuery provides several benefits. Here are a few use cases:

1. Advanced Analytics: BigQuery’s powerful data processing capabilities enable you to perform complex queries and data analysis on your Pivotal Tracker data, extracting insights that wouldn't be possible within Pivotal Tracker alone.
2. Data Consolidation: If you're using multiple other sources along with Pivotal Tracker, syncing to BigQuery allows you to centralize your data for a holistic view of your operations, and to set up a change data capture process so you never have any discrepancies in your data again.
3. Historical Data Analysis: Pivotal Tracker has limits on historical data. Syncing data to BigQuery allows for long-term data retention and analysis of historical trends over time.
4. Data Security and Compliance: BigQuery provides robust data security features. Syncing Pivotal Tracker data to BigQuery ensures your data is secured and allows for advanced data governance and compliance management.
5. Scalability: BigQuery can handle large volumes of data without affecting performance, providing an ideal solution for growing businesses with expanding Pivotal Tracker data.
6. Data Science and Machine Learning: By having Pivotal Tracker data in BigQuery, you can apply machine learning models to your data for predictive analytics, customer segmentation, and more.
7. Reporting and Visualization: While Pivotal Tracker provides reporting tools, data visualization tools like Tableau, PowerBI, Looker (Google Data Studio) can connect to BigQuery, providing more advanced business intelligence options. If you have a Pivotal Tracker table that needs to be converted to a BigQuery table, Airbyte can do that automatically.

Wrapping Up

To summarize, this tutorial has shown you how to:

1. Configure a Pivotal Tracker account as an Airbyte data source connector.
2. Configure BigQuery as a data destination connector.
3. Create an Airbyte data pipeline that will automatically be moving data directly from Pivotal Tracker to BigQuery after you set a schedule