How to load data from Pivotal Tracker to AWS Datalake

Learn how to use Airbyte to synchronize your Pivotal Tracker data into AWS Datalake within minutes.

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Set up a Pivotal Tracker connector in Airbyte

Connect to Pivotal Tracker or one of 400+ pre-built or 10,000+ custom connectors through simple account authentication.

Set up AWS Datalake for your extracted Pivotal Tracker data

Select AWS Datalake where you want to import data from your Pivotal Tracker source to. You can also choose other cloud data warehouses, databases, data lakes, vector databases, or any other supported Airbyte destinations.

Configure the Pivotal Tracker to AWS Datalake in Airbyte

This includes selecting the data you want to extract - streams and columns -, the sync frequency, where in the destination you want that data to be loaded.

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TL;DR

set up Pivotal Tracker as a source connector (using Auth, or usually an API key)
set up AWS Datalake as a destination connector
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

Pivotal Tracker is a project management tool that helps teams collaborate and manage their work efficiently. It provides a simple and intuitive interface for creating and prioritizing tasks, tracking progress, and communicating with team members. With Pivotal Tracker, teams can easily plan and execute their projects, breaking them down into manageable chunks and assigning tasks to team members. The tool also provides real-time visibility into project status, allowing teams to quickly identify and address any issues that arise. Pivotal Tracker is designed to help teams work more effectively, delivering high-quality results on time and within budget.

What is AWS Datalake

An AWS Data Lake is a centralized repository that allows you to store all your structured and unstructured data at any scale. It is designed to handle massive amounts of data from various sources, such as databases, applications, IoT devices, and more. With AWS Data Lake, you can easily ingest, store, catalog, process, and analyze data using a wide range of AWS services like Amazon S3, Amazon Athena, AWS Glue, and Amazon EMR. This allows you to build data lakes for machine learning, big data analytics, and data warehousing workloads. AWS Data Lake provides a secure, scalable, and cost-effective solution for managing your organization's data.

Integrate Pivotal Tracker with AWS Datalake in minutes

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Prerequisites

A Pivotal Tracker account to transfer your customer data automatically from.
A AWS Datalake account.
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 AWS Datalake, for seamless data migration.

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

Step 1: Set up Pivotal Tracker as a source connector

1. Go to the Airbyte website and sign up for an account.
2. Once you have signed up, log in to your account and click on the ""Sources"" tab.
3. Scroll down until you find the ""Pivotal Tracker"" source connector and click on it.
4. Click on the ""Create new connection"" button.
5. Enter your Pivotal Tracker API token in the appropriate field.
6. Enter the name of your Pivotal Tracker project in the appropriate field.
7. Choose the data you want to sync from Pivotal Tracker to Airbyte.
8. Click on the ""Test"" button to make sure the connection is working properly.
9. If the test is successful, click on the ""Save & Sync"" button to start syncing your Pivotal Tracker data to Airbyte.
10. You can now use Airbyte to analyze and visualize your Pivotal Tracker data.

Step 2: Set up AWS Datalake as a destination connector

1. Log in to your AWS account and navigate to the AWS Management Console.
2. Click on the S3 service and create a new bucket where you will store your data.
3. Create an IAM user with the necessary permissions to access the S3 bucket. Make sure to save the access key and secret key.
4. Open Airbyte and navigate to the Destinations tab.
5. Select the AWS Datalake destination connector and click on "Create new connection".
6. Enter a name for your connection and paste the access key and secret key you saved earlier.
7. Enter the name of the S3 bucket you created in step 2 and select the region where it is located.
8. Choose the format in which you want your data to be stored in the S3 bucket (e.g. CSV, JSON, Parquet).
9. Configure any additional settings, such as compression or encryption, if necessary.
10. Test the connection to make sure it is working properly.
11. Save the connection and start syncing your data to the AWS Datalake.

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

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

Create a new connection: On the Airbyte dashboard, navigate to the 'Connections' tab and click the '+ New Connection' button.
Choose your source: Select Pivotal Tracker from the dropdown list of your configured sources.
Select your destination: Choose AWS Datalake from the dropdown list of your configured destinations.
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.
Select the data to sync: Choose the specific Pivotal Tracker objects you want to import data from towards AWS Datalake. You can sync all data or select specific tables and fields.
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.
Test your connection: Click the 'Test Connection' button to make sure that your setup works. If the connection test is successful, save your configuration.
Start the sync: If the test passes, click 'Set Up Connection'. Airbyte will start moving data from Pivotal Tracker to AWS Datalake according to your settings.

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

Use Cases to transfer your Pivotal Tracker data to AWS Datalake

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

Advanced Analytics: AWS Datalake’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.
Data Consolidation: If you're using multiple other sources along with Pivotal Tracker, syncing to AWS Datalake 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.
Historical Data Analysis: Pivotal Tracker has limits on historical data. Syncing data to AWS Datalake allows for long-term data retention and analysis of historical trends over time.
Data Security and Compliance: AWS Datalake provides robust data security features. Syncing Pivotal Tracker data to AWS Datalake ensures your data is secured and allows for advanced data governance and compliance management.
Scalability: AWS Datalake can handle large volumes of data without affecting performance, providing an ideal solution for growing businesses with expanding Pivotal Tracker data.
Data Science and Machine Learning: By having Pivotal Tracker data in AWS Datalake, you can apply machine learning models to your data for predictive analytics, customer segmentation, and more.
Reporting and Visualization: While Pivotal Tracker provides reporting tools, data visualization tools like Tableau, PowerBI, Looker (Google Data Studio) can connect to AWS Datalake, providing more advanced business intelligence options. If you have a Pivotal Tracker table that needs to be converted to a AWS Datalake table, Airbyte can do that automatically.

Wrapping Up

To summarize, this tutorial has shown you how to:

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

With Airbyte, creating data pipelines take minutes, and the data integration possibilities are endless. Airbyte supports the largest catalog of API tools, databases, and files, among other sources. Airbyte's connectors are open-source, so you can add any custom objects to the connector, or even build a new connector from scratch without any local dev environment or any data engineer within 10 minutes with the no-code connector builder.

We look forward to seeing you make use of it! We invite you to join the conversation on our community Slack Channel, or sign up for our newsletter. You should also check out other Airbyte tutorials, and Airbyte’s content hub!

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How to Sync Pivotal Tracker to AWS Datalake Manually

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Step 3:

Step 4:

Step 5:

Step 6:

Step 7:

Step 8:

FAQs

What is ETL?

ETL, an acronym for Extract, Transform, Load, is a vital data integration process. It involves extracting data from diverse sources, transforming it into a usable format, and loading it into a database, data warehouse or data lake. This process enables meaningful data analysis, enhancing business intelligence.

What is Pivotal Tracker?

What data can you extract from Pivotal Tracker?

Pivotal Tracker's API provides access to a wide range of data related to software development projects. The following are the categories of data that can be accessed through the API:

1. Projects: Information about the projects, including their names, descriptions, and IDs.

2. Stories: Details about the individual stories within a project, including their titles, descriptions, and statuses.

3. Epics: Information about the epics within a project, including their titles, descriptions, and statuses.

4. Tasks: Details about the tasks associated with a story, including their titles, descriptions, and statuses.

5. Comments: Information about the comments made on stories, epics, and tasks.

6. Memberships: Details about the members of a project, including their names, email addresses, and roles.

7. Labels: Information about the labels used to categorize stories within a project.

8. Iterations: Details about the iterations within a project, including their start and end dates.

9. Activity: Information about the activity within a project, including changes made to stories, epics, and tasks.

Overall, Pivotal Tracker's API provides a comprehensive set of data that can be used to track and manage software development projects.

How do I transfer data from Pivotal Tracker?

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 to AWS Datalake as a source connector (using Auth, or usually an API key)
2. Choose a destination (more than 50 available destination databases, data warehouses or lakes) to sync data too and set it up as a destination connector
3. Define which data you want to transfer from Pivotal Tracker to AWS Datalake and how frequently
You can choose to self-host the pipeline using Airbyte Open Source or have it managed for you with Airbyte Cloud.

What is ELT?

ELT, standing for Extract, Load, Transform, is a modern take on the traditional ETL data integration process. In ELT, data is first extracted from various sources, loaded directly into a data warehouse, and then transformed. This approach enhances data processing speed, analytical flexibility and autonomy.

Difference between ETL and ELT?

ETL and ELT are critical data integration strategies with key differences. ETL (Extract, Transform, Load) transforms data before loading, ideal for structured data. In contrast, ELT (Extract, Load, Transform) loads data before transformation, perfect for processing large, diverse data sets in modern data warehouses. ELT is becoming the new standard as it offers a lot more flexibility and autonomy to data analysts.

Back to all tutorials

Warehouses and Lakes

Engineering Analytics

How to load data from Pivotal Tracker to AWS Datalake

Learn how to use Airbyte to synchronize your Pivotal Tracker data into AWS Datalake within minutes.

Try Airbyte Cloud free

Deploy Airbyte Open Source

TL;DR

What is Pivotal Tracker

What is AWS Datalake

Prerequisites

Step 1: Setup Pivotal Tracker

Step 2: Setup AWS Datalake

Step 3: Setup a connection Use Cases Wrapping Up

Example H2

Example H3

Sync data between Pivotal Tracker and AWS Datalake

Get your data syncing in minutes

Try Airbyte free

TL;DR

set up Pivotal Tracker as a source connector (using Auth, or usually an API key)
set up AWS Datalake as a destination connector
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 AWS Datalake

Integrate Pivotal Tracker with AWS Datalake in minutes

Try for free now

Prerequisites

A Pivotal Tracker account to transfer your customer data automatically from.
A AWS Datalake account.
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.

Step 1: Set up Pivotal Tracker as a source connector

Step 2: Set up AWS Datalake as a destination connector

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

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

Create a new connection: On the Airbyte dashboard, navigate to the 'Connections' tab and click the '+ New Connection' button.
Choose your source: Select Pivotal Tracker from the dropdown list of your configured sources.
Select your destination: Choose AWS Datalake from the dropdown list of your configured destinations.
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.
Select the data to sync: Choose the specific Pivotal Tracker objects you want to import data from towards AWS Datalake. You can sync all data or select specific tables and fields.
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.
Test your connection: Click the 'Test Connection' button to make sure that your setup works. If the connection test is successful, save your configuration.
Start the sync: If the test passes, click 'Set Up Connection'. Airbyte will start moving data from Pivotal Tracker to AWS Datalake according to your settings.

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

Use Cases to transfer your Pivotal Tracker data to AWS Datalake

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

Advanced Analytics: AWS Datalake’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.
Data Consolidation: If you're using multiple other sources along with Pivotal Tracker, syncing to AWS Datalake 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.
Historical Data Analysis: Pivotal Tracker has limits on historical data. Syncing data to AWS Datalake allows for long-term data retention and analysis of historical trends over time.
Data Security and Compliance: AWS Datalake provides robust data security features. Syncing Pivotal Tracker data to AWS Datalake ensures your data is secured and allows for advanced data governance and compliance management.
Scalability: AWS Datalake can handle large volumes of data without affecting performance, providing an ideal solution for growing businesses with expanding Pivotal Tracker data.
Data Science and Machine Learning: By having Pivotal Tracker data in AWS Datalake, you can apply machine learning models to your data for predictive analytics, customer segmentation, and more.
Reporting and Visualization: While Pivotal Tracker provides reporting tools, data visualization tools like Tableau, PowerBI, Looker (Google Data Studio) can connect to AWS Datalake, providing more advanced business intelligence options. If you have a Pivotal Tracker table that needs to be converted to a AWS Datalake table, Airbyte can do that automatically.