How to load data from Pivotal Tracker to Databricks Lakehouse

Learn how to use Airbyte to synchronize your Pivotal Tracker data into Databricks Lakehouse within minutes.

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Start syncing with Airbyte in 3 easy steps within 10 minutes

Set up a Pivotal Tracker connector in Airbyte

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

Set up Databricks Lakehouse for your extracted Pivotal Tracker data

Select where you want to import data from your 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 Databricks Lakehouse 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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How to Sync to Manually

Step 1: Export Data from Pivotal Tracker

Begin by exporting the data you need from Pivotal Tracker. Pivotal Tracker provides a CSV export feature for workspaces, projects, or stories. Navigate to the desired project or workspace and use the export option to download the data as a CSV file. Ensure that you have the necessary permissions to access and export the data.

Open the exported CSV file to review its content. Look for any inconsistencies or unnecessary data that might need cleaning. Use a spreadsheet application like Microsoft Excel or Google Sheets to remove any irrelevant columns or rows and to correct any data formatting issues.

Depending on the structure of your Databricks Lakehouse, you may need to transform the data to ensure compatibility. This might involve renaming columns, adjusting data types, or restructuring the data to match your Lakehouse schema. Use scripting languages like Python or R for complex transformations if needed.

Set up your Databricks environment to receive the new data. This involves creating a new table or ensuring an existing table is structured correctly to accept the incoming data. Use the Databricks SQL interface or the Apache Spark interface to define the schema and storage format of your target table.

Upload the cleaned and transformed CSV file to the Databricks File System (DBFS). You can do this through the Databricks UI by navigating to the "Data" tab and selecting "Add Data." Upload the file and make a note of the path where the file is stored in DBFS.

Use a Databricks notebook to load the CSV data from DBFS into your Databricks table. Write a Spark job using PySpark or Scala to read the CSV file and write the data into the table. Ensure that the data types and schema match what you defined earlier in the Databricks environment.

Example PySpark code:
```python
df = spark.read.csv("/dbfs/path/to/your/csvfile.csv", header=True, inferSchema=True)
df.write.format("delta").mode("overwrite").saveAsTable("your_table_name")
```

After loading the data, run queries to verify that the data has been correctly imported into the Databricks Lakehouse. Check for completeness, consistency, and accuracy of the imported data. Perform any additional data validation checks necessary to ensure the integrity of your Lakehouse data.

By following these steps, you can manually extract, transform, and load data from Pivotal Tracker into a Databricks Lakehouse without relying on third-party connectors or integrations.