How to load data from PartnerStack to Databricks Lakehouse

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

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Bespoke pipelines are:
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Furthermore, you will need to build and maintain Y x Z pipelines with Y sources and Z destinations to cover all your needs.
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Start syncing with Airbyte in 3 easy steps within 10 minutes

Set up a PartnerStack 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 PartnerStack 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 PartnerStack 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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Check out our interactive demo and our how-to videos to learn how you can sync data from any source to any destination.

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Quickly get up and running with a 5-minute setup that enables both incremental and full refreshes for databases of any size, seamlessly scaling to handle large data volumes. Our optimized architecture overcomes performance bottlenecks, ensuring efficient data synchronization even as your datasets grow from gigabytes to petabytes.

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Airbyte secures your data with cloud-hosted, self-hosted or hybrid deployment options. Single Sign-On (SSO) and Role-Based Access Control (RBAC) ensure only authorized users have access with the right permissions. Airbyte acts as a HIPAA conduit and supports compliance with CCPA, GDPR, and SOC2.

Fully Featured & Integrated

Airbyte automates schema evolution for seamless data flow, and utilizes efficient Change Data Capture (CDC) for real-time updates. Select only the columns you need, and leverage our dbt integration for powerful data transformations.

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Airbyte Self-Managed Enterprise comes with dedicated support and guaranteed service level agreements (SLAs), ensuring that your data movement infrastructure remains reliable and performant, and expert assistance is available when needed.

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Tech Lead at Symend

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"With Airbyte, we could just push a few buttons, allow API access, and bring all the data into Google BigQuery. By blending all the different marketing data sources, we can gain valuable insights."

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How to Sync to Manually

Step 1: Export Data from PartnerStack

Begin by logging into your PartnerStack account. Navigate to the reporting or data section where you can export the required data. Typically, PartnerStack allows you to export data in CSV or Excel format. Choose the relevant datasets and download them to your local machine.

On your local machine, ensure you have the necessary tools to handle data files, such as a text editor or a spreadsheet application (e.g., Microsoft Excel or Google Sheets). Verify that your system has access to Python or any other scripting language you plan to use for data transformation.

Open the exported data files in your preferred tool. Review the data structure and perform any necessary data cleaning or transformation. This may include removing unwanted columns, handling missing values, or converting data types. If using Python, libraries like Pandas can be very helpful for this task.

Log into your Databricks account and access your Databricks workspace. Ensure that you have the necessary permissions to create a new cluster and perform data imports. If you haven’t done so already, set up a new cluster with the appropriate configuration for your data processing needs.

Use the Databricks interface to upload the transformed data files to the Databricks File System (DBFS). In the Databricks workspace, go to the ‘Data’ tab, then click on ‘Upload Data’. Follow the prompts to upload your local CSV or Excel files to DBFS.

With the data files now in DBFS, use a Databricks notebook to read the files into a Spark DataFrame. You can use PySpark or Scala for this task. Write a script to load the DataFrame into a Databricks table. This typically involves using commands such as `spark.read.csv()` to read the files and `DataFrame.write.saveAsTable()` to create the table in Databricks.

After loading the data into Databricks, run validation checks to ensure the data's integrity and accuracy. Compare the Databricks tables against your original files to ensure that all data has been correctly imported. Use SQL queries to perform spot checks or summary statistics to validate the data.

By following these steps, you'll be able to manually move data from PartnerStack to Databricks Lakehouse without relying on third-party connectors or integrations.