How to load data from Recurly to Databricks Lakehouse

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

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Start syncing with Airbyte in 3 easy steps within 10 minutes

Set up a Recurly 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 Recurly 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 Recurly 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: Understand Recurly's API and Data Structure

Begin by familiarizing yourself with Recurly's API documentation. Identify the endpoints that provide the data you need. Understand the data structure, formats (usually JSON), and authentication methods required to access the Recurly API.

Create a Recurly API key from the Recurly admin interface. Ensure that you have the necessary permissions to read the data. Store this key securely as you will need it to authenticate your requests to the Recurly API.

Write custom scripts (using Python, for example) to make HTTP requests to the Recurly API endpoints. Use libraries like `requests` in Python to handle API requests and responses. Extract the data you need and save it in a format that can be processed easily, such as CSV or JSON files.

Log into your Databricks account and create a new workspace if necessary. Configure your environment by setting up clusters with the required libraries (e.g., `pandas`, `numpy`, or any other necessary Python libraries) to process the data once it's uploaded.

Use Databricks' built-in tools to upload your extracted data files (CSV or JSON) to the Databricks File System (DBFS). This can be done through the Databricks user interface by navigating to the "Data" tab and selecting "Upload Data." Alternatively, use the Databricks CLI for more automated solutions.

Utilize Databricks notebooks to read and process the data files stored in DBFS. Use Spark or Pandas for data transformation tasks such as filtering, cleaning, and aggregating data. Write scripts that load the data into tables or temporary views for further analysis or integration into the Lakehouse architecture.

Finally, load the processed data into the Databricks Lakehouse. Create appropriate Delta tables to store your data efficiently. Use Delta Lake features for data versioning, schema enforcement, and performance optimizations. Ensure that your Lakehouse is structured to support future data loads seamlessly.

By following these steps, you can effectively move data from Recurly to Databricks Lakehouse without relying on third-party connectors, leveraging custom scripts and Databricks' built-in capabilities instead.