How to load data from TVMaze Schedule to Databricks Lakehouse

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

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Set up a TVMaze Schedule 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 TVMaze Schedule 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 TVMaze Schedule 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: Access the TVmaze API

Start by accessing the TVmaze schedule data via their API. TVmaze provides a RESTful API that you can use to fetch schedule information. Visit the API documentation at [TVmaze API](https://www.tvmaze.com/api) for details on endpoints and parameters. Typically, you would use the endpoint that provides the schedule data, specifying any necessary parameters like date or country.

Use Python's `requests` library to make HTTP GET requests to the TVmaze API. Write a Python script that sends requests to the API and retrieves the JSON response containing the schedule data. Ensure you handle potential errors, such as network issues or invalid responses, by implementing appropriate error handling in your script.

Once the data is fetched, parse the JSON response using Python's built-in `json` library. Extract the relevant fields from the JSON structure that you need for analysis or storage in Databricks. Perform any necessary transformations or cleaning on the data, such as filtering out unwanted fields, converting data types, or renaming fields for consistency.

Log in to your Databricks account and set up a new cluster if you haven't already. Configure the cluster with the necessary resources and libraries, such as adding Python if it's not installed by default. Ensure that your workspace is ready for data ingestion and processing.

Use the Databricks CLI or the web interface to upload the transformed JSON data from your local system to the Databricks File System (DBFS). You can save the data as a file in a directory of your choice. This step ensures that the data is accessible from within your Databricks notebooks.

In a Databricks notebook, use PySpark or Pandas to read the data from DBFS into a DataFrame. PySpark's `spark.read.json()` method is particularly useful for loading JSON data directly into a Spark DataFrame. Validate the DataFrame by checking the schema and a few sample records to ensure the data has been loaded correctly.

Finally, write the DataFrame to a table in the Databricks Lakehouse. Use the DataFrame's `write` method to save the data in a format suitable for your needs, such as Delta Lake, which supports ACID transactions and efficient data processing. Specify the storage location and partitioning strategy if required, to optimize performance and management.

By following these steps, you can efficiently transfer data from the TVmaze schedule to a Databricks Lakehouse without relying on third-party connectors or integrations.