Converting JSON Data to Newline Delimited JSON for BigQuery Load Using Python

Learn how to easily convert JSON data to newline delimited format using Python for seamless loading into BigQuery.
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Converting JSON Data to Newline Delimited JSON for BigQuery Load Using Python

When working with cloud functions and BigQuery, you may need to convert standard JSON data into a format that BigQuery can work with effectively. One common requirement is to transform JSON into newline delimited JSON (NDJSON), where each record is placed on a new line. This format is particularly useful for loading data into BigQuery, especially when handling large datasets.

In this guide, we will walk through how to convert a typical JSON format into newline delimited JSON using Python. We will also cover a more concrete implementation that includes aggregating these individual records into a single string for further processing.

Understanding the Task

Input JSON Data

Imagine you have the following JSON structure:

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Expected Output

The expected output for loading into BigQuery should look like this:

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Each JSON object should be in its own line, ready for a seamless load into BigQuery.

Solution Steps

Step 1: Import Required Libraries

Before we start, ensure you have Python’s built-in json library imported to your script. This will help us handle JSON data fluently.

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Step 2: Prepare Your JSON Data

Assuming you have an input JSON object, you can access the data easily:

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Step 3: Convert JSON to Newline Delimited Format

To achieve the conversion, use the following code to iterate through the data records and format them correctly:

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Step 4: Final Output

At this stage, json_content will hold all your records in the NDJSON format. This can now be printed, logged, or passed to another function for further processing.

Conclusion

Converting JSON data into newline delimited JSON for BigQuery in Python is straightforward, and crucial for cloud-based workflows. By following the steps outlined above, you should be able to effectively prepare your data for loading into BigQuery, ensuring compatibility and efficiency.

With this knowledge, you can easily manipulate JSON data structures in Python and prepare them for various data processing tasks. Happy coding!