How to load data from LaunchDarkly to BigQuery

Learn how to use Airbyte to synchronize your LaunchDarkly data into BigQuery within minutes.

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Start syncing with Airbyte in 3 easy steps within 10 minutes

Set up a LaunchDarkly connector in Airbyte

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

Set up BigQuery for your extracted LaunchDarkly 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 LaunchDarkly to BigQuery 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 LaunchDarkly API and Data Requirements

Begin by familiarizing yourself with the LaunchDarkly API documentation. Identify the endpoints that provide the data you need to transfer to BigQuery. Typically, you might need data from the flags, environments, or audit log endpoints. Ensure you have the necessary API access credentials.

Prepare your local or cloud environment to extract data from LaunchDarkly. Install necessary programming tools such as Python or Node.js, and set up libraries for making HTTP requests (e.g., `requests` in Python or `axios` in Node.js). This will allow you to programmatically interact with the LaunchDarkly API.

Write scripts to call LaunchDarkly's REST API, authenticate using the API key, and extract the required data. Ensure you handle pagination if the data is large. For example, in Python, use a loop to handle paginated responses and store data in a structured format, such as JSON.

Convert the extracted JSON data into a format suitable for BigQuery, such as CSV or newline-delimited JSON (NDJSON). This involves parsing the JSON response and writing the data fields into a structured tabular format. Use libraries like `pandas` in Python to facilitate this transformation and handle any necessary data cleaning.

Install and configure the Google Cloud SDK on your machine. Authenticate using your Google Cloud account to gain access to BigQuery. Run `gcloud auth login` to authenticate and set your project using `gcloud config set project YOUR_PROJECT_ID`.

Before loading data into BigQuery, upload your transformed data file to a Google Cloud Storage bucket. Use the `gsutil` command-line tool provided by the Google Cloud SDK: `gsutil cp your_data_file gs://your-bucket-name/`. Ensure the bucket is in the same region as your BigQuery dataset for optimal performance.

Use the BigQuery command-line tool or console to load data from Google Cloud Storage into BigQuery. This can be done using the `bq load` command: `bq load --source_format=NEWLINE_DELIMITED_JSON dataset.table gs://your-bucket-name/your_data_file`. Specify the correct data schema and ensure the table is set up to match the structure of your data.

This guide outlines the end-to-end process of manually moving data from LaunchDarkly to BigQuery without relying on third-party connectors. Each step involves using native tools and services provided by LaunchDarkly and Google Cloud Platform.