How to load data from Amplitude to BigQuery
Learn how to use Airbyte to synchronize your Amplitude data into BigQuery within minutes.
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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."
How to Sync to Manually
Step 1: Set up Google Cloud Project and BigQuery
1. Create a Google Cloud Project:
- Go to the Google Cloud Console (https://console.cloud.google.com/).
- Click on the project dropdown and then on "New Project".
- Enter a project name, select a billing account, and click "Create".
2. Enable BigQuery API:
- In the Cloud Console, go to the API Library.
- Search for "BigQuery API" and enable it for your project.
3. Set up a BigQuery Dataset:
- Go to the BigQuery console.
- Click on your project name, then click "Create Dataset".
- Enter a dataset ID and choose default data expiration as needed, then click "Create dataset".
Step 2: Obtain Amplitude API Key and Secret
1. Log in to your Amplitude dashboard.
2. Go to Settings > Projects, and select the project you want to export data from.
3. Under the General tab, you'll find your API Key and Secret Key. Take note of these as you'll need them for API requests.
Step 3: Step 3: Extract Data from Amplitude
1. Identify the data you want to export:
Determine the events or properties you need from Amplitude.
2. Use Amplitude's Export API:
- Write a script (e.g., in Python) that uses the `requests` library to make API calls to Amplitude's Export API.
- The API endpoint is typically `https://amplitude.com/api/2/export`.
- You'll need to pass the API Key and Secret Key for authentication.
- Specify the start and end times for the data export.
- The response will be a zipped file containing the data in JSON format.
Step 4: Format the Data
1. Unzip the exported data:
Use a tool or script to unzip the downloaded file.
2. Transform the JSON data:
Write a script to parse the JSON data and transform it into a structured format suitable for BigQuery (e.g., CSV, Avro, or Parquet).
3. Ensure data types match:
Make sure that the data types in your transformed data match the schema you intend to use in BigQuery.
Step 5: Upload Data to BigQuery
1. Create a BigQuery table:
- Define the schema that corresponds to the data you've formatted.
- Use the BigQuery web UI, the `bq` command-line tool, or the BigQuery API to create a new table with this schema.
2. Upload the data:
- You can upload the data files to Google Cloud Storage and then use the BigQuery Data Transfer Service or `bq` command-line tool to load the data into BigQuery.
- Alternatively, you can use the BigQuery API to stream the data directly into BigQuery.
Step 6: Verify Data Integrity
1. Check the data:
After the data is uploaded, run some queries to ensure that the data has been loaded correctly and completely.
2. Validate data types and counts:
Confirm that the data types are correct and the counts of rows match what you exported from Amplitude.
Step 7: Automate the Process
1. Create a script or application:
Combine all the steps into a script or an application that automates the extraction, transformation, and loading (ETL) process.
2. Schedule the ETL process:
Use a scheduler like `cron` on a Unix-like system or the Google Cloud Scheduler to run your ETL process at regular intervals.
Step 8: Monitor and Maintain
1. Set up logging and monitoring:
Implement logging in your ETL script and monitor the ETL process to quickly identify and fix any issues.
2. Regularly check for API changes:
Keep an eye on any updates to the Amplitude API or BigQuery API that might require changes to your ETL script.