How to load data from Square to BigQuery

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

Try Airbyte Cloud Manual method (the hard way)

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Bespoke pipelines are:

Inconsistent and inaccurate data
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Furthermore, you will need to build and maintain Y x Z pipelines with Y sources and Z destinations to cover all your needs.

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Start syncing with Airbyte in 3 easy steps within 10 minutes

Set up a Square 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 Square 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 Square 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.

Try Airbyte Cloud Manual method (the hard way)

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Check out our interactive demo and our how-to videos to learn how you can sync data from any source to any destination.

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Try Airbyte Cloud Manual method (the hard way)

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Quickly get up and running with a 5-minute setup that enables both incremental and full refreshes for databases of any size, seamlessly scaling to handle large data volumes. Our optimized architecture overcomes performance bottlenecks, ensuring efficient data synchronization even as your datasets grow from gigabytes to petabytes.

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Airbyte secures your data with cloud-hosted, self-hosted or hybrid deployment options. Single Sign-On (SSO) and Role-Based Access Control (RBAC) ensure only authorized users have access with the right permissions. Airbyte acts as a HIPAA conduit and supports compliance with CCPA, GDPR, and SOC2.

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Airbyte automates schema evolution for seamless data flow, and utilizes efficient Change Data Capture (CDC) for real-time updates. Select only the columns you need, and leverage our dbt integration for powerful data transformations.

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Predictable, straightforward pricing model that simplified budgeting and significantly reduced overall spend

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“Airbyte helped us accelerate our progress by years, compared to our competitors. We don’t need to worry about connectors and focus on creating value for our users instead of building infrastructure. That’s priceless. The time and energy saved allows us to disrupt and grow faster.”

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Rupak Patel

Operational Intelligence Manager

"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."

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How to Sync to Manually

Step 1: Set Up Square API Access

To begin, you must obtain API credentials from Square. Log into your Square Developer Dashboard, create a new application, and note down your Access Token and Application ID. These credentials will be used to authenticate requests to the Square API.

Step 2: Extract Data from Square

Using the Square API, you can programmatically extract data such as transactions, customers, and items. Write a script in a programming language of your choice (e.g., Python) to send HTTP GET requests to the Square API endpoints. Ensure you handle pagination and rate limits as per Square's API documentation.

Step 3: Transform Data to BigQuery Schema

Once you've extracted the data, transform it into a format compatible with BigQuery. This involves converting the JSON response from Square into a CSV or JSON Lines (NDJSON) format that aligns with the schema you've defined for your BigQuery tables. Pay attention to data types and nested structures.

Step 4: Set Up Google Cloud SDK

Install the Google Cloud SDK on your local machine or server. This toolkit will allow you to interact with Google Cloud services, including BigQuery. Authenticate with your Google Cloud account using `gcloud auth login` and configure the SDK to use your project with `gcloud config set project [PROJECT_ID]`.

Step 5: Upload Data to Google Cloud Storage

Before inserting data into BigQuery, upload the transformed data files to a Google Cloud Storage (GCS) bucket. Use the `gsutil` command-line tool included with the Cloud SDK for this purpose. Run `gsutil cp [LOCAL_FILE_PATH] gs://[BUCKET_NAME]/[FILE_NAME]` to upload your data.

Step 6: Load Data into BigQuery

With your data in GCS, you can load it into BigQuery. Use the `bq` command-line tool to create a load job. The command `bq load --source_format=[CSV/NEWLINE_DELIMITED_JSON] [DATASET_NAME].[TABLE_NAME] gs://[BUCKET_NAME]/[FILE_NAME]` specifies the format and location of your data and the target BigQuery dataset and table.

Step 7: Verify Data Integrity and Automate

After loading the data, verify that it is correctly inserted into BigQuery by running simple queries to check for expected record counts and data consistency. Once verified, automate the extraction, transformation, and loading (ETL) process using scripts scheduled with cron jobs or similar scheduling tools to ensure regular updates from Square to BigQuery.

By following these steps, you can securely and efficiently transfer data from Square to BigQuery without relying on third-party connectors or integrations.