TL;DR

This can be done by building a data pipeline manually, usually a Python script (you can leverage a tool as Apache Airflow for this). This process can take more than a full week of development. Or it can be done in minutes on Airbyte in three easy steps:

1. set up Parquet File as a source connector (using Auth, or usually an API key)
2. set up BigQuery as a destination connector
3. define which data you want to transfer and how frequently

You can choose to self-host the pipeline using Airbyte Open Source or have it managed for you with Airbyte Cloud.

This tutorial’s purpose is to show you how.

What is Parquet File

Parquet File is a columnar storage file format that is designed to store and process large amounts of data efficiently. It is an open-source project that was developed by Cloudera and Twitter. Parquet File is optimized for use with Hadoop and other big data processing frameworks, and it is designed to work well with both structured and unstructured data. The format is highly compressed, which makes it ideal for storing and processing large datasets. Parquet File is also designed to be highly scalable, which means that it can be used to store and process data across multiple nodes in a distributed computing environment.

What is BigQuery

BigQuery is an enterprise data warehouse that draws on the processing power of Google Cloud Storage to enable fast processing of SQL queries through massive datasets. BigQuery helps businesses select the most appropriate software provider to assemble their data, based on the platforms the business uses. Once a business’ data is acculumated, it is moved into BigQuery. The company controls access to the data, but BigQuery stores and processes it for greater speed and convenience.

{{COMPONENT_CTA}}

Prerequisites

1. A Parquet File account to transfer your customer data automatically from.
2. A BigQuery account.
3. An active Airbyte Cloud account, or you can also choose to use Airbyte Open Source locally. You can follow the instructions to set up Airbyte on your system using docker-compose.

Airbyte is an open-source data integration platform that consolidates and streamlines the process of extracting and loading data from multiple data sources to data warehouses. It offers pre-built connectors, including Parquet File and BigQuery, for seamless data migration.

When using Airbyte to move data from Parquet File to BigQuery, it extracts data from Parquet File using the source connector, converts it into a format BigQuery can ingest using the provided schema, and then loads it into BigQuery via the destination connector. This allows businesses to leverage their Parquet File data for advanced analytics and insights within BigQuery, simplifying the ETL process and saving significant time and resources.

Methods to Move Data From Parquet to bigquery

• Method 1: Connecting Parquet to bigquery using Airbyte.
• Method 2: Connecting Parquet to bigquery manually.

Method 1: Connecting Parquet to bigquery using Airbyte

Step 1: Set up Parquet File as a source connector

1. Open the Airbyte dashboard and click on "Sources" on the left-hand side of the screen.
2. Click on the "Create Connection" button and select "Parquet File" from the list of available connectors.
3. Enter a name for your connection and click on "Next".
4. In the "Configuration" tab, enter the path to your Parquet file in the "File Path" field.
5. If your Parquet file is password-protected, enter the password in the "Password" field.
6. If your Parquet file is encrypted, select the appropriate encryption type from the "Encryption Type" dropdown menu and enter the encryption key in the "Encryption Key" field.
7. Click on "Test Connection" to ensure that your credentials are correct and that Airbyte can connect to your Parquet file.
8. If the test is successful, click on "Create" to save your connection.
9. You can now use this connection to create a new Airbyte pipeline and start syncing data from your Parquet file to your destination.

Step 2: Set up BigQuery as a destination connector

1. First, navigate to the Airbyte dashboard and select the "Destinations" tab on the left-hand side of the screen.

2. Scroll down until you find the "BigQuery" destination connector and click on it.

3. Click the "Create Destination" button to begin setting up your BigQuery destination.

4. Enter your Google Cloud Platform project ID and service account credentials in the appropriate fields.

5. Next, select the dataset you want to use for your destination and enter the table prefix you want to use.

6. Choose the schema mapping for your data, which will determine how your data is organized in BigQuery.

7. Finally, review your settings and click the "Create Destination" button to complete the setup process.

8. Once your destination is created, you can begin configuring your source connectors to start syncing data to BigQuery.

9. To do this, navigate to the "Sources" tab on the left-hand side of the screen and select the source connector you want to use.

10. Follow the prompts to enter your source credentials and configure your sync settings.

11. When you reach the "Destination" step, select your BigQuery destination from the dropdown menu and choose the dataset and table prefix you want to use.

12. Review your settings and click the "Create Connection" button to start syncing data from your source to your BigQuery destination.

Step 3: Set up a connection to sync your Parquet File data to BigQuery

Once you've successfully connected Parquet File as a data source and BigQuery as a destination in Airbyte, you can set up a data pipeline between them with the following steps:

1. Create a new connection: On the Airbyte dashboard, navigate to the 'Connections' tab and click the '+ New Connection' button.
2. Choose your source: Select Parquet File from the dropdown list of your configured sources.
3. Select your destination: Choose BigQuery from the dropdown list of your configured destinations.
4. Configure your sync: Define the frequency of your data syncs based on your business needs. Airbyte allows both manual and automatic scheduling for your data refreshes.
5. Select the data to sync: Choose the specific Parquet File objects you want to import data from towards BigQuery. You can sync all data or select specific tables and fields.
6. Select the sync mode for your streams: Choose between full refreshes or incremental syncs (with deduplication if you want), and this for all streams or at the stream level. Incremental is only available for streams that have a primary cursor.
7. Test your connection: Click the 'Test Connection' button to make sure that your setup works. If the connection test is successful, save your configuration.
8. Start the sync: If the test passes, click 'Set Up Connection'. Airbyte will start moving data from Parquet File to BigQuery according to your settings.

Remember, Airbyte keeps your data in sync at the frequency you determine, ensuring your BigQuery data warehouse is always up-to-date with your Parquet File data.

Method 2: Connecting Parquet to bigquery manually

Moving data from Parquet files to Google BigQuery without using third-party connectors or integrations involves a few steps, including setting up Google Cloud services, preparing the data, and using Google Cloud's native tools to import the data. Here is a step-by-step guide:

Step 1: Set up Google Cloud Platform (GCP) Project

1. Go to the Google Cloud Console: https://console.cloud.google.com/

2. Create a new project or select an existing one.

3. Enable billing for the project if it's not already enabled.

Step 2: Enable BigQuery API

1. Navigate to the "APIs & Services" dashboard.

2. Click on "+ ENABLE APIS AND SERVICES".

3. Search for "BigQuery API" and enable it for your project.

Step 3: Set Up Authentication

1. Go to the "IAM & Admin" section, then select "Service accounts".

2. Create a new service account with a role that has permissions to access BigQuery (e.g., BigQuery Admin).

3. Create a key for the service account in JSON format and download it.

Step 4: Install Google Cloud SDK (if not already installed)

1. Download and install the Google Cloud SDK from: https://cloud.google.com/sdk/docs/install

2. Initialize the SDK by running `gcloud init` and follow the prompts to authenticate and set your default project.

Step 5: Prepare the Parquet Data

1. Ensure your Parquet files are accessible. If they are on your local machine, make sure they are in a directory that you can easily navigate to.

2. If the Parquet files are large, consider splitting them into smaller chunks to optimize the upload and import process.

Step 6: Upload Parquet Files to Google Cloud Storage (GCS)

1. Create a new bucket in GCS or use an existing one.

2. Use `gsutil cp` command to upload Parquet files to the bucket:

   ```

   gsutil cp /path/to/your/parquet/files/*.parquet gs://your-bucket-name/parquet-files/

   ```

3. Ensure the files are successfully uploaded to the GCS bucket.

Step 7: Create a BigQuery Dataset and Table

1. In the BigQuery Console, create a new dataset where you will store your imported data.

2. Define a schema for your BigQuery table that corresponds to the schema of your Parquet files. You can define the schema manually or let BigQuery auto-detect it during the import process.

Step 8: Import Data from GCS to BigQuery

1. In the BigQuery Console, navigate to your dataset.

2. Click on "CREATE TABLE", and in the source section, select "Google Cloud Storage".

3. Enter the GCS URI of your Parquet files (e.g., `gs://your-bucket-name/parquet-files/*.parquet`).

4. Choose "Parquet" as the source data format.

5. Configure the destination table with the appropriate dataset and table name.

6. (Optional) Choose the schema auto-detection if you did not define a schema in Step 7.

7. Click "Create table" to start the import process.

Step 9: Verify Data Import

1. After the import process is complete, run some queries in BigQuery to ensure that the data has been imported correctly.

2. Check for any errors or warnings that might have occurred during the import process.

Step 10: Clean Up

1. If you no longer need the Parquet files in GCS, delete them to avoid incurring storage costs.

2. Remove any unnecessary service account keys and revoke roles that are no longer needed.

By following these steps, you can move data from Parquet files to Google BigQuery without the need for third-party connectors or integrations. Remember to handle your credentials securely and to follow best practices for managing GCP resources.

Use Cases to transfer your Parquet File data to BigQuery

Integrating data from Parquet File to BigQuery provides several benefits. Here are a few use cases:

1. Advanced Analytics: BigQuery’s powerful data processing capabilities enable you to perform complex queries and data analysis on your Parquet File data, extracting insights that wouldn't be possible within Parquet File alone.
2. Data Consolidation: If you're using multiple other sources along with Parquet File, syncing to BigQuery allows you to centralize your data for a holistic view of your operations, and to set up a change data capture process so you never have any discrepancies in your data again.
3. Historical Data Analysis: Parquet File has limits on historical data. Syncing data to BigQuery allows for long-term data retention and analysis of historical trends over time.
4. Data Security and Compliance: BigQuery provides robust data security features. Syncing Parquet File data to BigQuery ensures your data is secured and allows for advanced data governance and compliance management.
5. Scalability: BigQuery can handle large volumes of data without affecting performance, providing an ideal solution for growing businesses with expanding Parquet File data.
6. Data Science and Machine Learning: By having Parquet File data in BigQuery, you can apply machine learning models to your data for predictive analytics, customer segmentation, and more.
7. Reporting and Visualization: While Parquet File provides reporting tools, data visualization tools like Tableau, PowerBI, Looker (Google Data Studio) can connect to BigQuery, providing more advanced business intelligence options. If you have a Parquet File table that needs to be converted to a BigQuery table, Airbyte can do that automatically.

Wrapping Up

To summarize, this tutorial has shown you how to:

1. Configure a Parquet File account as an Airbyte data source connector.
2. Configure BigQuery as a data destination connector.
3. Create an Airbyte data pipeline that will automatically be moving data directly from Parquet File to BigQuery after you set a schedule

With Airbyte, creating data pipelines take minutes, and the data integration possibilities are endless. Airbyte supports the largest catalog of API tools, databases, and files, among other sources. Airbyte's connectors are open-source, so you can add any custom objects to the connector, or even build a new connector from scratch without any local dev environment or any data engineer within 10 minutes with the no-code connector builder.

We look forward to seeing you make use of it! We invite you to join the conversation on our community Slack Channel, or sign up for our newsletter. You should also check out other Airbyte tutorials, and Airbyte’s content hub!