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 BigQuery as a source connector (using Auth, or usually an API key)
2. set up Apache Iceberg 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 BigQuery

BigQuery is a cloud-based data warehousing and analytics platform that allows users to store, manage, and analyze large amounts of data in real-time. It is a fully managed service that eliminates the need for users to manage their own infrastructure, and it offers a range of features such as SQL querying, machine learning, and data visualization. BigQuery is designed to handle petabyte-scale datasets and can be used for a variety of use cases, including business intelligence, data exploration, and predictive analytics. It is a powerful tool for organizations looking to gain insights from their data and make data-driven decisions.

What is Apache Iceberg

For huge analytical tables, Apache Iceberg is a high-performance format. Using Apache Iceberg, engines such as Spark, Trino, Flink, Presto, Hive and Impala can safely work with the same tables, at the same time, providing the reliability and simplicity of SQL tables to big data. With Apache Iceberg, you can merge new data, update existing rows, and delete specific rows. Data files can be eagerly rewritten or deleted deltas can be used to make updates faster.

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Prerequisites

1. A BigQuery account to transfer your customer data automatically from.
2. A Apache Iceberg 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 BigQuery and Apache Iceberg, for seamless data migration.

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

Methods to Move Data From bigquery to apache iceberg

• Method 1: Connecting bigquery to apache iceberg using Airbyte.
• Method 2: Connecting biguery to apache iceberg manually.

Method 1: Connecting bigquery to apache iceberg using Airbyte

Step 1: Set up BigQuery as a source connector

1. First, you need to have a Google Cloud Platform account and a project with BigQuery enabled.

2. Go to the Google Cloud Console and create a new service account with the necessary permissions to access your BigQuery data.

3. Download the JSON key file for the service account and keep it safe.

4. Open Airbyte and go to the Sources page.

5. Click on the "Create a new source" button and select "BigQuery" from the list of available sources.

6. Enter a name for your source and click on "Next".

7. In the "Connection Configuration" section, enter the following information:  
- Project ID: the ID of your Google Cloud Platform project  
- JSON Key: copy and paste the contents of the JSON key file you downloaded earlier  
- Dataset: the name of the dataset you want to connect to

8. Click on "Test Connection" to make sure everything is working correctly.

9. If the test is successful, click on "Create Source" to save your configuration.

10. You can now use your BigQuery source connector to extract data from your dataset and load it into Airbyte for further processing.

Step 2: Set up Apache Iceberg as a destination connector

1. Open the Airbyte platform and navigate to the "Destinations" tab on the left-hand side of the screen.
2. Click on the "Apache Iceberg" destination connector and select "Create new connection."
3. Enter a name for your connection and provide the necessary credentials for your Apache Iceberg database, including the host, port, database name, username, and password.
4. Test the connection to ensure that it is successful. 5. Select the tables or data sources that you want to replicate to your Apache Iceberg database.
6. Configure any additional settings or options for your connection, such as the frequency of data replication or any transformations that you want to apply to your data.
7. Save your connection and start the replication process.
8. Monitor the progress of your data replication and troubleshoot any issues that may arise.
9. Once the replication process is complete, verify that your data has been successfully replicated to your Apache Iceberg database.
10. Use your Apache Iceberg database to analyze and query your data as needed.

Step 3: Set up a connection to sync your BigQuery data to Apache Iceberg

Once you've successfully connected BigQuery as a data source and Apache Iceberg 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 BigQuery from the dropdown list of your configured sources.
3. Select your destination: Choose Apache Iceberg 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 BigQuery objects you want to import data from towards Apache Iceberg. 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 BigQuery to Apache Iceberg according to your settings.

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

Method 2: Connecting biguery to apache iceberg manually

Moving data from Google BigQuery to Apache Iceberg without using third-party connectors or integrations involves several steps. You will need to extract the data from BigQuery, format it appropriately, and then load it into an Apache Iceberg table. Below is a step-by-step guide to accomplish this task.

Prerequisites

- Access to a Google Cloud Platform (GCP) account with BigQuery enabled.

- A local or cloud environment where you can run Apache Iceberg, such as a Hadoop cluster or a local setup with Hadoop libraries.

- Necessary permissions to read from BigQuery and write to the Iceberg table.

- Apache Spark or a similar tool that supports reading from BigQuery and writing to Iceberg tables.

Step 1: Set Up Your Environment

1. Install Java Development Kit (JDK) 8 or higher.

2. Install Apache Spark compatible with Iceberg. Make sure Spark is properly configured.

3. Set up Apache Iceberg by adding the Iceberg library to your Spark environment.

Step 2: Export Data from BigQuery

1. Log in to your Google Cloud Platform console.

2. Go to the BigQuery service.

3. Locate the dataset and table you want to export.

4. Export the table data to a Google Cloud Storage bucket in a format that is compatible with Apache Iceberg (e.g., Avro, Parquet).

   ```sql

   EXPORT DATA OPTIONS(

     uri='gs://your-bucket-name/your-data-prefix-*.parquet',

     format='PARQUET',

     overwrite=true

   ) AS

   SELECT * FROM your_dataset.your_table;

   ```

5. Ensure the export is successful and the files are in your Cloud Storage bucket.

Step 3: Download Data from Google Cloud Storage

1. Install and configure the `gsutil` command-line tool to interact with Google Cloud Storage.

2. Use `gsutil` to download the exported files to your local system or directly to the machine where Apache Iceberg is set up.

   ```

   gsutil cp gs://your-bucket-name/your-data-prefix-*.parquet /path/to/local/directory

   ```

Step 4: Set Up Apache Iceberg Table

1. Using Apache Spark with Iceberg, initialize the Iceberg table if it doesn't exist yet.

   ```scala

   val spark = SparkSession.builder()

     .appName(""Iceberg"")

     .config(""spark.sql.extensions"", ""org.apache.iceberg.spark.extensions.IcebergSparkSessionExtensions"")

     .config(""spark.sql.catalog.local"", ""org.apache.iceberg.spark.SparkCatalog"")

     .config(""spark.sql.catalog.local.type"", ""hadoop"")

     .config(""spark.sql.catalog.local.warehouse"", ""/path/to/iceberg/warehouse"")

     .getOrCreate()

   spark.sql(""CREATE TABLE IF NOT EXISTS local.db.table_name (id bigint, data string) USING iceberg"")

   ```

Step 5: Load Data into Apache Iceberg Table

1. Read the downloaded data into a Spark DataFrame.

   ```scala

   val parquetData = spark.read.parquet(""/path/to/local/directory/your-data-prefix-*.parquet"")

   ```

2. Append the data to the Iceberg table.

   ```scala

   parquetData.write.format(""iceberg"").mode(""append"").save(""local.db.table_name"")

   ```

3. Verify that the data has been successfully written to the Iceberg table.

   ```scala

   spark.read.format(""iceberg"").load(""local.db.table_name"").show()

   ```

Step 6: Clean Up

1. Once the data is confirmed to be in the Iceberg table, clean up any temporary files or data that you no longer need.

Notes

- Make sure you handle schema evolution, if applicable, when moving data from BigQuery to Iceberg.

- Consider incremental updates if you plan to sync the data regularly.

- Always monitor the data transfer process for any errors or discrepancies.

By following these steps, you can manually move data from BigQuery to Apache Iceberg without the need for third-party connectors or integrations. Remember to adjust file paths, table names, and configurations according to your specific environment and needs.

Use Cases to transfer your BigQuery data to Apache Iceberg

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

1. Advanced Analytics: Apache Iceberg’s powerful data processing capabilities enable you to perform complex queries and data analysis on your BigQuery data, extracting insights that wouldn't be possible within BigQuery alone.
2. Data Consolidation: If you're using multiple other sources along with BigQuery, syncing to Apache Iceberg 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: BigQuery has limits on historical data. Syncing data to Apache Iceberg allows for long-term data retention and analysis of historical trends over time.
4. Data Security and Compliance: Apache Iceberg provides robust data security features. Syncing BigQuery data to Apache Iceberg ensures your data is secured and allows for advanced data governance and compliance management.
5. Scalability: Apache Iceberg can handle large volumes of data without affecting performance, providing an ideal solution for growing businesses with expanding BigQuery data.
6. Data Science and Machine Learning: By having BigQuery data in Apache Iceberg, you can apply machine learning models to your data for predictive analytics, customer segmentation, and more.
7. Reporting and Visualization: While BigQuery provides reporting tools, data visualization tools like Tableau, PowerBI, Looker (Google Data Studio) can connect to Apache Iceberg, providing more advanced business intelligence options. If you have a BigQuery table that needs to be converted to a Apache Iceberg table, Airbyte can do that automatically.

Wrapping Up

To summarize, this tutorial has shown you how to:

1. Configure a BigQuery account as an Airbyte data source connector.
2. Configure Apache Iceberg as a data destination connector.
3. Create an Airbyte data pipeline that will automatically be moving data directly from BigQuery to Apache Iceberg 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!