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Migrating databases from Google BigQuery to Yandex Managed Service for ClickHouse®

Written by
Updated at April 20, 2026

You can migrate a database from Google BigQuery to Yandex Managed Service for ClickHouse® and then use Yandex DataLens to analyze it.

A table is compressed and moved to a Google Storage bucket, from where it is transferred to an Yandex Object Storage bucket. After that, the data is imported to a Managed Service for ClickHouse® cluster where you can analyze it using Yandex DataLens.

This migration method offers the following benefits:

  • You can specify the export format and the compression ratio.
  • Significantly reduced data volume leads to faster migration and lower costs.

However, in this case, the data is migrated as is without transforming or copying the updated increments.

To migrate a database from Google BigQuery to Managed Service for ClickHouse®:

  1. Transfer data from Google BigQuery to Yandex Object Storage.
  2. Configure data mapping from Yandex Object Storage to the Managed Service for ClickHouse® cluster.
  3. Analyze your data with Yandex DataLens.

If you no longer need the resources you created, delete them.

The support cost for this solution includes:

Getting started

To migrate your database, first create the following Google Cloud and Yandex Cloud resources.

Create Google Cloud resources

  1. Create a Google Storage bucket.

  2. Create a Google Cloud service account with the BigQuery Data Editor and Storage Object Admin roles.

  3. Create an access key for the service account and save it as a .json file.

  4. Download and install the google-cloud-sdk CLI utilities.

  5. Authenticate in gcloud CLI.

  6. Install the Google BigQuery Python SDK. This package requires Python version 3.7 or higher.

  7. Prepare a dataset for Google BigQuery. For this example, we use a Google BigQuery’s public dataset google_trends, which includes the international_top_terms table with the following columns:

    • rank
    • country_name
    • country_code
    • region_name
    • week
    • score
    • region_code
    • term
    • refresh_date

Create Yandex Cloud resources

  1. Create a service account with the storage.uploader role to access the Object Storage bucket.

  2. Create a static access key for the service account. Save the key ID and secret key, as you will need them later.

  3. Create a Managed Service for ClickHouse® cluster of any suitable configuration. When creating your cluster:

    • Specify the service account you created earlier.

    • Enable DataLens access.

    • Add ZooKeeper hosts to ensure high cluster availability.

  4. Create an Object Storage bucket. When creating the bucket, activate public read permissions for objects and the bucket listing.

Migrate data from Google BigQuery to Yandex Object Storage

  1. Create a file named credentials.boto with access credentials for Google Cloud and Yandex Cloud resources:

    [Credentials]
gs_service_client_id  =<Google_Cloud_service_account>
gs_service_key_file   =<absolute_path_to_JSON_file>
aws_access_key_id     =<service_account_key_ID>
aws_secret_access_key =<service_account_secret_key>

[GSUtil]
  default_project_id    =<Google_Cloud_project_ID>

[s3]
  calling_format=boto.s3.connection.OrdinaryCallingFormat
  host=storage.yandexcloud.net

    Where:

    • gs_service_client_id: Google Cloud service account name in service-account-name@project-id.iam.gserviceaccount.com format.

    • gs_service_key_file: Absolute path to the JSON file containing your Google Cloud service account’s access key.

    • aws_access_key_id: Yandex Cloud service account key ID.

    • aws_secret_access_key: Yandex Cloud service account secret key.

    • default_project_id: Google Cloud project ID.

  2. Create a main.py script for data compression and migration:

    main.py
    from google.cloud import bigquery
import sys
import argparse
import time
import subprocess
import os
os.environ["GOOGLE_APPLICATION_CREDENTIALS"]="<absolute_path_to_JSON_file_with_Google_Cloud_service_account_access_key>"
os.environ["BOTO_CONFIG"]="<absolute_path_to_credentials.boto_file>"

def parse_args():
    parser = argparse.ArgumentParser(description='Export data from Google Big Query to Yandex Cloud object storage')
    parser.add_argument('--bq_project', type=str, help='GBQ project ID')
    parser.add_argument('--bq_location', type=str, help='GBQ table AND GS location')
    parser.add_argument('--gs_bucket', type=str, help='GS export destination bucket')
    parser.add_argument('--yc_bucket', type=str, help='YC copy destination bucket')
    parser.add_argument('--gsutil_path', type=str, help='GSutil exec path', default='gsutil')
    return parser.parse_args()

def select_from_list(message, elements):
    print(message)
    print("\t{}. {}".format(0, "Export all"))
    for ind in range(len(elements)):
        if isinstance(elements[ind].reference, bigquery.DatasetReference):
            print("\t{}. {}".format(ind+1, elements[ind].reference.dataset_id))
        elif isinstance(elements[ind].reference, bigquery.TableReference):
            print("\t{}. {}".format(ind+1, elements[ind].reference.table_id))
    try:
        return int(input("(any letter for cancel) >> "))
    except ValueError:
        print("Exiting")
        sys.exit()

if __name__ == '__main__':
    args = parse_args()
    client = bigquery.Client()

    datasets = list(client.list_datasets(args.bq_project))
    dataset_selector = select_from_list("Datasets in project {}".format(args.bq_project), datasets)
    export_list = []
    for i in range(len(datasets)):
        dataset_ref = datasets[i].reference
        if dataset_selector == 0:
            export_list += list(client.list_tables(dataset_ref))
        else:
            if i == dataset_selector - 1:
                tables = list(client.list_tables(dataset_ref))
                table_selector = select_from_list("Tables in dataset {}".format(dataset_ref.dataset_id),
                                                  tables)
                for j in range(len(tables)):
                    if table_selector == 0 or j == table_selector - 1:
                        export_list.append(tables[j])

    print("Starting tables export")
    for n in range(len(export_list)):
        table_ref = export_list[n].reference

        # Creating Extract Job config. Selecting compression level and data format.
        job_config = bigquery.job.ExtractJobConfig()
        job_config.compression = bigquery.Compression.GZIP
        job_config.destination_format = bigquery.DestinationFormat.PARQUET

        print("Exporting {} table".format(table_ref.table_id))
        extract_job = client.extract_table(
            source=table_ref,
            destination_uris="gs://{}/{}".format(args.gs_bucket, "{}-*".format(table_ref.table_id)),
            job_id="export-job-{}-{}".format(table_ref.table_id, round(time.time() * 1000)),
            location=args.bq_location,
            job_config=job_config)
        extract_job.result()
    print("Tables export done")

    # Calling gsutil rsync to synchronize source and destination buckets.
    source_uri = "gs://{}/".format(args.gs_bucket)
    destination_uri = "s3://{}/".format(args.yc_bucket)
    print("Synchronizing {} with {}...".format(source_uri, destination_uri))
    proc = subprocess.Popen([args.gsutil_path, "-m", "rsync", source_uri, destination_uri],
                            stdout=sys.stdout,
                            stderr=sys.stderr)
    proc.communicate()
    print("Buckets synchronization done")

  3. Run the main.py script to migrate data from Google BigQuery to the Google Storage, with subsequent transfer to the Yandex Object Storage bucket:

    python main.py \
    --bq_project=<Google_Cloud_project_ID> \
    --bq_location=US \
    --gs_bucket=<Google_Cloud_Storage_bucket_name> \
    --yc_bucket=<Object_Storage_bucket_name>

    Wait for the data migration to complete.

Configure data mapping from Yandex Object Storage to the Managed Service for ClickHouse® cluster

  1. To create a view of the imported data, connect to the Managed Service for ClickHouse® cluster database and run the following SQL query:

    CREATE view db1.v$google_top_rising_terms on cluster on cluster '{cluster}' AS
(SELECT
term,
score,
rank,
country_name,
country_code,
region_name,
region_code,
week,
refresh_date
FROM s3Cluster(
  '<cluster_ID>',
  'https://storage.yandexcloud.net/<Object_Storage_bucket_name>/top_terms-*',
  'Parquet',
  'rank Int32,
  country_name String,
  country_code String,
  region_name String,
  week Timestamp,
  score Nullable(Int32),
  region_code String,
  term String,
  refresh_date Timestamp')
)

    Where:

    • db1: Name of the database in the Managed Service for ClickHouse® cluster where you want to create a view.
    • v$google_top_rising_terms: Name of the view for the imported data.
    • <cluster_ID>: Managed Service for ClickHouse® cluster ID. You can get it from your folder’s cluster list.
    • top_terms-*: Naming prefix for Object Storage bucket objects. For example, if you migrate a Google Cloud table containing rows named top_terms, the corresponding objects in the Object Storage bucket will have the following names: top_terms-000000000001, top_terms-000000000002, etc. To include all table entries with this name in the view, use the top_terms-* pattern in your SQL query .

  2. To retrieve the first 100 records from the view, run the following SQL query (in our example, we use database db1 and its view v$google_top_rising_terms):

    SELECT * FROM db1.v$google_top_rising_terms limit 100

Analize your data with Yandex DataLens

  1. Connect the Managed Service for ClickHouse® cluster to DataLens.

  2. Create a dataset from the db1.v$google_top_rising_terms table. For the score field, select the average aggregation.

  3. Create a bar chart:

    1. Drag the country_name field to the X section.
    2. Drag the score field to the Y section.
    3. Drag the term field to the Filters section. In the form that opens, specify the following settings:
      • Operation: Belongs to a set.
      • Available: Select a term from a list of available terms, then click Apply filter.
    4. Drag the term field to the Sorting section.

The use of the specified search query will be analyzed, with the result displayed as a country-by-country bar chart.

Delete the resources you created

Delete the resources you no longer need to avoid paying for them:

  1. Delete the Managed Service for ClickHouse® cluster.
  2. Delete all objects from the Object Storage bucket, then delete the bucket.
  3. Delete the Google Storage bucket.

ClickHouse® is a registered trademark of ClickHouse, Inc.

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