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Copy file name to clipboardExpand all lines: docs/cloud/reference/changelog.md
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@@ -30,10 +30,21 @@ import query_endpoints from '@site/static/images/cloud/reference/may-17-query-en
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import dashboards from '@site/static/images/cloud/reference/may-30-dashboards.png';
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In addition to this ClickHouse Cloud changelog, please see the [Cloud Compatibility](/cloud/reference/cloud-compatibility.md) page.
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## June 13, 2025 {#june-13-2025}
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- We're excited to announce that ClickHouse Cloud Dashboards are now generally available. Dashboards allow users to visualize queries on dashboards, interact with data via filters and query parameters, and manage sharing.
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## June 27, 2025 {#june-27-2025}
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- We now officially support a Terraform provider for managing database privileges
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which is also compatible with self-managed deployments. Please refer to the
and our [docs](https://registry.terraform.io/providers/ClickHouse/clickhousedbops/latest/docs)
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for more information.
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- Enterprise tier services can now enlist in the [slow release channel](/manage/updates/#slow-release-channel-deferred-upgrades) to defer
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upgrades by two weeks after the regular release to permit additional time for
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testing.
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## June 13, 2025 {#june-13-2025}
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- We're excited to announce that ClickHouse Cloud Dashboards are now generally available. Dashboards allow users to visualize queries on dashboards, interact with data via filters and query parameters, and manage sharing.
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- API key IP filters: we are introducing an additional layer of protection for your interactions with ClickHouse Cloud. When generating an API key, you may setup an IP allow list to limit where the API key may be used. Please refer to the [documentation](https://clickhouse.com/docs/cloud/security/setting-ip-filters) for details.
Copy file name to clipboardExpand all lines: docs/integrations/data-ingestion/google-dataflow/templates/bigquery-to-clickhouse.md
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Default values for all `ClickHouseIO` parameters can be found in [`ClickHouseIO` Apache Beam Connector](/integrations/apache-beam#clickhouseiowrite-parameters)
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:::
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## Source and Target Tables Schema {#source-and-target-tables-schema}
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## Source and target tables schema {#source-and-target-tables-schema}
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To effectively load the BigQuery dataset into ClickHouse, the pipeline performs a column inference process with the following phases:
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-**Job Name:** The text following the `run` keyword is the unique job name.
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-**Template File:** The JSON file specified by `--template-file-gcs-location` defines the template structure and
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details about the accepted parameters. The mention file path is public and ready to use.
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-**Parameters:** Parameters are separated by commas. For string-based parameters, enclose the values in double quotes.
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### Expected Response {#expected-response}
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### Expected response {#expected-response}
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After running the command, you should see a response similar to the following:
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* Increase the instance resources: Upgrade your ClickHouse server to a larger instance with more memory to handle the data processing load.
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* Decrease the batch size: Adjust the batch size in your Dataflow job configuration to send smaller chunks of data to ClickHouse, reducing memory consumption per batch. These changes can help balance resource usage during data ingestion.
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## Template Source Code {#template-source-code}
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## Template source code {#template-source-code}
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The template's source code is available in ClickHouse's [DataflowTemplates](https://github.com/ClickHouse/DataflowTemplates) fork.
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