Add Google Dataflow docs

docs/en/integrations/data-ingestion/google-dataflow/dataflow.md

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+---
+sidebar_label: Integrating Dataflow with ClickHouse
+slug: /en/integrations/google-dataflow/dataflow
+sidebar_position: 1
+description: Users can ingest data into ClickHouse using Google Dataflow
+---
+
+# Integrating Google Dataflow with ClickHouse
+
+[Google Dataflow](https://cloud.google.com/dataflow) is a fully managed stream and batch data processing service. It supports pipelines written in Java or Python and is built on the Apache Beam SDK.
+
+There are two main ways to use Google Dataflow with ClickHouse, both are leveraging [`ClickHouseIO Apache Beam connector`](../../apache-beam):
+
+## 1. **[Java Runner](./java-runner)**
+The Java Runner allows users to implement custom Dataflow pipelines using the Apache Beam SDK `ClickHouseIO` integration. This approach provides full flexibility and control over the pipeline logic, enabling users to tailor the ETL process to specific requirements.
+However, this option requires knowledge of Java programming and familiarity with the Apache Beam framework.
+
+### Key Features:
+- High degree of customization.
+- Ideal for complex or advanced use cases.
+- Requires coding and understanding of the Beam API.
+
+## 2. **[Predefined Templates](./templates)**
+ClickHouse offers predefined templates designed for specific use cases, such as importing data from BigQuery into ClickHouse. These templates are ready-to-use and simplify the integration process, making them an excellent choice for users who prefer a no-code solution.
+
+### Key Features:
+- No Beam coding required.
+- Quick and easy setup for simple use cases.
+- Suitable also for users with minimal programming expertise.
+
+Both approaches are fully compatible with Google Cloud and the ClickHouse ecosystem, offering flexibility depending on your technical expertise and project requirements.  

docs/en/integrations/data-ingestion/google-dataflow/java-runner.md

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+---
+sidebar_label: Java Runner
+slug: /en/integrations/google-dataflow/java-runner
+sidebar_position: 2
+description: Users can ingest data into ClickHouse using Google Dataflow Java Runner
+---
+
+# Dataflow Java Runner
+
+The Dataflow Java Runner lets you execute custom Apache Beam pipelines on Google Cloud's Dataflow service. This approach provides maximum flexibility and is well-suited for advanced ETL workflows.
+
+## How It Works
+
+1. **Pipeline Implementation**  
+   To use the Java Runner, you need to implement your Beam pipeline using the `ClickHouseIO` - our official Apache Beam connector. For code examples and instructions on how to use the `ClickHouseIO`, please visit [ClickHouse Apache Beam](../../apache-beam).
+
+2. **Deployment**  
+   Once your pipeline is implemented and configured, you can deploy it to Dataflow using Google Cloud's deployment tools. Comprehensive deployment instructions are provided in the [Google Cloud Dataflow documentation - Java Pipeline](https://cloud.google.com/dataflow/docs/quickstarts/create-pipeline-java).
+
+**Note**: This approach assumes familiarity with the Beam framework and coding expertise. If you prefer a no-code solution, consider using [ClickHouse's predefined templates](./templates).  

docs/en/integrations/data-ingestion/google-dataflow/templates.md

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+---
+sidebar_label: Templates
+slug: /en/integrations/google-dataflow/templates
+sidebar_position: 3
+description: Users can ingest data into ClickHouse using Google Dataflow Templates
+---
+
+# Google Dataflow Templates
+
+Google Dataflow templates provide a convenient way to execute prebuilt, ready-to-use data pipelines without the need to write custom code. These templates are designed to simplify common data processing tasks and are built using [Apache Beam](https://beam.apache.org/), leveraging connectors like `ClickHouseIO` for seamless integration with ClickHouse databases. By running these templates on Google Dataflow, you can achieve highly scalable, distributed data processing with minimal effort.
+
+
+
+
+## Why Use Dataflow Templates?
+
+- **Ease of Use**: Templates eliminate the need for coding by offering preconfigured pipelines tailored to specific use cases.
+- **Scalability**: Dataflow ensures your pipeline scales efficiently, handling large volumes of data with distributed processing.
+- **Cost Efficiency**: Pay only for the resources you consume, with the ability to optimize pipeline execution costs.
+
+## How to Run Dataflow Templates
+
+As of today, the ClickHouse official template is available via the Google Cloud CLI or Dataflow REST API.
+For detailed step-by-step instructions, refer to the [Google Dataflow Run Pipeline From a Template Guide](https://cloud.google.com/dataflow/docs/templates/provided-templates).
+
+
+## List of ClickHouse Templates
+* [BigQuery To ClickHouse](./templates/bigquery-to-clickhouse)
+* GCS To ClickHouse (coming soon!)
+* Pub Sub To ClickHouse (coming soon!)

docs/en/integrations/index.mdx

@@ -242,6 +242,7 @@ We are actively compiling this list of ClickHouse integrations below, so it's no
 |------|----|----------------|------------------|-------------|
 |Apache Airflow|<img src={require('./images/logos/logo_airflow.png').default} className="image" alt="Airflow logo" style={{width: '3rem', 'backgroundColor': 'transparent'}}/>|Data ingestion|Open-source workflow management platform for data engineering pipelines|[Github](https://github.com/bryzgaloff/airflow-clickhouse-plugin)|
 |Apache Beam|<img src={require('./images/logos/logo_beam.png').default} className="image" alt="Beam logo" style={{width: '3rem', 'backgroundColor': 'transparent'}}/>|Data ingestion|Open source, unified model and set of language-specific SDKs for defining and executing data processing workflows. Compatible with Google Dataflow.|[Documentation](https://clickhouse.com/docs/en/integrations/apache-beam),<br/>[Examples](https://github.com/ClickHouse/clickhouse-beam-connector/)|
+|Google Dataflow|<img src={require('./images/logos/dataflow_logo.png').default} className="image" alt="Dataflow logo" style={{width: '3rem', 'backgroundColor': 'transparent'}}/>|Data ingestion|Google Dataflow is a serverless service for running batch and streaming data pipelines using Apache Beam.|[Documentation](/docs/en/integrations/google-dataflow/dataflow)|
 |Apache InLong|<img src={require('./images/logos/logo_inlong.png').default} className="image" alt="InLong logo" style={{width: '3rem', 'backgroundColor': 'transparent'}}/>|Data ingestion|One-stop integration framework for massive data|[Documentation](https://inlong.apache.org/docs/data_node/load_node/clickhouse)|
 |Apache NiFi|<img src={require('./images/logos/logo_nifi.png').default} className="image" alt="NiFi logo" style={{width: '3rem', 'backgroundColor': 'transparent'}}/>|Data ingestion|Automates the flow of data between software systems|[Documentation](/docs/en/integrations/nifi)|
 |Apache SeaTunnel|<img src={require('./images/logos/logo_seatunnel.png').default} className="image" alt="SeaTunnel logo" style={{width: '3rem', 'backgroundColor': 'transparent'}}/>|Data ingestion|SeaTunnel is a very easy-to-use ultra-high-performance distributed data integration platform|[Website](https://seatunnel.apache.org/docs/2.3.0/connector-v2/sink/Clickhouse)|

scripts/aspell-ignore/en/aspell-dict.txt

@@ -3452,4 +3452,4 @@ znode
 znodes
 zookeeperSessionUptime
 zstd
-Okta
+Okta

scripts/aspell-ignore/en/integrations/data-ingestion/google-dataflow/aspell-dict.txt

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+DataFlow
+Dataflow
+DataflowTemplates
+GoogleSQL

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