Merge pull request #3524 from ClickHouse/supported_badges

Community Maintained + ClickHouse Supported Badges

Author: gingerwizard

docs/integrations/data-ingestion/etl-tools/airbyte-and-clickhouse.md

@@ -17,9 +17,12 @@ import airbyte06 from '@site/static/images/integrations/data-ingestion/etl-tools
 import airbyte07 from '@site/static/images/integrations/data-ingestion/etl-tools/airbyte_07.png';
 import airbyte08 from '@site/static/images/integrations/data-ingestion/etl-tools/airbyte_08.png';
 import airbyte09 from '@site/static/images/integrations/data-ingestion/etl-tools/airbyte_09.png';
+import CommunityMaintainedBadge from '@theme/badges/CommunityMaintained';
 
 # Connect Airbyte to ClickHouse
 
+<CommunityMaintainedBadge/>
+
 :::note
 Please note that the Airbyte source and destination for ClickHouse are currently in Alpha status and not suitable for moving large datasets (> 10 million rows)
 :::

docs/integrations/data-ingestion/etl-tools/apache-beam.md

@@ -5,8 +5,12 @@ description: 'Users can ingest data into ClickHouse using Apache Beam'
 title: 'Integrating Apache Beam and ClickHouse'
 ---
 
+import ClickHouseSupportedBadge from '@theme/badges/ClickHouseSupported';
+
 # Integrating Apache Beam and ClickHouse
 
+<ClickHouseSupportedBadge/>
+
 **Apache Beam**  is an open-source, unified programming model that enables developers to define and execute both batch and stream (continuous) data processing pipelines. The flexibility of Apache Beam lies in its ability to support a wide range of data processing scenarios, from ETL (Extract, Transform, Load) operations to complex event processing and real-time analytics.
 This integration leverage ClickHouse's official [JDBC connector](https://github.com/ClickHouse/clickhouse-java) for the underlying insertion layer.
 
@@ -77,7 +81,7 @@ public class Main {
         PCollection<Row> rows = lines.apply("ConvertToRow", ParDo.of(new DoFn<String, Row>() {
             @ProcessElement
             public void processElement(@Element String line, OutputReceiver<Row> out) {
-            
+
                 String[] values = line.split(",");
                 Row row = Row.withSchema(SCHEMA)
                         .addValues(values[0], Short.parseShort(values[1]), DateTime.now())
@@ -141,10 +145,10 @@ You can adjust the `ClickHouseIO.Write` configuration with the following setter
 
 Please consider the following limitations when using the connector:
 * As of today, only Sink operation is supported. The connector doesn't support Source operation.
-* ClickHouse performs deduplication when inserting into a `ReplicatedMergeTree` or a `Distributed` table built on top of a `ReplicatedMergeTree`. Without replication, inserting into a regular MergeTree can result in duplicates if an insert fails and then successfully retries. However, each block is inserted atomically, and the block size can be configured using `ClickHouseIO.Write.withMaxInsertBlockSize(long)`. Deduplication is achieved by using checksums of the inserted blocks. For more information about deduplication, please visit [Deduplication](/guides/developer/deduplication) and [Deduplicate insertion config](/operations/settings/settings#insert_deduplicate). 
+* ClickHouse performs deduplication when inserting into a `ReplicatedMergeTree` or a `Distributed` table built on top of a `ReplicatedMergeTree`. Without replication, inserting into a regular MergeTree can result in duplicates if an insert fails and then successfully retries. However, each block is inserted atomically, and the block size can be configured using `ClickHouseIO.Write.withMaxInsertBlockSize(long)`. Deduplication is achieved by using checksums of the inserted blocks. For more information about deduplication, please visit [Deduplication](/guides/developer/deduplication) and [Deduplicate insertion config](/operations/settings/settings#insert_deduplicate).
 * The connector doesn't perform any DDL statements; therefore, the target table must exist prior insertion.
 
 
 ## Related Content {#related-content}
 * `ClickHouseIO` class [documentation](https://beam.apache.org/releases/javadoc/current/org/apache/beam/sdk/io/clickhouse/ClickHouseIO.html).
-* `Github` repository of examples [clickhouse-beam-connector](https://github.com/ClickHouse/clickhouse-beam-connector). 
+* `Github` repository of examples [clickhouse-beam-connector](https://github.com/ClickHouse/clickhouse-beam-connector).

docs/integrations/data-ingestion/etl-tools/dbt/index.md

@@ -15,9 +15,12 @@ import dbt_04 from '@site/static/images/integrations/data-ingestion/etl-tools/db
 import dbt_05 from '@site/static/images/integrations/data-ingestion/etl-tools/dbt/dbt_05.png';
 import dbt_06 from '@site/static/images/integrations/data-ingestion/etl-tools/dbt/dbt_06.png';
 import dbt_07 from '@site/static/images/integrations/data-ingestion/etl-tools/dbt/dbt_07.png';
+import ClickHouseSupportedBadge from '@theme/badges/ClickHouseSupported';
 
 # Integrating dbt and ClickHouse
 
+<ClickHouseSupportedBadge/>
+
 **dbt** (data build tool) enables analytics engineers to transform data in their warehouses by simply writing select statements. dbt handles materializing these select statements into objects in the database in the form of tables and views - performing the T of [Extract Load and Transform (ELT)](https://en.wikipedia.org/wiki/Extract,_load,_transform). Users can create a model defined by a SELECT statement.
 
 Within dbt, these models can be cross-referenced and layered to allow the construction of higher-level concepts. The boilerplate SQL required to connect models is automatically generated. Furthermore, dbt identifies dependencies between models and ensures they are created in the appropriate order using a directed acyclic graph (DAG).

docs/integrations/data-ingestion/etl-tools/dlt-and-clickhouse.md

@@ -6,15 +6,19 @@ title: 'Connect dlt to ClickHouse'
 slug: /integrations/data-ingestion/etl-tools/dlt-and-clickhouse
 ---
 
+import CommunityMaintainedBadge from '@theme/badges/CommunityMaintained';
+
 # Connect dlt to ClickHouse
 
+<CommunityMaintainedBadge/>
+
 <a href="https://dlthub.com/docs/intro" target="_blank">dlt</a>  is an open-source library that you can add to your Python scripts to load data from various and often messy data sources into well-structured, live datasets.
 
 ## Install dlt with ClickHouse {#install-dlt-with-clickhouse}
 
 ### To Install the `dlt` library with ClickHouse dependencies: {#to-install-the-dlt-library-with-clickhouse-dependencies}
 ```bash
-pip install "dlt[clickhouse]" 
+pip install "dlt[clickhouse]"
 ```
 
 ## Setup Guide {#setup-guide}
@@ -98,7 +102,7 @@ All [write dispositions](https://dlthub.com/docs/general-usage/incremental-loadi
 
 Write dispositions in the dlt library define how the data should be written to the destination. There are three types of write dispositions:
 
-**Replace**: This disposition replaces the data in the destination with the data from the resource. It deletes all the classes and objects and recreates the schema before loading the data. You can learn more about it <a href="https://dlthub.com/docs/general-usage/full-loading">here</a>. 
+**Replace**: This disposition replaces the data in the destination with the data from the resource. It deletes all the classes and objects and recreates the schema before loading the data. You can learn more about it <a href="https://dlthub.com/docs/general-usage/full-loading">here</a>.
 
 **Merge**: This write disposition merges the data from the resource with the data at the destination. For `merge` disposition, you would need to specify a `primary_key` for the resource. You can learn more about it <a href="https://dlthub.com/docs/general-usage/incremental-loading">here</a>.
 
@@ -107,7 +111,7 @@ Write dispositions in the dlt library define how the data should be written to t
 ## Data Loading {#data-loading}
 Data is loaded into ClickHouse using the most efficient method depending on the data source:
 
-- For local files, the `clickhouse-connect` library is used to directly load files into ClickHouse tables using the `INSERT` command. 
+- For local files, the `clickhouse-connect` library is used to directly load files into ClickHouse tables using the `INSERT` command.
 - For files in remote storage like `S3`,` Google Cloud Storage`, or `Azure Blob Storage`, ClickHouse table functions like s3, gcs and azureBlobStorage are used to read the files and insert the data into tables.
 
 ## Datasets {#datasets}
@@ -220,4 +224,3 @@ Integration with <a href="https://dlthub.com/docs/dlt-ecosystem/transformations/
 
 ### Syncing of `dlt` state {#syncing-of-dlt-state}
 This destination fully supports <a href="https://dlthub.com/docs/general-usage/state#syncing-state-with-destination">dlt</a> state sync.
-

docs/integrations/data-ingestion/etl-tools/fivetran/index.md

@@ -6,11 +6,15 @@ description: 'Users can transform and model their data in ClickHouse using dbt'
 title: 'Fivetran and ClickHouse Cloud'
 ---
 
+import ClickHouseSupportedBadge from '@theme/badges/ClickHouseSupported';
+
 # Fivetran and ClickHouse Cloud
 
+<ClickHouseSupportedBadge/>
+
 ## Overview {#overview}
 
-[Fivetran](https://www.fivetran.com) is the automated data movement platform moving data out of, into and across your cloud data platforms. 
+[Fivetran](https://www.fivetran.com) is the automated data movement platform moving data out of, into and across your cloud data platforms.
 
 [ClickHouse Cloud](https://clickhouse.com/cloud) is supported as a [Fivetran destination](https://fivetran.com/docs/destinations/clickhouse), allowing users to load data from various sources into ClickHouse.
 

docs/integrations/data-ingestion/etl-tools/nifi-and-clickhouse.md

@@ -24,9 +24,12 @@ import nifi12 from '@site/static/images/integrations/data-ingestion/etl-tools/ni
 import nifi13 from '@site/static/images/integrations/data-ingestion/etl-tools/nifi_13.png';
 import nifi14 from '@site/static/images/integrations/data-ingestion/etl-tools/nifi_14.png';
 import nifi15 from '@site/static/images/integrations/data-ingestion/etl-tools/nifi_15.png';
+import CommunityMaintainedBadge from '@theme/badges/CommunityMaintained';
 
 # Connect Apache NiFi to ClickHouse
 
+<CommunityMaintainedBadge/>
+
 <a href="https://nifi.apache.org/" target="_blank">Apache NiFi</a> is an open-source workflow management software designed to automate data flow between software systems. It allows the creation of ETL data pipelines and is shipped with more than 300 data processors. This step-by-step tutorial shows how to connect Apache NiFi to ClickHouse as both a source and destination, and to load a sample dataset.
 
 ## 1. Gather your connection details {#1-gather-your-connection-details}

docs/integrations/data-ingestion/etl-tools/vector-to-clickhouse.md

@@ -9,9 +9,12 @@ title: 'Integrating Vector with ClickHouse'
 import Image from '@theme/IdealImage';
 import vector01 from '@site/static/images/integrations/data-ingestion/etl-tools/vector_01.png';
 import vector02 from '@site/static/images/integrations/data-ingestion/etl-tools/vector_02.png';
+import CommunityMaintainedBadge from '@theme/badges/CommunityMaintained';
 
 # Integrating Vector with ClickHouse
 
+<CommunityMaintainedBadge/>
+
 Being able to analyze your logs in real time is critical for production applications. Have you ever wondered if ClickHouse is good at storing and analyzing log data? Just checkout <a href="https://eng.uber.com/logging/" target="_blank">Uber's experience</a> with converting their logging infrastructure from ELK to ClickHouse.
 
 This guide shows how to use the popular data pipeline <a href="https://vector.dev/docs/about/what-is-vector/" target="_blank">Vector</a> to tail an Nginx log file and send it to ClickHouse. The steps below would be similar for tailing any type of log file. We will assume you already have ClickHouse up and running and Vector installed (no need to start it yet though).

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

@@ -6,8 +6,12 @@ description: 'Users can ingest data into ClickHouse using Google Dataflow'
 title: 'Integrating Google Dataflow with ClickHouse'
 ---
 
+import ClickHouseSupportedBadge from '@theme/badges/ClickHouseSupported';
+
 # Integrating Google Dataflow with ClickHouse
 
+<ClickHouseSupportedBadge/>
+
 [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`](/integrations/apache-beam):
@@ -29,4 +33,4 @@ ClickHouse offers [predefined templates](./templates) designed for specific use
 - 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.  
+Both approaches are fully compatible with Google Cloud and the ClickHouse ecosystem, offering flexibility depending on your technical expertise and project requirements.

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

@@ -6,16 +6,20 @@ description: 'Users can ingest data into ClickHouse using Google Dataflow Java R
 title: 'Dataflow Java Runner'
 ---
 
+import ClickHouseSupportedBadge from '@theme/badges/ClickHouseSupported';
+
 # Dataflow Java Runner
 
+<ClickHouseSupportedBadge/>
+
 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 {#how-it-works}
 
-1. **Pipeline Implementation**  
+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](/integrations/apache-beam).
 
-2. **Deployment**  
+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).  
+**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/integrations/data-ingestion/google-dataflow/templates.md

@@ -6,8 +6,12 @@ description: 'Users can ingest data into ClickHouse using Google Dataflow Templa
 title: 'Google Dataflow Templates'
 ---
 
+import ClickHouseSupportedBadge from '@theme/badges/ClickHouseSupported';
+
 # Google Dataflow Templates
 
+<ClickHouseSupportedBadge/>
+
 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? {#why-use-dataflow-templates}
@@ -25,4 +29,4 @@ For detailed step-by-step instructions, refer to the [Google Dataflow Run Pipeli
 ## List of ClickHouse Templates {#list-of-clickhouse-templates}
 * [BigQuery To ClickHouse](./templates/bigquery-to-clickhouse)
 * [GCS To ClickHouse](https://github.com/ClickHouse/DataflowTemplates/issues/3) (coming soon!)
-* [Pub Sub To ClickHouse](https://github.com/ClickHouse/DataflowTemplates/issues/4) (coming soon!)
+* [Pub Sub To ClickHouse](https://github.com/ClickHouse/DataflowTemplates/issues/4) (coming soon!)