Merge branch 'main' into Blargian-patch-969026

Author: Blargian

docs/about-us/support.md

@@ -9,15 +9,15 @@ doc_type: 'reference'
 
 # ClickHouse Cloud support services
 
-ClickHouse provides Support Services for our ClickHouse Cloud users and customers. Our objective is a Support Services team that represents the ClickHouse product – unparalleled performance, ease of use, and exceptionally fast, high-quality results. For details, [visit our ClickHouse Support Program](https://clickhouse.com/support/program/) page.
+ClickHouse provides support services for our ClickHouse Cloud users and customers. Our objective is a support services team that represents the ClickHouse product – unparalleled performance, ease of use, and exceptionally fast, high-quality results. For details, [visit our ClickHouse Support Program](https://clickhouse.com/support/program/) page.
 
 [Login to the Cloud console](https://console.clickhouse.cloud/support) and select **Help -> Support** from the menu options to open a new support case and view the status of your submitted cases. 
 
 You can also subscribe to our [status page](https://status.clickhouse.com) to get notified quickly about any incidents affecting our platform.
 
 :::note
-Please note that only Subscription Customers have a Service Level Agreement on Support Incidents. If you are not currently a ClickHouse Cloud user – while we will try to answer your question, we'd encourage you to go instead to one of our Community resources:
+Please note that only subscription customers have a service level agreement on support incidents. If you are not currently a ClickHouse Cloud user – while we will try to answer your question, we'd encourage you to go instead to one of our community resources:
 
-- [ClickHouse Community Slack Channel](https://clickhouse.com/slack)
-- [Other Community Options](https://github.com/ClickHouse/ClickHouse/blob/master/README.md#useful-links)
+- [ClickHouse community Slack channel](https://clickhouse.com/slack)
+- [Other community options](https://github.com/ClickHouse/ClickHouse/blob/master/README.md#useful-links)
 :::

docs/best-practices/select_data_type.md

@@ -1,8 +1,8 @@
 ---
 slug: /best-practices/select-data-types
 sidebar_position: 10
-sidebar_label: 'Selecting data Types'
-title: 'Selecting data Types'
+sidebar_label: 'Selecting data types'
+title: 'Selecting data types'
 description: 'Page describing how to choose data types in ClickHouse'
 keywords: ['data types']
 doc_type: 'reference'

docs/integrations/data-ingestion/apache-spark/spark-jdbc.md

@@ -11,8 +11,12 @@ doc_type: 'guide'
 import Tabs from '@theme/Tabs';
 import TabItem from '@theme/TabItem';
 import TOCInline from '@theme/TOCInline';
+import ClickHouseSupportedBadge from '@theme/badges/ClickHouseSupported';
 
 # Spark JDBC
+
+<ClickHouseSupportedBadge/>
+
 JDBC is one of the most commonly used data sources in Spark.
 In this section, we will provide details on how to
 use the [ClickHouse official JDBC connector](/integrations/language-clients/java/jdbc) with Spark.

docs/integrations/data-ingestion/aws-glue/index.md

@@ -13,9 +13,12 @@ import Tabs from '@theme/Tabs';
 import TabItem from '@theme/TabItem';
 import notebook_connections_config from '@site/static/images/integrations/data-ingestion/aws-glue/notebook-connections-config.png';
 import dependent_jars_path_option from '@site/static/images/integrations/data-ingestion/aws-glue/dependent_jars_path_option.png';
+import ClickHouseSupportedBadge from '@theme/badges/ClickHouseSupported';
 
 # Integrating Amazon Glue with ClickHouse and Spark
 
+<ClickHouseSupportedBadge/>
+
 [Amazon Glue](https://aws.amazon.com/glue/) is a fully managed, serverless data integration service provided by Amazon Web Services (AWS). It simplifies the process of discovering, preparing, and transforming data for analytics, machine learning, and application development.
 
 ## Installation {#installation}

docs/integrations/data-ingestion/azure-data-factory/overview.md

@@ -7,8 +7,12 @@ title: 'Bringing Azure Data into ClickHouse'
 doc_type: 'guide'
 ---
 
+import ClickHouseSupportedBadge from '@theme/badges/ClickHouseSupported';
+
 # Bringing Azure Data into ClickHouse
 
+<ClickHouseSupportedBadge/>
+
 Microsoft Azure offers a wide range of tools to store, transform, and analyze
 data. However, in many scenarios, ClickHouse can provide significantly better
 performance for low-latency querying and processing of huge datasets. In

docs/integrations/data-ingestion/azure-data-factory/using_azureblobstorage.md

@@ -8,7 +8,6 @@ doc_type: 'guide'
 ---
 
 import Image from '@theme/IdealImage';
-
 import azureDataStoreSettings                   from '@site/static/images/integrations/data-ingestion/azure-data-factory/azure-data-store-settings.png';
 import azureDataStoreAccessKeys                 from '@site/static/images/integrations/data-ingestion/azure-data-factory/azure-data-store-access-keys.png';
 

docs/integrations/data-ingestion/azure-synapse/index.md

@@ -11,9 +11,12 @@ import TOCInline from '@theme/TOCInline';
 import Image from '@theme/IdealImage';
 import sparkConfigViaNotebook from '@site/static/images/integrations/data-ingestion/azure-synapse/spark_notebook_conf.png';
 import sparkUICHSettings from '@site/static/images/integrations/data-ingestion/azure-synapse/spark_ui_ch_settings.png';
+import ClickHouseSupportedBadge from '@theme/badges/ClickHouseSupported';
 
 # Integrating Azure Synapse with ClickHouse
 
+<ClickHouseSupportedBadge/>
+
 [Azure Synapse](https://azure.microsoft.com/en-us/products/synapse-analytics) is an integrated analytics service that combines big data, data science and warehousing to enable fast, large-scale data analysis.
 Within Synapse, Spark pools provide on-demand, scalable [Apache Spark](https://spark.apache.org) clusters that let users run complex data transformations, machine learning, and integrations with external systems.
 

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

@@ -18,11 +18,11 @@ 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';
+import PartnerBadge from '@theme/badges/PartnerBadge';
 
 # Connect Airbyte to ClickHouse
 
-<CommunityMaintainedBadge/>
+<PartnerBadge/>
 
 :::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/bladepipe-and-clickhouse.md

@@ -18,11 +18,11 @@ import bp_ck_6 from '@site/static/images/integrations/data-ingestion/etl-tools/b
 import bp_ck_7 from '@site/static/images/integrations/data-ingestion/etl-tools/bp_ck_7.png';
 import bp_ck_8 from '@site/static/images/integrations/data-ingestion/etl-tools/bp_ck_8.png';
 import bp_ck_9 from '@site/static/images/integrations/data-ingestion/etl-tools/bp_ck_9.png';
-import CommunityMaintainedBadge from '@theme/badges/CommunityMaintained';
+import PartnerBadge from '@theme/badges/PartnerBadge';
 
 # Connect BladePipe to ClickHouse
 
-<CommunityMaintainedBadge/>
+<PartnerBadge/>
 
 <a href="https://www.bladepipe.com/" target="_blank">BladePipe</a> is a real-time end-to-end data integration tool with sub-second latency, boosting seamless data flow across platforms. 
 

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

@@ -7,11 +7,11 @@ slug: /integrations/data-ingestion/etl-tools/dlt-and-clickhouse
 doc_type: 'guide'
 ---
 
-import CommunityMaintainedBadge from '@theme/badges/CommunityMaintained';
+import PartnerBadge from '@theme/badges/PartnerBadge';
 
 # Connect dlt to ClickHouse
 
-<CommunityMaintainedBadge/>
+<PartnerBadge/>
 
 <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.