Fix header casing

Author: Blargian

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

@@ -347,7 +347,7 @@ reading in parallel from multiple workers.
 Please visit Apache Spark's official documentation for more information
 on [JDBC configurations](https://spark.apache.org/docs/latest/sql-data-sources-jdbc.html#data-source-option).
 
-## JDBC Limitations {#jdbc-limitations}
+## JDBC limitations {#jdbc-limitations}
 
 * As of today, you can insert data using JDBC only into existing tables (currently there is no way to auto create the
   table on DF insertion, as Spark does with other connectors).

docs/integrations/data-ingestion/apache-spark/spark-native-connector.md

@@ -11,7 +11,7 @@ import Tabs from '@theme/Tabs';
 import TabItem from '@theme/TabItem';
 import TOCInline from '@theme/TOCInline';
 
-# Spark Connector
+# Spark connector
 
 This connector leverages ClickHouse-specific optimizations, such as advanced partitioning and predicate pushdown, to
 improve query performance and data handling.
@@ -35,7 +35,7 @@ catalog feature, it is now possible to add and work with multiple catalogs in a
 - Scala 2.12 or 2.13
 - Apache Spark 3.3 or 3.4 or 3.5
 
-## Compatibility Matrix {#compatibility-matrix}
+## Compatibility matrix {#compatibility-matrix}
 
 | Version | Compatible Spark Versions | ClickHouse JDBC version |
 |---------|---------------------------|-------------------------|
@@ -50,7 +50,7 @@ catalog feature, it is now possible to add and work with multiple catalogs in a
 | 0.2.1   | Spark 3.2                 | Not depend on           |
 | 0.1.2   | Spark 3.2                 | Not depend on           |
 
-## Installation & Setup {#installation--setup}
+## Installation & setup {#installation--setup}
 
 For integrating ClickHouse with Spark, there are multiple installation options to suit different project setups.
 You can add the ClickHouse Spark connector as a dependency directly in your project's build file (such as in `pom.xml`
@@ -146,7 +146,7 @@ for production.
 </TabItem>
 </Tabs>
 
-### Download The Library {#download-the-library}
+### Download the library {#download-the-library}
 
 The name pattern of the binary JAR is:
 
@@ -172,7 +172,7 @@ In any case, ensure that the package versions are compatible according to
 the [Compatibility Matrix](#compatibility-matrix).
 :::
 
-## Register The Catalog (required) {#register-the-catalog-required}
+## Register the catalog (required) {#register-the-catalog-required}
 
 In order to access your ClickHouse tables, you must configure a new Spark catalog with the following configs:
 
@@ -222,7 +222,7 @@ That way, you would be able to access clickhouse1 table `<ck_db>.<ck_table>` fro
 
 :::
 
-## ClickHouse Cloud Settings {#clickhouse-cloud-settings}
+## ClickHouse Cloud settings {#clickhouse-cloud-settings}
 
 When connecting to [ClickHouse Cloud](https://clickhouse.com), make sure to enable SSL and set the appropriate SSL mode. For example:
 
@@ -231,7 +231,7 @@ spark.sql.catalog.clickhouse.option.ssl        true
 spark.sql.catalog.clickhouse.option.ssl_mode   NONE
 ```
 
-## Read Data {#read-data}
+## Read data {#read-data}
 
 <Tabs groupId="spark_apis">
 <TabItem value="Java" label="Java" default>
@@ -338,7 +338,7 @@ df.show()
 </TabItem>
 </Tabs>
 
-## Write Data {#write-data}
+## Write data {#write-data}
 
 <Tabs groupId="spark_apis">
 <TabItem value="Java" label="Java" default>
@@ -472,7 +472,7 @@ df.writeTo("clickhouse.default.example_table").append()
 </TabItem>
 </Tabs>
 
-## DDL Operations {#ddl-operations}
+## DDL operations {#ddl-operations}
 
 You can perform DDL operations on your ClickHouse instance using Spark SQL, with all changes immediately persisted in
 ClickHouse.
@@ -530,7 +530,7 @@ The following are the adjustable configurations available in the connector:
 | spark.clickhouse.write.retryInterval               | 10s                                                    | The interval in seconds between write retry.                                                                                                                                                                                                                                                                                                                                                                    | 0.1.0 |
 | spark.clickhouse.write.retryableErrorCodes         | 241                                                    | The retryable error codes returned by ClickHouse server when write failing.                                                                                                                                                                                                                                                                                                                                     | 0.1.0 |
 
-## Supported Data Types {#supported-data-types}
+## Supported data types {#supported-data-types}
 
 This section outlines the mapping of data types between Spark and ClickHouse. The tables below provide quick references
 for converting data types when reading from ClickHouse into Spark and when inserting data from Spark into ClickHouse.
@@ -596,7 +596,7 @@ for converting data types when reading from ClickHouse into Spark and when inser
 | `Object`                            |                      | ❌         |              |                                        |
 | `Nested`                            |                      | ❌         |              |                                        |
 
-## Contributing and Support {#contributing-and-support}
+## Contributing and support {#contributing-and-support}
 
 If you'd like to contribute to the project or report any issues, we welcome your input!
 Visit our [GitHub repository](https://github.com/ClickHouse/spark-clickhouse-connector) to open an issue, suggest

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

@@ -78,7 +78,7 @@ SELECT * FROM azureBlobStorage(
 This allows you to efficiently pull external data into ClickHouse without
 needing intermediate ETL steps.
 
-## A simple example using the Environmental Sensors Dataset {#simple-example-using-the-environmental-sensors-dataset}
+## A simple example using the Environmental sensors dataset {#simple-example-using-the-environmental-sensors-dataset}
 
 As an example we will download a single file from the Environmental Sensors
 Dataset.
@@ -152,7 +152,7 @@ inference from input data](https://clickhouse.com/docs/interfaces/schema-inferen
 Your sensors table is now populated with data from the `2019-06_bmp180.csv.zst`
 file stored in Azure Blob Storage.
 
-## Additional Resources {#additional-resources}
+## Additional resources {#additional-resources}
 
 This is just a basic introduction to using the azureBlobStorage function. For
 more advanced options and configuration details, please refer to the official

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

@@ -34,7 +34,7 @@ import adfCopyDataSource                        from '@site/static/images/integr
 import adfCopyDataSinkSelectPost                from '@site/static/images/integrations/data-ingestion/azure-data-factory/adf-copy-data-sink-select-post.png';
 import adfCopyDataDebugSuccess                  from '@site/static/images/integrations/data-ingestion/azure-data-factory/adf-copy-data-debug-success.png';
 
-# Using ClickHouse HTTP Interface in Azure Data Factory {#using-clickhouse-http-interface-in-azure-data-factory}
+# Using ClickHouse HTTP interface in Azure data factory {#using-clickhouse-http-interface-in-azure-data-factory}
 
 The [`azureBlobStorage` Table Function](https://clickhouse.com/docs/sql-reference/table-functions/azureBlobStorage)
 is a fast and convenient way to ingest data from Azure Blob Storage into
@@ -118,7 +118,7 @@ Service to your ClickHouse instance, define a Dataset for the
 [REST sink](https://learn.microsoft.com/en-us/azure/data-factory/connector-rest),
 and create a Copy Data activity to send data from Azure to ClickHouse.
 
-## Creating an Azure Data Factory instance {#create-an-azure-data-factory-instance}
+## Creating an Azure data factory instance {#create-an-azure-data-factory-instance}
 
 This guide assumes that you have access to Microsoft Azure account, and you
 already have configured a subscription and a resource group. If you have
@@ -321,7 +321,7 @@ Now that we've configured both the input and output datasets, we can set up a
 
 6. Once complete, click **Publish all** to save your pipeline and dataset changes.
 
-## Additional Resources {#additional-resources-1}
+## Additional resources {#additional-resources-1}
 - [HTTP Interface](https://clickhouse.com/docs/interfaces/http)
 - [Copy and transform data from and to a REST endpoint by using Azure Data Factory](https://learn.microsoft.com/en-us/azure/data-factory/connector-rest?tabs=data-factory)
 - [Selecting an Insert Strategy](https://clickhouse.com/docs/best-practices/selecting-an-insert-strategy)

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

@@ -72,15 +72,15 @@ Please visit the [ClickHouse Spark configurations page](/integrations/apache-spa
 When working with ClickHouse Cloud Please make sure to set the [required Spark settings](/integrations/apache-spark/spark-native-connector#clickhouse-cloud-settings).  
 :::
 
-## Setup Verification {#setup-verification}
+## Setup verification {#setup-verification}
 
 To verify that the dependencies and configurations were set successfully, please visit your session's Spark UI, and go to your `Environment` tab.
 There, look for your ClickHouse related settings:
 
 <Image img={sparkUICHSettings} size="xl" alt="Verifying ClickHouse settings using Spark UI" border/>
 
 
-## Additional Resources {#additional-resources}
+## Additional resources {#additional-resources}
 
 - [ClickHouse Spark Connector Docs](/integrations/apache-spark)
 - [Azure Synapse Spark Pools Overview](https://learn.microsoft.com/en-us/azure/synapse-analytics/spark/apache-spark-overview)

docs/integrations/data-ingestion/clickpipes/index.md

@@ -80,7 +80,7 @@ Steps:
 ClickPipes will create a table next to your destination table with the postfix `<destination_table_name>_clickpipes_error`. This table will contain any errors from the operations of your ClickPipe (network, connectivity, etc.) and also any data that don't conform to the schema. The error table has a [TTL](/engines/table-engines/mergetree-family/mergetree#table_engine-mergetree-ttl) of 7 days.
 If ClickPipes cannot connect to a data source or destination after 15min., ClickPipes instance stops and  stores an appropriate message in the error table (providing the ClickHouse instance is available).
 
-## F.A.Q {#faq}
+## FAQ {#faq}
 - **What is ClickPipes?**
 
   ClickPipes is a ClickHouse Cloud feature that makes it easy for users to connect their ClickHouse services to external data sources, specifically Kafka. With ClickPipes for Kafka, users can easily continuously load data into ClickHouse, making it available for real-time analytics.

docs/integrations/data-ingestion/clickpipes/kafka.md

@@ -165,7 +165,7 @@ ClickPipes support the JSON type in the following circumstances:
 Note that you will have to manually change the destination column to the desired JSON type, including any fixed or skipped paths.
 
 ### Avro {#avro}
-#### Supported Avro Data Types {#supported-avro-data-types}
+#### Supported Avro data types {#supported-avro-data-types}
 
 ClickPipes supports all Avro Primitive and Complex types, and all Avro Logical types except `time-millis`, `time-micros`, `local-timestamp-millis`, `local_timestamp-micros`, and `duration`.  Avro `record` types are converted to Tuple, `array` types to Array, and `map` to Map (string keys only).  In general the conversions listed [here](/interfaces/formats/Avro#data-types-matching) are available.  We recommend using exact type matching for Avro numeric types, as ClickPipes does not check for overflow or precision loss on type conversion.
 
@@ -210,7 +210,7 @@ view), it may improve ClickPipes performance to delete all the "non-virtual" col
 
 ## Best practices {#best-practices}
 
-### Message Compression {#compression}
+### Message compression {#compression}
 We strongly recommend using compression for your Kafka topics. Compression can result in a significant saving in data transfer costs with virtually no performance hit.
 To learn more about message compression in Kafka, we recommend starting with this [guide](https://www.confluent.io/blog/apache-kafka-message-compression/).
 
@@ -300,7 +300,7 @@ Role-based access only works for ClickHouse Cloud instances deployed to AWS.
 ```
 
 
-### Custom Certificates {#custom-certificates}
+### Custom certificates {#custom-certificates}
 ClickPipes for Kafka supports the upload of custom certificates for Kafka brokers with SASL & public SSL/TLS certificate. You can upload your certificate in the SSL Certificate section of the ClickPipe setup.
 :::note
 Please note that while we support uploading a single SSL certificate along with SASL for Kafka, SSL with Mutual TLS (mTLS) is not supported at this time.
@@ -333,7 +333,7 @@ Regardless number of running consumers, fault tolerance is available by design.
 If a consumer or its underlying infrastructure fails,
 the ClickPipe will automatically restart the consumer and continue processing messages.
 
-## F.A.Q {#faq}
+## FAQ {#faq}
 
 ### General {#general}
 

docs/integrations/data-ingestion/clickpipes/kinesis.md

@@ -84,12 +84,12 @@ You have familiarized yourself with the [ClickPipes intro](./index.md) and setup
 10. **Congratulations!** you have successfully set up your first ClickPipe. If this is a streaming ClickPipe it will be continuously running, ingesting data in real-time from your remote data source. Otherwise it will ingest the batch and complete.
 
 
-## Supported Data Formats {#supported-data-formats}
+## Supported data formats {#supported-data-formats}
 
 The supported formats are:
 - [JSON](../../../interfaces/formats.md/#json)
 
-## Supported Data Types {#supported-data-types}
+## Supported data types {#supported-data-types}
 
 ### Standard types support {#standard-types-support}
 The following ClickHouse data types are currently supported in ClickPipes:
@@ -125,7 +125,7 @@ have to submit a support ticket to enable it on your service.
 JSON fields that are always a JSON object can be assigned to a JSON destination column.  You will have to manually change the destination
 column to the desired JSON type, including any fixed or skipped paths. 
 
-## Kinesis Virtual Columns {#kinesis-virtual-columns}
+## Kinesis virtual columns {#kinesis-virtual-columns}
 
 The following virtual columns are supported for Kinesis stream.  When creating a new destination table virtual columns can be added by using the `Add Column` button.
 

docs/integrations/data-ingestion/clickpipes/mysql/index.md

@@ -74,7 +74,7 @@ Make sure you are logged in to your ClickHouse Cloud account. If you don't have
 
    <Image img={mysql_connection_details} alt="Fill in connection details" size="lg" border/>
 
-#### (Optional) Set up SSH Tunneling {#optional-setting-up-ssh-tunneling}
+#### (Optional) Set up SSH tunneling {#optional-setting-up-ssh-tunneling}
 
 You can specify SSH tunneling details if your source MySQL database is not publicly accessible.
 

docs/integrations/data-ingestion/clickpipes/mysql/source/aurora.md

@@ -73,7 +73,7 @@ Then click on `Save Changes` in the top-right. You may need to reboot your insta
 If you have a MySQL cluster, the above parameters would be found in a [DB Cluster](https://docs.aws.amazon.com/AmazonRDS/latest/AuroraUserGuide/USER_WorkingWithParamGroups.CreatingCluster.html) parameter group and not the DB instance group.
 :::
 
-## Enabling GTID Mode {#gtid-mode-aurora}
+## Enabling GTID mode {#gtid-mode-aurora}
 Global Transaction Identifiers (GTIDs) are unique IDs assigned to each committed transaction in MySQL. They simplify binlog replication and make troubleshooting more straightforward.
 
 If your MySQL instance is MySQL 5.7, 8.0 or 8.4, we recommend enabling GTID mode so that the MySQL ClickPipe can use GTID replication.