various minor improvements to integrations docs

Author: Blargian

docs/_snippets/_gather_your_details_http.mdx

@@ -4,17 +4,18 @@ import Image from '@theme/IdealImage';
 
 To connect to ClickHouse with HTTP(S) you need this information:
 
-- The HOST and PORT: typically, the port is 8443 when using TLS or 8123 when not using TLS.
+| Parameter(s)            | Description                                                                                                   |
+|-------------------------|---------------------------------------------------------------------------------------------------------------|
+|`HOST` and `PORT`        | Typically, the port is 8443 when using TLS or 8123 when not using TLS.                                        |
+|`DATABASE NAME`          | Out of the box, there is a database named `default`, use the name of the database that you want to connect to.|
+|`USERNAME` and `PASSWORD`| Out of the box, the username is `default`. Use the username appropriate for your use case.                    |
 
-- The DATABASE NAME: out of the box, there is a database named `default`, use the name of the database that you want to connect to.
-
-- The USERNAME and PASSWORD: out of the box, the username is `default`. Use the username appropriate for your use case.
-
-The details for your ClickHouse Cloud service are available in the ClickHouse Cloud console.  Select the service that you will connect to and click **Connect**:
+The details for your ClickHouse Cloud service are available in the ClickHouse Cloud console.
+Select a service and click **Connect**:
 
 <Image img={cloud_connect_button} size="md" alt="ClickHouse Cloud service connect button" border />
 
-Choose **HTTPS**, and the details are available in an example `curl` command.
+Choose **HTTPS**. Connection details are displayed in an example `curl` command.
 
 <Image img={connection_details_https} size="md" alt="ClickHouse Cloud HTTPS connection details" border/>
 

docs/_snippets/_gather_your_details_native.md

@@ -4,13 +4,14 @@ import Image from '@theme/IdealImage';
 
 To connect to ClickHouse with native TCP you need this information:
 
-- The HOST and PORT: typically, the port is 9440 when using TLS, or 9000 when not using TLS.
-
-- The DATABASE NAME: out of the box there is a database named `default`, use the name of the database that you want to connect to.
-
-- The USERNAME and PASSWORD: out of the box the username is `default`. Use the username appropriate for your use case.
-
-The details for your ClickHouse Cloud service are available in the ClickHouse Cloud console.  Select the service that you will connect to and click **Connect**:
+| Parameter(s)              | Description                                                                                                   |
+|---------------------------|---------------------------------------------------------------------------------------------------------------|
+| `HOST` and `PORT`         | Typically, the port is 9440 when using TLS, or 9000 when not using TLS.                                       |
+| `DATABASE NAME`           | Out of the box there is a database named `default`, use the name of the database that you want to connect to. |
+| `USERNAME` and `PASSWORD` | Out of the box the username is `default`. Use the username appropriate for your use case.                     |
+
+The details for your ClickHouse Cloud service are available in the ClickHouse Cloud console.
+Select the service that you will connect to and click **Connect**:
 
 <Image img={cloud_connect_button} size="md" alt="ClickHouse Cloud service connect button" border/>
 

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

@@ -30,7 +30,9 @@ Please note that the Airbyte source and destination for ClickHouse are currently
 
 <a href="https://www.airbyte.com/" target="_blank">Airbyte</a> is an open-source data integration platform. It allows the creation of <a href="https://airbyte.com/blog/why-the-future-of-etl-is-not-elt-but-el" target="_blank">ELT</a> data pipelines and is shipped with more than 140 out-of-the-box connectors. This step-by-step tutorial shows how to connect Airbyte to ClickHouse as a destination and load a sample dataset.
 
-## 1. Download and run Airbyte {#1-download-and-run-airbyte}
+<VerticalStepper headerLevel="h2">
+
+## Download and run Airbyte {#1-download-and-run-airbyte}
 
 1. Airbyte runs on Docker and uses `docker-compose`. Make sure to download and install the latest versions of Docker.
 
@@ -50,7 +52,7 @@ Please note that the Airbyte source and destination for ClickHouse are currently
         Alternatively, you can signup and use <a href="https://docs.airbyte.com/deploying-airbyte/on-cloud" target="_blank">Airbyte Cloud</a>
         :::
 
-## 2. Add ClickHouse as a destination {#2-add-clickhouse-as-a-destination}
+## Add ClickHouse as a destination {#2-add-clickhouse-as-a-destination}
 
 In this section, we will display how to add a ClickHouse instance as a destination.
 
@@ -80,7 +82,7 @@ GRANT CREATE ON * TO my_airbyte_user;
 ```
 :::
 
-## 3. Add a dataset as a source {#3-add-a-dataset-as-a-source}
+## Add a dataset as a source {#3-add-a-dataset-as-a-source}
 
 The example dataset we will use is the <a href="https://clickhouse.com/docs/getting-started/example-datasets/nyc-taxi/" target="_blank">New York City Taxi Data</a> (on <a href="https://github.com/toddwschneider/nyc-taxi-data" target="_blank">Github</a>). For this tutorial, we will use a subset of this dataset which corresponds to the month of Jan 2022.
 
@@ -98,7 +100,7 @@ The example dataset we will use is the <a href="https://clickhouse.com/docs/gett
 
 3. Congratulations! You have now added a source file in Airbyte.
 
-## 4. Create a connection and load the dataset into ClickHouse {#4-create-a-connection-and-load-the-dataset-into-clickhouse}
+## Create a connection and load the dataset into ClickHouse {#4-create-a-connection-and-load-the-dataset-into-clickhouse}
 
 1. Within Airbyte, select the "Connections" page and add a new connection
 
@@ -170,3 +172,5 @@ The example dataset we will use is the <a href="https://clickhouse.com/docs/gett
         Now that the dataset is loaded on your ClickHouse instance, you can create an new table and use more suitable ClickHouse data types (<a href="https://clickhouse.com/docs/getting-started/example-datasets/nyc-taxi/" target="_blank">more details</a>).
 
 8. Congratulations - you have successfully loaded the NYC taxi data into ClickHouse using Airbyte!
+
+</VerticalStepper>

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

@@ -24,7 +24,9 @@ pip install "dlt[clickhouse]"
 
 ## Setup guide {#setup-guide}
 
-### 1. Initialize the dlt Project {#1-initialize-the-dlt-project}
+<VerticalStepper headerLevel="h3">
+
+### Initialize the dlt Project {#1-initialize-the-dlt-project}
 
 Start by initializing a new `dlt` project as follows:
 ```bash
@@ -42,7 +44,7 @@ pip install -r requirements.txt
 
 or with `pip install dlt[clickhouse]`, which installs the `dlt` library and the necessary dependencies for working with ClickHouse as a destination.
 
-### 2. Setup ClickHouse Database {#2-setup-clickhouse-database}
+### Setup ClickHouse Database {#2-setup-clickhouse-database}
 
 To load data into ClickHouse, you need to create a ClickHouse database. Here's a rough outline of what should you do:
 
@@ -60,7 +62,7 @@ GRANT SELECT ON INFORMATION_SCHEMA.COLUMNS TO dlt;
 GRANT CREATE TEMPORARY TABLE, S3 ON *.* TO dlt;
 ```
 
-### 3. Add credentials {#3-add-credentials}
+### Add credentials {#3-add-credentials}
 
 Next, set up the ClickHouse credentials in the `.dlt/secrets.toml` file as shown below:
 
@@ -78,8 +80,7 @@ secure = 1                               # Set to 1 if using HTTPS, else 0.
 dataset_table_separator = "___"          # Separator for dataset table names from dataset.
 ```
 
-:::note
-HTTP_PORT
+:::note HTTP_PORT
 The `http_port` parameter specifies the port number to use when connecting to the ClickHouse server's HTTP interface. This is different from default port 9000, which is used for the native TCP protocol.
 
 You must set `http_port` if you are not using external staging (i.e. you don't set the staging parameter in your pipeline). This is because the built-in ClickHouse local storage staging uses the <a href="https://github.com/ClickHouse/clickhouse-connect">clickhouse content</a> library, which communicates with ClickHouse over HTTP.
@@ -94,6 +95,8 @@ You can pass a database connection string similar to the one used by the `clickh
 destination.clickhouse.credentials="clickhouse://dlt:Dlt*12345789234567@localhost:9000/dlt?secure=1"
 ```
 
+</VerticalStepper>
+
 ## Write disposition {#write-disposition}
 
 All [write dispositions](https://dlthub.com/docs/general-usage/incremental-loading#choosing-a-write-disposition)

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

@@ -33,20 +33,23 @@ import CommunityMaintainedBadge from '@theme/badges/CommunityMaintained';
 
 <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}
+<VerticalStepper headerLevel="h2">
+
+## Gather your connection details {#1-gather-your-connection-details}
+
 <ConnectionDetails />
 
-## 2. Download and run Apache NiFi {#2-download-and-run-apache-nifi}
+## Download and run Apache NiFi {#2-download-and-run-apache-nifi}
 
-1. For a new setup, download the binary from https://nifi.apache.org/download.html and start by running `./bin/nifi.sh start`
+For a new setup, download the binary from https://nifi.apache.org/download.html and start by running `./bin/nifi.sh start`
 
-## 3. Download the ClickHouse JDBC driver {#3-download-the-clickhouse-jdbc-driver}
+## Download the ClickHouse JDBC driver {#3-download-the-clickhouse-jdbc-driver}
 
 1. Visit the <a href="https://github.com/ClickHouse/clickhouse-java/releases" target="_blank">ClickHouse JDBC driver release page</a> on GitHub and look for  the latest JDBC release version
 2. In the release version, click on "Show all xx assets" and look for the JAR file containing the keyword "shaded" or "all", for example, `clickhouse-jdbc-0.5.0-all.jar`
 3. Place the JAR file in a folder accessible by Apache NiFi and take note of the absolute path
 
-## 4. Add `DBCPConnectionPool` Controller Service and configure its properties {#4-add-dbcpconnectionpool-controller-service-and-configure-its-properties}
+## Add `DBCPConnectionPool` Controller Service and configure its properties {#4-add-dbcpconnectionpool-controller-service-and-configure-its-properties}
 
 1. To configure a Controller Service in Apache NiFi, visit the NiFi Flow Configuration page by clicking on the "gear" button
 
@@ -90,7 +93,7 @@ import CommunityMaintainedBadge from '@theme/badges/CommunityMaintained';
 
     <Image img={nifi08} size="lg" border alt="Controller Services list showing enabled ClickHouse JDBC service" />
 
-## 5. Read from a table using the `ExecuteSQL` processor {#5-read-from-a-table-using-the-executesql-processor}
+## Read from a table using the `ExecuteSQL` processor {#5-read-from-a-table-using-the-executesql-processor}
 
 1. Add an ​`​ExecuteSQL` processor, along with the appropriate upstream and downstream processors
 
@@ -115,7 +118,7 @@ import CommunityMaintainedBadge from '@theme/badges/CommunityMaintained';
 
     <Image img={nifi12} size="lg" border alt="FlowFile content viewer showing query results in formatted view" />
 
-## 6. Write to a table using `MergeRecord` and `PutDatabaseRecord` processor {#6-write-to-a-table-using-mergerecord-and-putdatabaserecord-processor}
+## Write to a table using `MergeRecord` and `PutDatabaseRecord` processor {#6-write-to-a-table-using-mergerecord-and-putdatabaserecord-processor}
 
 1. To write multiple rows in a single insert, we first need to merge multiple records into a single record. This can be done using the `MergeRecord` processor
 
@@ -153,3 +156,5 @@ import CommunityMaintainedBadge from '@theme/badges/CommunityMaintained';
     <Image img={nifi15} size="sm" border alt="Query results showing row count in the destination table" />
 
 5. Congratulations - you have successfully loaded your data into ClickHouse using Apache NiFi !
+
+</VerticalStepper>

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

@@ -17,46 +17,56 @@ import PartnerBadge from '@theme/badges/PartnerBadge';
 
 <PartnerBadge/>
 
-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.
+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 [Uber's experience](https://eng.uber.com/logging/) 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).
+This guide shows you how to use the popular data pipeline [Vector](https://vector.dev/docs/about/what-is-vector/) 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).
 
-## 1. Create a database and table {#1-create-a-database-and-table}
+<VerticalStepper headerLevel="h2">
 
-Let's define a table to store the log events:
+## Create a database and table {#1-create-a-database-and-table}
 
-1. We will start with a new database named `nginxdb`:
-    ```sql
-    CREATE DATABASE IF NOT EXISTS nginxdb
-    ```
+Define a table to store the log events:
 
-2. For starters, we are just going to insert the entire log event as a single string. Obviously this is not a great format for performing analytics on the log data, but we will figure that part out below using ***materialized views***.
-    ```sql
-    CREATE TABLE IF NOT EXISTS  nginxdb.access_logs (
-        message String
-    )
-    ENGINE = MergeTree()
-    ORDER BY tuple()
-    ```
-    :::note
-    There is not really a need for a primary key yet, so that is why **ORDER BY** is set to **tuple()**.
-    :::
+1. Begin with a new database named `nginxdb`:
 
-## 2.  Configure Nginx {#2--configure-nginx}
+```sql
+CREATE DATABASE IF NOT EXISTS nginxdb
+```
+
+2. Insert the entire log event as a single string. Obviously this is not a great format for performing analytics on the log data, but we will figure that part out below using ***materialized views***.
+
+```sql
+CREATE TABLE IF NOT EXISTS  nginxdb.access_logs (
+  message String
+)
+ENGINE = MergeTree()
+ORDER BY tuple()
+```
+
+ :::note
+ **ORDER BY** is set to **tuple()** (an empty tuple) as there is no need for a primary key yet.
+ :::
+
+## Configure Nginx {#2--configure-nginx}
 
 We certainly do not want to spend too much time explaining Nginx, but we also do not want to hide all the details, so in this step we will provide you with enough details to get Nginx logging configured.
 
 1. The following `access_log` property sends logs to `/var/log/nginx/my_access.log` in the **combined** format. This value goes in the `http` section of your `nginx.conf` file:
-    ```bash
-    http {
-        include       /etc/nginx/mime.types;
-        default_type  application/octet-stream;
-        access_log  /var/log/nginx/my_access.log combined;
-        sendfile        on;
-        keepalive_timeout  65;
-        include /etc/nginx/conf.d/*.conf;
-    }
-    ```
+    
+```bash
+http {
+  include       /etc/nginx/mime.types;
+  default_type  application/octet-stream;
+  access_log  /var/log/nginx/my_access.log combined;
+  sendfile        on;
+  keepalive_timeout  65;
+  include /etc/nginx/conf.d/*.conf;
+}
+```
 
 2. Be sure to restart Nginx if you had to modify `nginx.conf`.
 
@@ -67,7 +77,7 @@ We certainly do not want to spend too much time explaining Nginx, but we also do
     192.168.208.1 - - [12/Oct/2021:03:31:49 +0000] "GET / HTTP/1.1" 304 0 "-" "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/93.0.4577.63 Safari/537.36"
     ```
 
-## 3. Configure Vector {#3-configure-vector}
+## Configure Vector {#3-configure-vector}
 
 Vector collects, transforms and routes logs, metrics, and traces (referred to as **sources**) to lots of different vendors (referred to as **sinks**), including out-of-the-box compatibility with ClickHouse. Sources and sinks are defined in a configuration file named **vector.toml**.
 
@@ -95,7 +105,7 @@ Vector collects, transforms and routes logs, metrics, and traces (referred to as
     ```
     <Image img={vector01} size="lg" border alt="View ClickHouse logs in table format" />
 
-## 4. Parse the Logs {#4-parse-the-logs}
+## Parse the Logs {#4-parse-the-logs}
 
 Having the logs in ClickHouse is great, but storing each event as a single string does not allow for much data analysis. Let's see how to parse the log events using a materialized view.
 
@@ -180,4 +190,6 @@ Having the logs in ClickHouse is great, but storing each event as a single strin
     The lesson above stored the data in two tables, but you could change the initial `nginxdb.access_logs` table to use the **Null** table engine - the parsed data will still end up in the `nginxdb.access_logs_view` table, but the raw data will not be stored in a table.
     :::
 
-**Summary:** By using Vector, which only required a simple install and quick configuration, we can send logs from an Nginx server to a table in ClickHouse. By using a clever materialized view, we can parse those logs into columns for easier analytics.
+</VerticalStepper>
+
+> By using Vector, which only requires a simple install and quick configuration, you can send logs from an Nginx server to a table in ClickHouse. By using a materialized view, you can parse those logs into columns for easier analytics.

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

@@ -15,18 +15,21 @@ import ClickHouseSupportedBadge from '@theme/badges/ClickHouseSupported';
 
 [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):
+There are two main ways to use Google Dataflow with ClickHouse, both of which leverage [`ClickHouseIO Apache Beam connector`](/integrations/apache-beam).
+These are:
+- [Java runner](#1-java-runner)
+- [Predefined templates](#2-predefined-templates)
 
-## 1. Java runner {#1-java-runner}
-The [Java Runner](./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.
+## Java runner {#1-java-runner}
+The [Java runner](./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 {#key-features}
 - High degree of customization.
 - Ideal for complex or advanced use cases.
 - Requires coding and understanding of the Beam API.
 
-## 2. Predefined templates {#2-predefined-templates}
+## Predefined templates {#2-predefined-templates}
 ClickHouse offers [predefined templates](./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 {#key-features-1}