Remove related content

Author: Blargian

docs/cloud/reference/shared-merge-tree.md

@@ -119,7 +119,3 @@ Most of the time, you should not be using `select_sequential_consistency` or `SY
 2. If you write to one replica and read from another, you can use `SYSTEM SYNC REPLICA LIGHTWEIGHT` to force the replica to fetch the metadata from ClickHouse-Keeper.
 
 3. Use `select_sequential_consistency` as a setting as part of your query.
-
-## Related Content {#related-content}
-
-- [ClickHouse Cloud boosts performance with SharedMergeTree and Lightweight Updates](https://clickhouse.com/blog/clickhouse-cloud-boosts-performance-with-sharedmergetree-and-lightweight-updates)

docs/faq/use-cases/time-series.md

@@ -17,7 +17,3 @@ First of all, there are **[specialized codecs](../../sql-reference/statements/cr
 Second, time-series queries often hit only recent data, like one day or one week old. It makes sense to use servers that have both fast NVMe/SSD drives and high-capacity HDD drives. ClickHouse [TTL](/engines/table-engines/mergetree-family/mergetree#table_engine-mergetree-ttl) feature allows to configure keeping fresh hot data on fast drives and gradually move it to slower drives as it ages. Rollup or removal of even older data is also possible if your requirements demand it.
 
 Even though it's against ClickHouse philosophy of storing and processing raw data, you can use [materialized views](../../sql-reference/statements/create/view.md) to fit into even tighter latency or costs requirements.
-
-## Related Content {#related-content}
-
-- Blog: [Working with time series data in ClickHouse](https://clickhouse.com/blog/working-with-time-series-data-and-functions-ClickHouse)

docs/guides/best-practices/sparse-primary-indexes.md

@@ -4,6 +4,7 @@ sidebar_position: 1
 description: 'In this guide we are going to do a deep dive into ClickHouse indexing.'
 title: 'A Practical Introduction to Primary Indexes in ClickHouse'
 slug: /guides/best-practices/sparse-primary-indexes
+show_related_blogs: true
 ---
 
 import sparsePrimaryIndexes01 from '@site/static/images/guides/best-practices/sparse-primary-indexes-01.png';
@@ -156,10 +157,6 @@ ClickHouse client's result output indicates that ClickHouse executed a full tabl
 
 To make this (way) more efficient and (much) faster, we need to use a table with a appropriate primary key. This will allow ClickHouse to automatically (based on the primary key's column(s)) create a sparse primary index which can then be used to significantly speed up the execution of our example query.
 
-### Related content {#related-content}
-- Blog: [Super charging your ClickHouse queries](https://clickhouse.com/blog/clickhouse-faster-queries-with-projections-and-primary-indexes)
-
-
 ## ClickHouse Index Design {#clickhouse-index-design}
 
 ### An index design for massive data scales {#an-index-design-for-massive-data-scales}
@@ -1473,11 +1470,6 @@ Therefore the `cl` values are most likely in random order and therefore have a b
 
 For both the efficient filtering on secondary key columns in queries and the compression ratio of a table's column data files it is beneficial to order the columns in a primary key by their cardinality in ascending order.
 
-
-### Related content {#related-content-1}
-- Blog: [Super charging your ClickHouse queries](https://clickhouse.com/blog/clickhouse-faster-queries-with-projections-and-primary-indexes)
-
-
 ## Identifying single rows efficiently {#identifying-single-rows-efficiently}
 
 Although in general it is [not](/knowledgebase/key-value) the best use case for ClickHouse,

docs/guides/developer/ttl.md

@@ -5,6 +5,7 @@ sidebar_position: 2
 keywords: ['ttl', 'time to live', 'clickhouse', 'old', 'data']
 description: 'TTL (time-to-live) refers to the capability of having rows or columns moved, deleted, or rolled up after a certain interval of time has passed.'
 title: 'Manage Data with TTL (Time-to-live)'
+show_related_blogs: true
 ---
 
 import CloudNotSupportedBadge from '@theme/badges/CloudNotSupportedBadge';
@@ -258,8 +259,3 @@ The response will look like:
 │ all_2_2_0   │ hot_disk  │
 └─────────────┴───────────┘
 ```
-
-
-## Related Content {#related-content}
-
-- Blog & Webinar: [Using TTL to Manage Data Lifecycles in ClickHouse](https://clickhouse.com/blog/using-ttl-to-manage-data-lifecycles-in-clickhouse)

docs/integrations/data-ingestion/data-formats/intro.md

@@ -5,6 +5,7 @@ sidebar_position: 1
 keywords: ['clickhouse', 'CSV', 'TSV', 'Parquet', 'clickhouse-client', 'clickhouse-local']
 title: 'Importing from various data formats to ClickHouse'
 description: 'Page describing how to import various data formats into ClickHouse'
+show_related_blogs: true
 ---
 
 # Importing from various data formats to ClickHouse
@@ -32,8 +33,3 @@ Handle common Apache formats such as Parquet and Arrow.
 Need a SQL dump to import into MySQL or Postgresql? Look no further.
 
 If you are looking to connect a BI tool like Grafana, Tableau and others, check out the [Visualize category](../../data-visualization/index.md) of the docs.
-
-
-## Related Content {#related-content}
-
-- Blog: [An Introduction to Data Formats in ClickHouse](https://clickhouse.com/blog/data-formats-clickhouse-csv-tsv-parquet-native)

docs/integrations/data-ingestion/dbms/postgresql/connecting-to-postgresql.md

@@ -3,6 +3,7 @@ slug: /integrations/postgresql/connecting-to-postgresql
 title: 'Connecting to PostgreSQL'
 keywords: ['clickhouse', 'postgres', 'postgresql', 'connect', 'integrate', 'table', 'engine']
 description: 'Page describing the various ways to connect PostgreSQL to ClickHouse'
+show_related_blogs: true
 ---
 
 import CloudNotSupportedBadge from '@theme/badges/CloudNotSupportedBadge';
@@ -341,8 +342,3 @@ This integration guide focused on a simple example on how to replicate a databas
 :::info
 For more features available for advanced options, please see the [reference documentation](/engines/database-engines/materialized-postgresql).
 :::
-
-
-## Related content {#related-content}
-- Blog: [ClickHouse and PostgreSQL - a match made in data heaven - part 1](https://clickhouse.com/blog/migrating-data-between-clickhouse-postgres)
-- Blog: [ClickHouse and PostgreSQL - a Match Made in Data Heaven - part 2](https://clickhouse.com/blog/migrating-data-between-clickhouse-postgres-part-2)

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

@@ -1101,7 +1101,3 @@ Additional configuration for the plugin is described [here](https://github.com/s
 ## Fivetran {#fivetran}
 
 The `dbt-clickhouse` connector is also available for use in [Fivetran transformations](https://fivetran.com/docs/transformations/dbt), allowing seamless integration and transformation capabilities directly within the Fivetran platform using `dbt`.
-
-## Related Content {#related-content}
-
-- Blog & Webinar: [ClickHouse and dbt - A Gift from the Community](https://clickhouse.com/blog/clickhouse-dbt-project-introduction-and-webinar)

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

@@ -4,6 +4,7 @@ sidebar_position: 220
 slug: /integrations/vector
 description: 'How to tail a log file into ClickHouse using Vector'
 title: 'Integrating Vector with ClickHouse'
+show_related_blogs: true
 ---
 
 import Image from '@theme/IdealImage';
@@ -185,9 +186,3 @@ Having the logs in ClickHouse is great, but storing each event as a single strin
 
 
 **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.
-
-## Related Content {#related-content}
-
-- Blog: [Building an Observability Solution with ClickHouse in 2023 - Part 1 - Logs](https://clickhouse.com/blog/storing-log-data-in-clickhouse-fluent-bit-vector-open-telemetry)
-- Blog: [Sending Nginx logs to ClickHouse with Fluent Bit ](https://clickhouse.com/blog/nginx-logs-to-clickhouse-fluent-bit)
-- Blog: [Sending Kubernetes logs To ClickHouse with Fluent Bit](https://clickhouse.com/blog/kubernetes-logs-to-clickhouse-fluent-bit)

docs/integrations/data-ingestion/insert-local-files.md

@@ -4,6 +4,7 @@ sidebar_position: 2
 title: 'Insert Local Files'
 slug: /integrations/data-ingestion/insert-local-files
 description: 'Learn about Insert Local Files'
+show_related_blogs: true
 ---
 
 # Insert Local Files
@@ -114,8 +115,3 @@ cat comments.tsv | clickhouse-client \
 ```
 
 Visit the [docs page on `clickhouse-client`](/interfaces/cli) for details on how to install `clickhouse-client` on your local operating system.
-
-## Related Content {#related-content}
-
-- Blog: [Getting Data Into ClickHouse - Part 1](https://clickhouse.com/blog/getting-data-into-clickhouse-part-1)
-- Blog: [Exploring massive, real-world data sets: 100+ Years of Weather Records in ClickHouse](https://clickhouse.com/blog/real-world-data-noaa-climate-data)

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

@@ -31,8 +31,6 @@ import Image from '@theme/IdealImage';
   </iframe>
 </div>
 
-- Blog: [Optimizing Analytical Workloads: Comparing Redshift vs ClickHouse](https://clickhouse.com/blog/redshift-vs-clickhouse-comparison)
-
 ## Introduction {#introduction}
 
 [Amazon Redshift](https://aws.amazon.com/redshift/) is a popular cloud data warehousing solution that is part of the Amazon Web Services offerings. This guide presents different approaches to migrating data from a Redshift instance to ClickHouse. We will cover three options: