ClickHouse · kurnoolsaketh · Mar 13, 2026 · Feb 14, 2026 · Feb 14, 2026 · Feb 14, 2026
@@ -19,7 +19,7 @@ In general, denormalize when:
 - Tables change infrequently or when batch refreshes are acceptable.
 - Relationships aren't many-to-many or not excessively high in cardinality.
 - Only a limited subset of the columns will be queried, i.e. certain columns can be excluded from denormalization.
-- You have the capability to shift processing out of ClickHouse into upstream systems like Flink, where real-time enrichment or flattening can be managed.
+- You have the capability to shift processing out of ClickHouse into upstream systems like [Flink](/integrations/data-ingestion/apache-flink/flink-connector.md), where real-time enrichment or flattening can be managed.
 
 Not all data needs to be denormalized — focus on the attributes that are frequently queried. Also consider [materialized views](/best-practices/use-materialized-views) to incrementally compute aggregates instead of duplicating entire sub-tables. When schema updates are rare and latency is critical, denormalization offers the best performance trade-off.
 

@@ -37,7 +37,7 @@ In general, we would recommend denormalizing in the following cases:
 
 All information doesn't need to be denormalized - just the key information that needs to be frequently accessed.
 
-The denormalization work can be handled in either ClickHouse or upstream e.g. using Apache Flink.
+The denormalization work can be handled in either ClickHouse or upstream e.g. using [Apache Flink](/integrations/data-ingestion/apache-flink/flink-connector.md).
 
 ## Avoid denormalization on frequently updated data {#avoid-denormalization-on-frequently-updated-data}
 
@@ -371,4 +371,4 @@ Users have several options for orchestrating this in ClickHouse, assuming a peri
 
 ### Streaming {#streaming}
 
-You may alternatively wish to perform this outside of ClickHouse, prior to insertion, using streaming technologies such as [Apache Flink](https://flink.apache.org/). Alternatively, incremental [materialized views](/guides/developer/cascading-materialized-views) can be used to perform this process as data is inserted.
+You may alternatively wish to perform this outside of ClickHouse, prior to insertion, using streaming technologies such as [Apache Flink](/integrations/data-ingestion/apache-flink/flink-connector.md). Alternatively, incremental [materialized views](/guides/developer/cascading-materialized-views) can be used to perform this process as data is inserted.
@@ -15,16 +15,16 @@
 |------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
 | [Airbyte](/integrations/airbyte)                                 | An open-source data integration platform. It allows the creation of ELT data pipelines and is shipped with more than 140 out-of-the-box connectors.                                                                                   |
 | [Apache Spark](/integrations/apache-spark)                       | A multi-language engine for executing data engineering, data science, and machine learning on single-node machines or clusters                                                                                                        |
-| [Apache Flink](https://github.com/ClickHouse/flink-connector-clickhouse)                       | Real-time data ingestion and processing into ClickHouse through Flink's DataStream API with support for batch writes                                                                                                        |
+| [Apache Flink](/integrations/apache-flink)                       | Real-time data ingestion and processing into ClickHouse through Flink's DataStream API with support for batch writes                                                                                                                  |
 | [Amazon Glue](/integrations/glue)                                | A fully managed, serverless data integration service provided by Amazon Web Services (AWS) simplifying the process of discovering, preparing, and transforming data for analytics, machine learning, and application development.     |
 | [Artie](/integrations/artie)                                     | A fully managed real-time data streaming platform that replicates production data into ClickHouse, unlocking customer-facing analytics, operational workflows, and Agentic AI in production.                                          |
 | [Azure Synapse](/integrations/azure-synapse)                     | A fully managed, cloud-based analytics service provided by Microsoft Azure, combining big data and data warehousing to simplify data integration, transformation, and analytics at scale using SQL, Apache Spark, and data pipelines. |
-| [Azure Data Factory](/integrations/azure-data-factory)           | A cloud-based data integration service that enables you to create, schedule, and orchestrate data workflows at scale. |
+| [Azure Data Factory](/integrations/azure-data-factory)           | A cloud-based data integration service that enables you to create, schedule, and orchestrate data workflows at scale.                                                                                                                 |
 | [Apache Beam](/integrations/apache-beam)                         | An open-source, unified programming model that enables developers to define and execute both batch and stream (continuous) data processing pipelines.                                                                                 |
-| [BladePipe](/integrations/bladepipe)                         | A real-time end-to-end data integration tool with sub-second latency, boosting seamless data flow across platforms.                                                                                |
+| [BladePipe](/integrations/bladepipe)                             | A real-time end-to-end data integration tool with sub-second latency, boosting seamless data flow across platforms.                                                                                                                   |
 | [dbt](/integrations/dbt)                                         | Enables analytics engineers to transform data in their warehouses by simply writing select statements.                                                                                                                                |
 | [dlt](/integrations/data-ingestion/etl-tools/dlt-and-clickhouse) | 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.                                                                            |
-| [Estuary](/integrations/estuary)                                 | A right-time data platform that enables millisecond-latency ETL pipelines with flexible deployment options.                                    |
+| [Estuary](/integrations/estuary)                                 | A right-time data platform that enables millisecond-latency ETL pipelines with flexible deployment options.                                                                                                                           |
 | [Fivetran](/integrations/fivetran)                               | An automated data movement platform moving data out of, into and across your cloud data platforms.                                                                                                                                    |
 | [NiFi](/integrations/nifi)                                       | An open-source workflow management software designed to automate data flow between software systems.                                                                                                                                  |
 | [Vector](/integrations/vector)                                   | A high-performance observability data pipeline that puts organizations in control of their observability data.                                                                                                                        |
@@ -1294,4 +1294,46 @@ groupby
 skipna
 --docs/cloud/guides/infrastructure/01_deployment_options/byoc/03_onboarding/05_azure_private_preview.md--
 SignalR
-provisioner
+provisioner
+--docs/integrations/data-ingestion/apache-flink/flink-connector.md--
+AsyncSinkBase
+BigDecimal
+BigInteger
+ClientConfigProperties
+DataStream
+DataWriter
+ElementConverter
+Flink's
+Kryo
+LocalDate
+LocalDateTime
+ZonedDateTime
+actualBytesPerBatch
+actualRecordsPerBatch
+actualTimeInBuffer
+numBytesSend
+numOfDroppedBatches
+numOfDroppedRecords
+numRecordSend
+numRequestSubmitted
+TaskManager
+totalBatchRetries
+triggeredByMaxBatchSizeCounter
+triggeredByMaxBatchSizeInBytesCounter
+triggeredByMaxTimeInBufferMSCounter
+writeArray
+writeBoolean
+writeDate
+writeDateTime
+writeDecimal
+writeFailureLatencyHistogram
+writeFixedString
+writeFloat
+writeInt
+writeIntUUID
+writeJSON
+writeLatencyHistogram
+writeMap
+writeString
+writeTuple
+writeUInt
@@ -3913,6 +3913,7 @@ unnest
 unoptimized
 unparsed
 unpooled
+unprocessable
 unrealiable
 unreplicated
 unresolvable

@@ -1029,6 +1029,7 @@ const sidebars = {
             'integrations/data-ingestion/apache-spark/spark-jdbc',
           ],
         },
+        'integrations/data-ingestion/apache-flink/flink-connector',
         'integrations/data-ingestion/aws-glue/index',
         {
           type: 'category',