ClickHouse and Kafka integration
Streaming Kafka topics into self-hosted ClickHouse with the Kafka table engine and materialized views: consumer groups, SASL and TLS, the documented durability gap and fsync setting, and monitoring lag.
ClickHouse integrations
Documented ClickHouse table engines that connect a self-hosted cluster to the systems teams already run, starting with Kafka streams and PostgreSQL databases.
ClickHouse and PostgreSQL integration
Querying and copying PostgreSQL data from ClickHouse with the PostgreSQL table engine and postgresql table function: what is pushed down, version requirements, and when to copy data instead of querying it live.
ClickHouse and Amazon S3: querying and loading files
Reading and writing Parquet, CSV and JSON files in S3 or S3-compatible storage from ClickHouse with the S3 table engine and s3 table function: wildcards, compression, storage classes, caching and when to load data into MergeTree instead.
Continuous ingestion from S3 with the ClickHouse S3Queue engine
Streaming new files from an S3 bucket into ClickHouse with the S3Queue engine and a materialized view: ordered versus unordered mode, file tracking in Keeper, processing threads, and what to monitor so files are not skipped or loaded twice.
ClickHouse and MySQL integration
Querying and writing MySQL tables from ClickHouse with the MySQL table engine and MySQL database engine: type casting, Nullable handling, REPLACE and ON DUPLICATE KEY options, TLS through named collections, and when to copy data instead.
ClickHouse and MongoDB integration
Reading MongoDB collections from ClickHouse with the read-only MongoDB table engine: version requirements, ObjectId handling, missing-field defaults, and why analytics teams usually copy MongoDB data into MergeTree tables.
ClickHouse and Apache Iceberg integration
Querying and writing Apache Iceberg tables on S3, Azure or local storage from self-hosted ClickHouse: the iceberg table function versus the Iceberg table engine, catalogs such as Glue, Unity, Polaris and REST, and the schema and write limits to plan around.
ClickHouse and Delta Lake integration
Reading and writing Delta Lake tables in S3, Google Cloud Storage or Azure from ClickHouse with the DeltaLake engine: version requirements for writes, creating new Delta tables, URL formats, and when to accelerate queries with MergeTree copies.
ClickHouse Kafka Connect Sink setup
Delivering Kafka topics to ClickHouse with the official Kafka Connect Sink instead of the Kafka table engine: Connect and Java requirements, the minimum ClickHouse version, exactly-once options, and when each approach suits a self-hosted cluster.
ClickHouse and Apache Spark integration
Reading and writing ClickHouse from Apache Spark with the native Spark connector or Spark JDBC: the DataSourceV2 catalog connector ClickHouse recommends, version pairing, and batch loading patterns for backfills.
ClickHouse and Apache Flink integration
Writing Flink streams into ClickHouse with the official Flink sink connector: Flink 1.17 and 2.0 artifacts, Java requirements, DataStream-only support, and batching settings for high-volume streams.
ClickHouse and RabbitMQ integration
Consuming RabbitMQ queues into ClickHouse with the RabbitMQ table engine and a materialized view: exchanges and routing, persistent delivery, dead-letter handling of unprocessable messages, and block size tuning.
ClickHouse and NATS integration
Subscribing ClickHouse to NATS subjects with the NATS table engine: wildcard subjects, consumer counts, flush intervals and credentials files held in named collections.
Connecting Grafana to ClickHouse
Installing and configuring the signed Grafana Labs ClickHouse data source plugin against a self-hosted cluster: read-only users, TLS, query builder versus SQL, and dashboard performance on large tables.
Connecting Apache Superset to ClickHouse
Connecting self-hosted Apache Superset to ClickHouse with the clickhouse-connect driver: HTTP(S) connection details, read-only database users, and dashboard query patterns that keep cluster load predictable.
Connecting Metabase to ClickHouse
Adding ClickHouse to self-hosted Metabase with the ClickHouse Metabase driver: installing the JAR in the plugins folder, connection settings, and keeping dashboards fast with materialized views.
Connecting Tableau to ClickHouse
Using the official ClickHouse Tableau connector from Tableau Exchange with a self-hosted cluster: JDBC driver setup, live versus extract connections, and secure access for analysts.
Connecting Power BI to ClickHouse
Querying self-hosted ClickHouse from Power BI Desktop and Power BI Service: the ClickHouse ODBC driver, the built-in connector version, import versus DirectQuery, and gateway placement for on-premises clusters.
Shipping logs to ClickHouse with Vector
Using Vector to tail log files such as Nginx access logs and send them to ClickHouse: sink configuration, batching, parsing into typed columns, and when to choose Vector over the OpenTelemetry collector.
