Spark versus ClickHouse for analytics?

Summary

• Spark and ClickHouse cover different layers of analytics. Apache Spark is a distributed processing engine that transforms data held in files and open tables; ClickHouse is a column-oriented OLAP database that stores data in its own table engines and answers SQL over it.
• Spark fits work that transforms, joins and enriches large datasets, mixes SQL with Python or Scala, or runs streaming and machine learning in the same pipeline.
• ClickHouse fits work that loads event data into a database and serves repeated aggregation queries to dashboards or applications.
• The two connect: ClickHouse documents a Spark connector, and its deltaLake and iceberg table functions query tables that Spark writes.
• On Databricks, Spark pipelines and SQL analytics share the same governed Delta Lake and Iceberg tables, with SQL warehouses and the Photon engine serving interactive queries.

Spark versus ClickHouse for analytics?

Use Spark to build and reshape analytical data, and consider ClickHouse when you need a database that ingests data and serves aggregation queries over it. Apache Spark is "a unified analytics engine for large-scale data processing," with APIs in Python, Scala, Java and R (R is deprecated) plus Spark SQL. It's compute: it processes data that lives in files, tables and databases outside the engine. ClickHouse describes itself as a "column-oriented SQL database management system (DBMS) for online analytical processing (OLAP)," so data is loaded into ClickHouse tables and queried there. The real decision is where the data should live and what shape the work takes.

What each engine is built for

Apache Spark bundles Spark SQL for structured data, Structured Streaming "for incremental computation and stream processing," MLlib for machine learning and the pandas API on Spark. It runs on Spark Standalone, Hadoop YARN or Kubernetes, and Spark SQL "can also act as a distributed query engine using its JDBC/ODBC or command-line interface." Row-level changes come from table formats: Delta Lake and Apache Iceberg both support UPDATE, DELETE and MERGE from Spark.
ClickHouse stores data in table engines such as the MergeTree family, which its docs say is "designed for high data ingest rates and huge data volumes." Inserts create table parts that a background process merges, and the primary key sets the sort order inside each part through a sparse index. Incremental materialized views act as "a trigger that runs a query on blocks of data as they're inserted into a table," so aggregates are computed at insert time. ClickHouse is available as open-source software and as a cloud offering.

Spark vs ClickHouse: how they compare

Capability	Apache Spark	ClickHouse
Category	Distributed processing engine	Column-oriented OLAP database
Where data lives	Files and tables in external storage, read through data sources	ClickHouse tables, mainly the MergeTree family; table functions for external data
Interfaces	DataFrame APIs in Python, Scala, Java and R (R is deprecated); Spark SQL	SQL
Client access	Spark Connect; JDBC/ODBC through the Thrift server	HTTP and native TCP interfaces; JDBC, ODBC and ADBC drivers
Incremental processing	Structured Streaming	Incremental materialized views that run at insert time
Updates and deletes	Through Delta Lake or Iceberg: UPDATE, DELETE, MERGE	Mutations with ALTER TABLE; lightweight updates and deletes; deduplication for frequent updates
Open table formats	Reads and writes Delta Lake and Apache Iceberg tables	deltaLake and iceberg table functions, with writes in Beta; DataLakeCatalog engine for catalogs including Unity Catalog (Beta setting)
Deployment	Standalone, YARN or Kubernetes, or a managed service	Open-source software or a cloud offering

How to choose

• Shape of the work. Multi-step transformations, large joins and enrichment across sources point to Spark. Repeated aggregations over append-heavy event data, queried by dashboards or applications, point to ClickHouse.
• Where the data should live. If you want one copy in open tables that several engines can read, Spark with Delta Lake or Iceberg keeps it there. If you're comfortable loading data into a database's own storage, ClickHouse manages that storage for you.
• Languages and libraries. Python or Scala pipelines, notebooks and MLlib favor Spark. SQL-only teams can work in either.
• Update patterns. Frequent row-level changes map to MERGE on Delta Lake or Iceberg. ClickHouse's docs suggest deduplication or lightweight updates when updates are frequent.
• Running both. If you keep both, decide which copy is the source of truth and how ClickHouse stays in sync: through the Spark connector or by querying lake tables in place.

How Databricks approaches analytics on Spark

On Databricks, pipelines and BI work on the same tables. Lakeflow Pipelines builds streaming tables and materialized views, triggered or continuous. SQL warehouses, compute resources for querying data on Databricks, serve AI/BI dashboards and BI tools over JDBC and ODBC, and Photon, a Databricks-native vectorized engine, is on by default for SQL warehouses and serverless compute. Unity Catalog governs those Delta Lake and Apache Iceberg tables with privileges, row and column filters, lineage and auditing. If ClickHouse stays in your stack, its DataLakeCatalog engine lists Databricks Unity Catalog as a supported catalog for Delta Lake and Iceberg tables, and ClickHouse lists the allow_database_unity_catalog setting as Beta.

FAQs

Can ClickHouse replace Spark for ETL?

ClickHouse transforms data with SQL and incremental materialized views inside the database. Pipelines written in Python or Scala, or that use MLlib, run on Spark, which provides those APIs and libraries.

Can ClickHouse query Delta Lake tables written by Spark?

Yes. The deltaLake table function reads Delta Lake tables in Amazon S3, Google Cloud Storage, Azure Blob Storage or a local file system. Writes are a Beta feature, disabled by default.

How do I move data from Spark into ClickHouse?

ClickHouse recommends its Spark connector, which implements DataSourceV2 and has its own catalog management. Spark's JDBC data source is the alternative.

Can Spark serve dashboards directly?

Spark SQL can act as a distributed query engine through its JDBC/ODBC (Thrift) server, so BI tools can query it. On Databricks, SQL warehouses fill that role for dashboards and BI tools.