Equivalent concepts in ClickStack and Elastic

ClickHouse and Elasticsearch take fundamentally different approaches to lakehouse integration. ClickHouse is a fully-fledged query execution engine capable of executing queries over lakehouse formats such as [Iceberg](/docs/sql-reference/table-functions/iceberg) and [Delta Lake](/docs/sql-reference/table-functions/deltalake), as well as integrating with data lake catalogs such as [AWS Glue](/docs/use-cases/data-lake/glue-catalog) and [Unity catalog](/docs/use-cases/data-lake/unity-catalog). These formats rely on efficient querying of [Parquet](/docs/interfaces/formats/Parquet) files, which ClickHouse fully supports. ClickHouse can read both Iceberg and Delta Lake tables directly, enabling seamless integration with modern data lake architectures.

In contrast, Elasticsearch is tightly coupled to its internal data format and Lucene-based storage engine. It can't directly query lakehouse formats or Parquet files, limiting its ability to participate in modern data lake architectures. Elasticsearch requires data to be transformed and loaded into its proprietary format before it can be queried.

ClickHouse's lakehouse capabilities extend beyond just reading data:

- **Data catalog integration**: ClickHouse supports integration with data catalogs like [AWS Glue](/docs/use-cases/data-lake/glue-catalog), enabling automatic discovery and access to tables in object storage.
- **Object storage support**: native support for querying data residing in [S3](/docs/engines/table-engines/integrations/s3), [GCS](/docs/sql-reference/table-functions/gcs), and [Azure Blob Storage](/docs/engines/table-engines/integrations/azureBlobStorage) without requiring data movement.
- **Query federation**: the ability to correlate data across multiple sources, including lakehouse tables, traditional databases, and ClickHouse tables using [external dictionaries](/docs/dictionary) and [table functions](/docs/sql-reference/table-functions).
- **Incremental loading**: support for continuous loading from lakehouse tables into local [MergeTree](/docs/engines/table-engines/mergetree-family/mergetree) tables, using features like [S3Queue](/docs/engines/table-engines/integrations/s3queue) and [ClickPipes](/docs/integrations/clickpipes).
- **Performance optimization**: distributed query execution over lakehouse data using [cluster functions](/docs/sql-reference/table-functions/cluster) for improved performance.

These capabilities make ClickHouse a natural fit for organizations adopting lakehouse architectures, allowing them to leverage both the flexibility of data lakes and the performance of a columnar database.

- [Elastic Stack vs ClickStack](#elastic-vs-clickstack)- [Elasticsearch vs ClickHouse](#elasticsearch-vs-clickhouse)
    - [Core structural concepts](#core-structural-concepts)- [Data modeling and flexibility](#data-modeling-and-flexibility)- [Ingestion and transformation](#ingestion-and-transformation)- [Query languages](#query-languages)- [File formats and interfaces](#file-formats-and-interfaces)- [Indexing and storage](#indexing-and-storage)- [Distribution and replication](#distribution-and-replication)- [Deduplication and routing](#deduplication-and-routing)- [Aggregations and execution model](#aggregations-execution-model)- [Data management](#data-management)- [Rollups vs incremental aggregates](#rollups-vs-incremental-aggregates)- [Lakehouse support](#lakehouse-support)

Was this page helpful?