ClickHouse alternative — ClickHouse vs Skippr
ClickHouse is Apache-2.0 columnar analytics. MergeTree (and kin) is the engine. Latest stable GitHub tag is 26.10.2.103 (24 Oct 2026). Iceberg is a table function / engine (icebergS3(), IcebergS3, Azure and local variants), not MergeTree. Reads cover v1/v2; v3 is partial (deletion-vector reads, no manifest compaction). Equality deletes from 25.8. INSERT is beta from 26.2 and needs allow_insert_into_iceberg = 1. Table create, ALTER TABLE, compaction, expire snapshots, and orphan cleanup stay experimental. UPDATE / MERGE are not supported. Catalog writes (create + INSERT) are beta only on Databricks Unity, Microsoft OneLake, and SeaweedFS. Glue, Iceberg REST, Lakekeeper, and BigLake are read-only; Nessie is experimental read. CDC into MergeTree is ClickPipes on Cloud or PeerDB self-host, not a WAL-to-Iceberg path inside the server. Self-host is free. Cloud: 300 USD credits for 30 days, then compute-unit hours (1 CU = 8 GiB RAM, 2 vCPU) plus storage. Public us-east-1 Enterprise is 0.39030 USD a CU-hour and 25.30 USD a TB-month; idle compute scales to zero. ClickPipes is extra. This is not Iceberg ∪ WAL you query in one binary.
Skippr is an open Streamhouse you run as one Rust binary. Write skippr.yml (or the same thing in Python), discover, sync. You query live state over Iceberg ∪ WAL, and the history is ordinary Iceberg in your bucket. No MergeTree cluster. ELT into a warehouse is one path, not the product.
| Feature | Skippr | ClickHouse |
|---|---|---|
| Deploy | Single Rust binary, your VPC or on-prem | clickhouse server, or ClickHouse Cloud |
| Schema | In-stream evolution, sibling columns | MergeTree DDL. Iceberg ALTER is experimental |
| Storage | Open Iceberg on Parquet you own | MergeTree parts. Iceberg via table engine / catalog |
| CDC | Log-based; fails closed if the dest cannot honour | ClickPipes or PeerDB into MergeTree. Not Iceberg |
| Query | skipprd query on Iceberg ∪ WAL; history in any Iceberg engine | ClickHouse SQL on MergeTree, or icebergS3() / catalog |
| Pricing | Free self-hosted engine | Apache-2.0. Cloud is CU-hour + TB-month. |
What ClickHouse is good at
Columnar SQL on MergeTree at scan speed you already staff, plus reading Iceberg/Delta in place when the lake is someone else's write path. That is the product. Beta INSERT into Iceberg (and Unity/OneLake/SeaweedFS catalog writes) is why some warehouses now land Parquet without Spark. If the job is "this ClickHouse cluster, and Iceberg as a table function," that is a real product.
Where the job is wider
ClickHouse is a warehouse. It is not a Streamhouse. There is no Iceberg ∪ WAL. There is no skipprd query on in-flight lake rows. REST catalogs will not take INSERT. Iceberg UPDATE/MERGE do not exist. CDC still starts in ClickPipes or PeerDB and lands MergeTree first.
Parts, replicas, and CU-hours are the ops. Skippr will not replace a ClickHouse cluster that already serves BI. Skippr also will not write MergeTree. ClickPipes and PeerDB are separate pages.
If the job is "ClickHouse we already staff," ClickHouse is the narrower tool. If the job is live SQL on Iceberg you run, MergeTree was never that.
Getting started
Install skipprd from the install guide, or pip install skippr. The three tabs write the same skippr.yml. Then discover, sync, and query.
skippr:
workspace: analytics
skipprd_el_storage_mode: local
pipelines:
orders:
data_source: data_sources.app_db
data_sink: data_sinks.lake
data_sources:
app_db:
Postgres:
host: db.internal
user: skippr_reader
password: ${APP_DB_PASSWORD}
database: app
tables: [orders, customers]
cdc_mode: snapshot_then_cdc
data_sinks:
lake:
AthenaIceberg:
warehouse: s3://my-iceberg-warehouse/
glue_database_name: analytics
athena_workgroup_name: primary
athena_results_s3_bucket: my-athena-results
region: us-east-1
schema_sink: schema_sinks.lake_schema
schema_sinks:
lake_schema:
AthenaIceberg:
warehouse: s3://my-iceberg-warehouse/
glue_database_name: analytics
athena_workgroup_name: primary
athena_results_s3_bucket: my-athena-results
region: us-east-1skipprd --workspace analytics --storage-mode local connect data-source postgres \
--pipeline orders \
--name app_db \
--host db.internal \
--user skippr_reader \
--password '${APP_DB_PASSWORD}' \
--database app \
--tables orders \
--tables customers \
--cdc-mode snapshot_then_cdc
skipprd connect data-sink athenaiceberg \
--pipeline orders \
--name lake \
--warehouse s3://my-iceberg-warehouse/ \
--glue-database-name analytics \
--athena-workgroup-name primary \
--athena-results-s3-bucket my-athena-results \
--region us-east-1
skipprd connect schema-sink athenaiceberg \
--pipeline orders \
--name lake_schemafrom skippr import (
Config,
DataSinkAthenaIceberg,
DataSourcePostgres,
DataSourcePostgresSourceCdcMode,
EnvRef,
LocalStorage,
Pipeline,
)
cfg = Config().workspace("analytics").storage(LocalStorage())
app_db = cfg.data_source(
"app_db",
DataSourcePostgres(
host="db.internal",
user="skippr_reader",
password=EnvRef("APP_DB_PASSWORD"),
database="app",
tables=["orders", "customers"],
cdc_mode=DataSourcePostgresSourceCdcMode.SNAPSHOTTHENCDC,
),
)
lake = cfg.data_sink(
"lake",
DataSinkAthenaIceberg(
warehouse="s3://my-iceberg-warehouse/",
glue_database_name="analytics",
athena_workgroup_name="primary",
athena_results_s3_bucket="my-athena-results",
region="us-east-1",
),
schema_sink="lake_schema",
)
cfg.pipeline("orders", Pipeline(data_source=app_db, data_sink=lake))
cfg.save("skippr.yml")export APP_DB_PASSWORD='your-postgres-password'
skipprd discover --pipeline orders
skipprd sync --pipeline orders --once
skipprd query "SELECT count(*) FROM analytics.orders"Which to choose
Choose ClickHouse if you want MergeTree SQL, and Iceberg as a table function (or a beta INSERT) on that cluster is enough.
Choose Skippr if you want one binary, live SQL on Iceberg ∪ WAL, and files that stay yours. Skippr will not run ClickHouse.
Install from Install. Connector and query reference live in Developer Docs. For optional onboarding in your VPC, Enterprise Jumpstart or Contact.