ClickHouse
ClickHouse is a columnar OLAP database designed for real-time analytics on event streams. It ingests millions of rows per second and executes aggregations (COUNT, SUM, GROUP BY) orders of magnitude faster than traditional warehouses. GTM teams use it for real-time dashboards (website activity, product usage, marketing attribution) where sub-second query latency matters. It requires more hands-on management than Snowflake or BigQuery but delivers unmatched speed for aggregation-heavy workloads.
Why it ranks here
- Fastest aggregation performance among columnar databases; queries that take seconds in Snowflake run in milliseconds in ClickHouse.
- Handles high-volume inserts (millions of rows/sec) without backpressure, making it ideal for streaming event data.
- Open-source and self-hostable; ClickHouse Cloud offers managed hosting with pay-per-query pricing.
Where it sits
- Snowflake and BigQuery are easier to operate and have richer ecosystems; ClickHouse requires expertise in table engines, partitioning, and replication.
- Databricks handles batch and streaming in a unified platform; ClickHouse is SQL-only and requires separate tools for ML.
- ClickHouse excels at aggregations but is slower for complex joins and ad-hoc exploratory queries compared to Snowflake.
How GTM Systems teams use it
- Build real-time marketing attribution dashboards that aggregate web clickstream data by UTM parameters and join with Salesforce opportunities to show same-day pipeline impact.
- Store high-volume product usage events (API calls, feature usage, errors) in ClickHouse and surface them in real-time customer health dashboards.
- Use ClickHouse as the hot tier for recent data (last 90 days) and archive older data to S3; query both tiers together via ClickHouse's S3 table engine.
- Feed pre-aggregated metrics from ClickHouse into reverse-ETL tools (Hightouch, Census) to sync lead scores and engagement signals back to CRM.
In-depth notes
- Pay-per-query pricing (compute) plus storage; significantly lower costs than Snowflake for aggregation-heavy workloads.
- Self-hosted ClickHouse is free but requires managing replication, backups, and scaling. ClickHouse Cloud (managed) handles this but adds cost.
- Table engines (MergeTree, ReplacingMergeTree, AggregatingMergeTree) must be chosen upfront based on use case; wrong engine choice degrades performance.
- ClickHouse has limited support for updates and deletes; it is optimized for append-only data. Use ReplacingMergeTree for deduplication at read time.
- Joins are slower than in Snowflake or BigQuery; ClickHouse is best for pre-aggregated metrics or denormalized fact tables.
Best for
Real-time analytics dashboards on high-volume event data; teams that need sub-second query latency for aggregations.
Avoid if
Your queries are join-heavy or require frequent updates. Snowflake or BigQuery will be simpler and faster for traditional BI workloads.
The rest of Data warehouse & modeling
Snowflake Leader Cloud data warehouse with compute-separate-from-storage architecture; dominant choice for GTM analytics and cross-system reporting. #2 
Google BigQuery Leader Serverless cloud data warehouse on GCP; pay-per-query pricing and tight integration with Google Analytics and Sheets. #3 
dbt (data build tool) Leader SQL-based transformation framework; turns raw warehouse tables into tested, version-controlled models using Jinja-templated SELECT statements. #4 
Databricks Strong Unified lakehouse platform combining data warehousing, streaming, and ML; built on Apache Spark and Delta Lake. #5 
Microsoft Fabric Strong Unified analytics platform combining data warehouse (Synapse), lakehouse, data integration, and Power BI; Azure-native. #8 SQLMesh Situational Data transformation framework focused on incremental builds, fast CI, and column-level lineage; positioned as a next-generation dbt alternative. #9 
Coalesce Situational Visual data transformation platform built on Snowflake; provides a drag-and-drop DAG builder that compiles to dbt-compatible SQL. #10 
MotherDuck Situational Serverless cloud service for DuckDB; combines local query execution with cloud-based collaboration and storage. #11 
Firebolt Niche Cloud data warehouse optimized for sub-second query latency on large datasets; uses sparse indexing and aggressive caching. Learning guide
Setup
Sign up for ClickHouse Cloud (managed) or spin up a local ClickHouse server via Docker. Create a database and a MergeTree table with ORDER BY on the most-filtered column (timestamp, user_id), then stream sample events using INSERT or the ClickHouse HTTP API.
First thing to build
Build a real-time engagement dashboard that aggregates web clickstream events by UTM parameters using GROUP BY and COUNT. Query it via JDBC from Grafana or Metabase to validate sub-second latency.
What actually matters
- Table engines: use MergeTree for append-only data, ReplacingMergeTree for deduplication by primary key, and AggregatingMergeTree for pre-aggregated rollups.
- ORDER BY clause: this defines sort order and sparse indexing; choose columns used in WHERE filters (timestamp, user_id, session_id) to skip irrelevant data blocks.
- Partitioning: partition by date (PARTITION BY toYYYYMM(timestamp)) to drop old data fast and isolate queries to recent months.
- Replication and sharding: use Replicated table engines (ReplicatedMergeTree) for high availability; sharding requires manual setup via Distributed tables.
Watch out
ClickHouse is optimized for append-only data; frequent updates or deletes degrade performance, and ReplacingMergeTree deduplicates only at merge time.