ClickHouse Consulting & Implementation
We design, build and tune ClickHouse for real-time analytics, and we migrate workloads to it from Snowflake, BigQuery, Redshift and PostgreSQL. Our ClickHouse team includes certified professionals, and we work on ClickHouse Cloud as well as on clusters you run yourselves.
Where ClickHouse Fits in Your Data Platform
ClickHouse is an open-source, column-oriented database built for analytical queries over very large tables of events. It stores each table sorted by a key you choose, reads only the columns a query asks for and compresses them heavily, which is why it suits dashboards and applications that scan a lot of data and still need an answer quickly.
Most of the teams we talk to already have a warehouse such as Snowflake, BigQuery or Redshift, and they keep it for governed, company-wide reporting. ClickHouse usually goes in beside it, for the workloads where latency or query volume matter most. Our ClickHouse vs Snowflake comparison explains how we decide which workload belongs where.
We publish the builds we run ourselves. One of them is a self-managed CDC pipeline that loaded a MySQL table of five million rows into ClickHouse in about 3 minutes 23 seconds and then kept it current from the binary log. The write-up includes the configuration, the measured results and the points that need care in production.
Use Cases
Real-Time Use Cases for ClickHouse
These are the workloads ClickHouse is chosen for in production. Where a company has published how it runs one of them on ClickHouse, we link to its own write-up so you can read the details at the source.
Website and API traffic analytics
Every request is counted and charted within seconds, by country, status code, path or customer. Raw events stream in from Kafka, and materialized views roll them up into SummingMergeTree and AggregatingMergeTree tables, so dashboards read compact per-minute aggregates instead of raw logs.
In production: Cloudflare runs its HTTP analytics on ClickHouse. Its 2018 write-up describes an average of 6 million requests per second, with peaks of 8 million, fed from Kafka. Cloudflare engineering blog
Log analytics and observability
Application logs change shape every time a service changes, which breaks fixed schemas. We keep the common fields as columns and the rest as key-value data, add bloom-filter skipping indexes for lookups by request or trace ID, and move older data to object storage with TTL rules.
In production: Uber replaced its ELK stack with ClickHouse for logging. Its 2021 post reports millions of logs per second from thousands of services, at under half the hardware cost of ELK. Uber engineering blog
Product and user-behaviour analytics
Funnels, retention and cohort reports over large event tables, returned while the person who asked is still looking at the screen. Events are sorted by project, event name and time, and ClickHouse's windowFunnel, retention and uniq functions compute the reports directly in SQL.
In production: PostHog, the open-source product analytics platform, says ClickHouse powers all of its analytics features. PostHog handbook
LLM and AI application tracing
Every model call becomes a trace with prompts, token counts, latency and cost, and teams filter and aggregate those traces across millions of rows. ClickHouse absorbs the write rate, and ReplacingMergeTree tables let a trace be updated as later spans arrive.
In production: Langfuse moved its traces, observations and scores from Postgres to ClickHouse in version 3 (December 2024), after Postgres slowed down at millions of rows of tracing data. Langfuse changelog
Live dashboards on operational databases
Orders, payments or bookings appear on a dashboard seconds after they are written to MySQL or PostgreSQL, with little extra load on the production database. Change data capture streams every insert, update and delete into ReplacingMergeTree tables, and a current-state view feeds the dashboard.
In production: Our own build: a five-million-row MySQL table loaded in about 3 minutes 23 seconds, then kept current from the binary log. Read the write-up
Customer-facing analytics in your product
Usage dashboards and reports that your own customers open inside your application, where many small queries arrive at once and each has to return quickly. The tenant ID goes first in the sort key so each query reads only that customer's data, materialized views pre-aggregate the common reports, and row policies keep tenants apart.
Migration
Migrating to ClickHouse from Your Warehouse
Most migrations move specific workloads, usually the high-volume, low-latency ones, and leave governed reporting where it already runs. History moves in bulk through object storage, and an incremental feed keeps ClickHouse current until cutover. What differs by source is the export and the way the data types translate.
From Snowflake
Tables are exported with COPY INTO an external stage as Parquet files of around 150 MB, then loaded with INSERT INTO ... SELECT from ClickHouse's s3, gcs or azureBlobStorage table functions. VARIANT and OBJECT columns arrive as JSON strings and are converted to typed columns on insert. ClickHouse's guide reports about 30 minutes to export roughly 5 TB to S3 from a 2X-Large warehouse in the same region.
From BigQuery
Tables are exported to Google Cloud Storage as Parquet or Avro, in files of up to 1 GB, and loaded with s3Cluster so every node of a ClickHouse Cloud service reads in parallel. ClickHouse's guide notes that Google allows up to 50 TB of exports a day at no charge. ClickPipes also has a BigQuery connector, in private preview, for the bulk load. Changes after that come from scheduled exports filtered on an updated-at column.
From Redshift
UNLOAD writes each table to S3 as Parquet, and ClickHouse reads the files in parallel with the s3 table function, inferring the schema from them where that helps. ClickHouse's PostgreSQL table function cannot connect to Redshift, so a direct pull goes through the JDBC Bridge, and an ETL tool such as Airbyte is the third option.
From PostgreSQL or MySQL
When reporting runs on a read replica that can no longer keep up, ClickPipes or a self-managed CDC pipeline copies the tables into ClickHouse and keeps them in sync, so dashboards can move across one table at a time.
How a Migration Runs
- 01
Inventory
We list the queries, dashboards and APIs in scope with their latency targets, data volumes and refresh needs, and agree which workloads move and which stay on the warehouse.
- 02
Schema translation
Each table gets ClickHouse types, a sort key matched to its filters, and partitions by month or day where data is dropped by age. Columns are non-nullable unless they need to be, and because ClickHouse does not enforce primary key uniqueness, deduplication is designed into the table engine.
- 03
SQL translation
Queries and transformations are rewritten for ClickHouse SQL. Large joins are usually reworked, either by denormalising at load time or by turning small lookup tables into dictionaries.
- 04
Historical backfill
History moves through object storage as Parquet, is loaded in parallel, and is checked against the source with row counts and aggregates for every partition.
- 05
Incremental sync
New data follows by CDC, Kafka or scheduled exports until cutover, with updates and deletes applied through ReplacingMergeTree tables.
- 06
Parallel run and cutover
Both systems answer the same queries until the results reconcile and the latency targets are met. Dashboards and APIs are then pointed at ClickHouse one workload at a time.
What We Do
ClickHouse Services
Architecture and Table Design
Choosing table engines, sort keys, partitions and data types for your query patterns, and deciding between ClickHouse Cloud and a self-managed cluster replicated through ClickHouse Keeper.
Ingestion and CDC
Batch loads from object storage, streaming from Kafka, and change data capture from MySQL and PostgreSQL, using ClickPipes where it fits and self-managed pipelines where it does not.
Real-Time Rollups
Incremental materialized views, AggregatingMergeTree tables with State and Merge functions, and refreshable views for joins and periodic snapshots.
Query Performance Tuning
Finding slow queries in system.query_log, reading EXPLAIN output, and fixing them with better sort keys, skipping indexes, projections and PREWHERE.
Migration from Your Warehouse
Moving workloads from Snowflake, BigQuery, Redshift or PostgreSQL to ClickHouse, with schema and SQL translation, a reconciled backfill and a parallel run before cutover.
Operations and Cost Control
Monitoring, backups, upgrades and capacity planning for self-managed clusters, and service sizing and idling settings on ClickHouse Cloud.
Deep Dives
Our ClickHouse Guides
ClickHouse vs Snowflake: Architecture, Trade-offs and Real-Time Scenarios
How we decide which workloads belong in ClickHouse and which stay in the warehouse, covering real-time analytics, CDC, cost and governance.
ClickHouse Data Integration: Batch, Streaming, CDC and API Patterns
Choosing a reliable way to load ClickHouse from files, Kafka, operational databases, APIs and other warehouses.
ClickHouse Materialized Views for Real-Time Rollups
Incremental materialized views, AggregatingMergeTree with State and Merge functions, backfills, joins and refreshable views.
ClickHouse Query Performance Tuning: Sort Keys, Indexes and Projections
Using query_log and EXPLAIN to find slow queries, then fixing them with sort keys, partitions, skipping indexes, projections and PREWHERE.
MySQL to ClickHouse with Continuous CDC
A build we ran ourselves: a five-million-row MySQL table loaded in about 3 minutes 23 seconds and then kept current from the binary log.
Local MySQL to ClickHouse Cloud with ClickPipes CDC
Initial loading and continuous change capture from MySQL using ClickPipes, the managed ingestion service in ClickHouse Cloud.
ClickHouse Questions
We Actually Get Asked
In most cases you do not need to. Snowflake is strong at governed, organisation-wide reporting, data sharing and mixed workloads, and most teams keep it. ClickHouse is the better fit for the low-latency, high-volume workloads that sit beside it. We look at which queries would actually benefit before recommending any move, and our ClickHouse vs Snowflake comparison sets out the trade-offs.
Yes. We move the workloads that benefit, usually the high-volume, low-latency ones, by exporting history through object storage as Parquet, translating the schema and SQL, and running both systems side by side until the results match. The migration section on this page describes how the data moves from each source.
ClickHouse Cloud separates storage from compute, scales services for you and can idle them when nobody is querying, so there is no cluster for your team to operate. Self-managed open-source ClickHouse gives you full control over hardware, versions and network placement, and your team then owns replication, backups and upgrades. We work with both and help you choose based on data residency, cost and the skills you have in-house.
Yes, with the right table design. We land changes in ReplacingMergeTree tables that carry a version column and a delete marker, so the latest version of each row wins when parts merge. Dashboards that need an exact current state read through a view that removes superseded and deleted rows. Our MySQL to ClickHouse CDC write-up shows the full pattern.
Yes, our ClickHouse team includes certified professionals. They work alongside our Snowflake and data engineering consultants, so a project that spans ClickHouse and a warehouse is staffed by one team.
We start with a short assessment of the workloads you have in mind: the queries, data volumes, latency targets and where the data comes from. You receive a recommended design and a costed plan, and you can stop there or ask us to build it.
Something else on your mind?
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Tell us about the queries, the data volumes and the latency you need, and we will tell you what is realistic.
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