ClickHouse vs Snowflake: A Hands-on Migration Guide for Analytics Teams

Facing Snowflake lock-in? A hands-on ClickHouse migration path for analytics teams in 2026

Hook: If your team is wrestling with rising Snowflake bills, unpredictable query concurrency, or the need for sub-second aggregations at scale, you’re not alone. In late 2025 ClickHouse secured a $400M round led by Dragoneer at a $15B valuation — a clear sign that high-performance, cost-efficient OLAP alternatives are mainstream. This guide gives analytics teams a practical, step-by-step migration path from Snowflake to ClickHouse with code samples, schema mapping, validation strategies, and realistic cost/performance tradeoffs.

Executive summary — what you’ll get

• Concrete migration checklist and cutover strategies for low-risk transitions
• Type-by-type schema mapping and examples
• Scripts for exporting from Snowflake and loading into ClickHouse (batch and streaming)
• Performance tuning guidelines and query equivalence patterns
• Cost comparison model and real-world tuning levers
• Validation and test plans to measure parity and performance

Why migrate in 2026 — trends that matter

In 2026 the OLAP landscape continued to split along two axes: compute-model flexibility and cost per query. Snowflake remains an incumbent with excellent developer ergonomics, strong ecosystem integrations (Snowpark, external functions, Snowpipe), and mature governance. But ClickHouse has accelerated feature parity and adoption — driven by better on-prem & cloud-managed options, faster ingestion for streaming pipelines, and attractive TCO for CPU-heavy workloads. Use these trends to decide what to migrate and why:

• Real-time analytics growth: ClickHouse's ingestion and low-latency aggregation are often better fit for sub-second dashboards and event streams.
• Compute vs storage economics: Snowflake separates compute (warehouses) and storage (S3), billing compute per-second. ClickHouse deployments (self-hosted or cloud) let teams optimize cores and leverage cheaper storage for slower data tiers.
• Vendor dynamics: ClickHouse's late-2025 raise (~$400M, $15B valuation) signals stronger managed-cloud features and enterprise support — lowering migration risk.

High-level migration strategy

1. Assess: query patterns, data volumes, SLA, concurrency, and regulatory constraints.
2. Map schema and functions: translate types, SQL functions, and analytic logic.
3. Prototype: spin up a ClickHouse cluster and load a representative subset.
4. Validate: run query parity tests, measure P95 latencies and cost per query.
5. Iterate: optimize schema, add materialized views or pre-aggregations.
6. Cutover: choose blue/green or hybrid mode with dual-writes or query routing for a controlled switch.

Assessment checklist

• Top 100 queries by CPU/time and frequency (gravity analysis)
• Table sizes and retention policies (hot vs cold data)
• CDC needs: Do you need real-time replication (sub-minute) or batch nightly loads?
• Security & compliance: encryption, VPC, IAM, audit logging
• UDFs and Snowpark pipelines — list code that needs porting

Schema mapping: Snowflake → ClickHouse

Below are pragmatic type mappings and notes for common Snowflake types. ClickHouse prefers precise numeric and fixed-length time types; design ORDER BY and partitioning keys carefully.

<!-- Schema mapping (examples) -->
Snowflake                     ClickHouse                 Notes
---------------------------   ------------------------   ---------------------------------
VARCHAR, STRING               String                     unlimited UTF-8 string
CHAR, TEXT                    String
BOOLEAN                       UInt8                      use 0/1; ClickHouse has no native boolean
NUMBER(p,s)                   Decimal(p,s) or Float64    prefer Decimal for money, Float for analytics
INT, INTEGER                  Int32 / Int64
BIGINT                        Int64
DATE                          Date                       ClickHouse Date is 32-bit, UTC 
TIMESTAMP_NTZ                 DateTime64(3)              choose precision (3-6) and timezone handling
TIMESTAMP_TZ                  DateTime64(6, 'UTC')      store normalized timezone
VARIANT (semi-structured)     String / JSONCompact       ClickHouse has JSON functions; for indexing, extract columns
ARRAY                         Array(Type)               use for small arrays; otherwise normalize
BINARY                        FixedString(N)            for exact binary blobs

Key design decisions:

• Choose MergeTree variants for OLAP: ReplacingMergeTree, AggregatingMergeTree, or SummingMergeTree depending on dedup and aggregation patterns.
• Define ORDER BY to match your most common GROUP BY and WHERE patterns: this directly impacts read performance.
• Use PARTITION BY for time-based retention; keep partitions coarse (monthly/weekly) to avoid many small parts.

Export from Snowflake — batch and incremental patterns

Two common patterns:

1. Bulk export of historical data via COPY INTO to S3/GCS and batch load into ClickHouse.
2. CDC with Snowflake STREAMS + TASKS or external CDC (Debezium) → Kafka → ClickHouse for near-real-time sync.

Batch export example (CSV to S3)

-- Snowflake: export a table to S3
COPY INTO 's3://my-bucket/exports/events_2025-10.csv'
FROM mydb.analytics.events
CREDENTIALS=(aws_key_id='AKIA...' aws_secret_key='...')
FILE_FORMAT=(TYPE=CSV FIELD_DELIMITER=',' NULL_IF=('') SINGLE=TRUE);

Incremental export using Streams + Tasks

Use STREAM to capture changes and a TASK to write diffs periodically to an external stage that ClickHouse can consume.

-- Snowflake: create a stream and task (pseudo)
CREATE STREAM events_stream ON TABLE mydb.analytics.events;
CREATE TASK export_task
  WAREHOUSE = ETL_WAREHOUSE
  SCHEDULE = 'USING CRON 0 * * * * UTC'
AS
COPY INTO 's3://my-bucket/exports/events_delta' FROM (
  SELECT * FROM mydb.analytics.events STREAM (events_stream)
) FILE_FORMAT = (TYPE=CSV);

Load into ClickHouse — options and code

ClickHouse supports multiple ingestion methods. Use the S3 table function or the S3 input format for batch loads. For streaming, use Kafka engine or HTTP ingestion.

Batch load from S3 using table function

-- Create the target table in ClickHouse
CREATE TABLE analytics.events (
  event_id UInt64,
  user_id UInt64,
  event_time DateTime64(3),
  event_type String,
  props String
) ENGINE = MergeTree()
PARTITION BY toYYYYMM(event_time)
ORDER BY (event_time, event_id)
SETTINGS index_granularity = 8192;

-- Insert from S3 CSV
INSERT INTO analytics.events
SELECT
  toUInt64(event_id),
  toUInt64(user_id),
  parseDateTimeBestEffort(event_time),
  event_type,
  props
FROM s3('https://s3.amazonaws.com/my-bucket/exports/events_2025-10.csv', 'CSV', 'event_id String, user_id String, event_time String, event_type String, props String');

Streaming via Kafka

-- Define a ClickHouse table backed by Kafka
CREATE TABLE kafka_events (
  event_id UInt64,
  user_id UInt64,
  event_time DateTime64(3),
  event_type String,
  props String
) ENGINE = Kafka SETTINGS
 kafka_broker_list = 'kafka1:9092,...',
 kafka_topic_list = 'events',
 kafka_group_name = 'clickhouse-consumer',
 kafka_format = 'JSONEachRow';

-- Materialize into a MergeTree table
CREATE TABLE events_buffer AS kafka_events ENGINE = MergeTree() ORDER BY (event_time, event_id);

-- Use a materialized view to move messages from Kafka engine to MergeTree
CREATE MATERIALIZED VIEW mv_kafka_to_mt TO events_buffer AS SELECT * FROM kafka_events;

SQL differences and translation notes

Snowflake provides a lot of built-ins (semi-structured JSON handling, lateral flatten, analytic functions) — most are available or reproducible in ClickHouse, sometimes with different function names or strategies.

• Window functions: ClickHouse supports window functions, but performance can differ; use pre-aggregation when possible.
• JSON/VARIANT: In ClickHouse, store JSON as String or JSONCompact and use JSONExtract functions. Consider extracting hot attributes to columns.
• Clustering / Cluster keys: Snowflake clustering keys are advisory. In ClickHouse, ORDER BY drives physical sort — make it match your query patterns.
• Time travel: Snowflake Time Travel and Fail-safe are built-in. ClickHouse manages retention with TTL and backups — plan retention and snapshots explicitly.

Performance tuning: ClickHouse levers that matter

When you benchmark, focus on these levers:

• ORDER BY (primary key): Determines on-disk sort — critical for fast GROUP BY / WHERE range queries.
• Partitioning: Use to prune large time ranges. Avoid too many small partitions.
• Compression codecs: LZ4 for speed, ZSTD for higher compression; choose per-table.
• Summaries & materialized views: Pre-aggregate heavy queries to reduce CPU.
• Merge Tree settings: parts_to_throw_insert, max_bytes_to_merge_at_min_space_in_pool and max_threads tuning for merge throughput.
• Join strategy: Use Dictionary tables for small lookup tables to avoid expensive distributed joins.

Example: converting a heavy Snowflake GROUP BY

Snowflake SQL:

SELECT event_type, date_trunc('hour', event_time) AS hr, count(*)
FROM analytics.events
WHERE event_time BETWEEN '2025-11-01' AND '2025-11-30'
GROUP BY 1, 2
ORDER BY 3 DESC;

ClickHouse approach — pre-aggregate with materialized view for hourly rollups:

CREATE MATERIALIZED VIEW mv_events_hourly
TO analytics.events_hourly
AS
SELECT
  event_type,
  toStartOfHour(event_time) AS hr,
  count() AS cnt
FROM analytics.events
GROUP BY event_type, hr;

-- Query the pre-aggregated table
SELECT event_type, hr, cnt
FROM analytics.events_hourly
WHERE hr BETWEEN '2025-11-01 00:00:00' AND '2025-11-30 23:00:00'
ORDER BY cnt DESC
LIMIT 100;

Validation and parity testing

Don’t assume parity — verify. Build an automated test suite:

1. Define a representative query set (top 100 queries + edge-cases using analytic UDFs).
2. Run queries on Snowflake and ClickHouse on identical datasets and compare results (aggregates, row counts, sample rows).
3. Measure latency percentiles (P50, P95, P99), resource usage (CPU, memory), and concurrency behavior.
4. Create data drifting tests: run nightly diffs of row counts and checksums for critical tables.

-- Simple checksum example (bigint hash)
SELECT sipHash64(tuple(*)) AS checksum FROM analytics.events WHERE toYYYYMM(event_time) = 202511;

Cutover strategies

• Phased reads: Route a subset of dashboards to ClickHouse while keeping Snowflake for others. Compare results & latencies.
• Blue/Green: Dual-write to both databases for a probation period. Use feature flags to switch consumers gradually.
• Final cutover: Freeze a writable window, replicate final delta, validate checksums, then flip the route.

Cost comparison: how to model TCO

Costs depend on deployment model. Below are modeling principles and an example with hypothetical numbers to illustrate tradeoffs. Replace with your org's unit costs.

Cost levers to model

• Storage: S3/GCS costs vs local SSD/remote block storage in your ClickHouse deployment.
• Compute: Snowflake credits for warehouses vs VM/EPHEMERAL cores for ClickHouse nodes.
• Network: egress from Snowflake vs internal network costs; S3 egress when moving data.
• Operational engineering: managed service vs self-hosted ops hours.
• Concurrency discounts: Snowflake auto-scaling vs ClickHouse cluster sizing.

Example (illustrative estimates)

Assumptions (monthly): 10TB compressed data hot, 100M queries (ad-hoc + dashboards), moderate concurrency.

• Snowflake: storage + compute credits (auto-scale) — business unit shows $20k–$50k/month depending on concurrency and caching.
• ClickHouse (managed cloud): cluster of 8 r5-like nodes + storage in S3 + managed service fee — estimated $8k–$25k/month depending on cloud region & redundancy.

Takeaway: For CPU-heavy, high-concurrency workloads with many repeated aggregations, ClickHouse often has better marginal cost per query. Snowflake shines for simplified operations, near-zero ops, and unpredictable workloads that benefit from elastic per-second pricing.

Operational considerations and risks

• Backups & snapshots: ClickHouse requires active snapshot plans; Snowflake has built-in time travel.
• Security: ensure IAM roles, encryption at rest, and network isolation are replicated in the new environment.
• Skill gap: train analysts on ClickHouse SQL nuances and performance patterns (ORDER BY, TTL, MergeTree concepts).
• Third-party integrations: check BI tools and orchestration integrations. Most major BI tools support ClickHouse in 2026, but test connectors and ODBC/JDBC drivers.

Real-world checklist and timeline (12-week example)

1. Week 1–2: Assessment, select pilot tables (10% data that represent hot queries).
2. Week 3–4: Prototype ClickHouse cluster, implement schema mapping, bulk load pilot data.
3. Week 5–6: Run parity tests, tune ORDER BY, set up materialized views for heavy queries.
4. Week 7–8: Implement streaming/CDC for delta sync, set up monitoring & alerts.
5. Week 9–10: Dual-write or read-switch for a subset of dashboards; monitor and iterate.
6. Week 11–12: Final cutover for remaining workloads, decommission or archive Snowflake tables.

Tools and reference utilities

• Snowflake: Streams & Tasks, COPY INTO, Snowpipe for continuous export
• ClickHouse: clickhouse-client, s3 table function, Kafka engine, materialized views
• CDC: Debezium, Maxwell, or cloud-native DMS depending on source databases
• Benchmarks: TPC-DS, custom query replays, and production query sampling
• Monitoring: Grafana + ClickHouse exporter, and query profiler

Practical rule: migrate the queries, not the tables. Prioritize migrating the top consumer queries and ensure they perform at parity before porting every table.

Advanced strategies and 2026-forward patterns

• Hybrid tiering: Keep raw historical data in cheaper object storage (S3) and use ClickHouse for hot aggregates and rolling windows.
• Vector & ML features: In 2026 ClickHouse ecosystems increasingly integrate vector indexes and approximate functions; plan for embedding vectors in secondary tables for feature stores.
• Serverless OLAP: Consider serverless ClickHouse offerings if you want a managed scaling model similar to Snowflake but with ClickHouse cost characteristics.

Actionable takeaways

• Start with a representative pilot of the top 10% queries and 10% of data.
• Translate schemas focusing on ORDER BY and partitioning first — they determine most read performance.
• Use materialized views and aggregation tables to emulate Snowflake performance for heavy GROUP BY workloads.
• Model TCO with realistic concurrency assumptions — ClickHouse often wins on high-concurrency, CPU-bound workloads.
• Plan a phased cutover with automated parity tests and checksum validation.

Final checklist before switching production

1. All critical queries validated for correctness and latency
2. Monitoring, alerting, and dashboards mirror Snowflake baselines
3. Backup & disaster recovery tested
4. Operational runbooks and on-call training completed
5. Security and compliance controls verified

Closing — why this moment is right for re-evaluation

ClickHouse's late-2025 funding and expanding managed offerings make it a practical alternative to Snowflake for many analytics workloads in 2026. The decision should be driven by workload characteristics, cost modeling, and operational readiness. This guide gives you the practical steps to make that migration predictable and measurable.

Next steps (quick wins)

• Run a 1-week pilot: pick 3 critical dashboards, export the backing tables, and load them into ClickHouse to compare latencies.
• Create a parity test harness: translate the top 50 queries and run them nightly to catch drift.
• Estimate cost with actual query samples rather than raw TB/month numbers — that’s where the biggest TCO surprise lies.

Call to action: Ready to build a migration plan tailored to your workload? Start with our migration checklist and an automated parity test harness. If you’d like a template (schema mapping CSV + sample scripts for Snowflake & ClickHouse), download the repo linked from our site or contact our consulting team to run a 2-week pilot.

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