A Practical Migration Plan: Moving Analytics from Snowflake to ClickHouse

Move fast, avoid data loss: a practical migration plan from Snowflake to ClickHouse

If your analytics costs are ballooning, queries are slowing or you need sub-second OLAP for high-concurrency dashboards, migrating from Snowflake to ClickHouse can cut costs and boost performance — but it is not a lift-and-shift. This guide gives a pragmatic, step-by-step checklist for teams in 2026 who need a reliable migration path: schema translation, query compatibility, ETL rework, and robust validation testing, plus common pitfalls and mitigation patterns we've seen in production.

Why teams are moving in 2026

ClickHouse’s rapid enterprise adoption accelerated through 2024–2025, backed by major funding and feature expansion. Teams that require high-concurrency, low-latency analytics — especially for event streams, ad-hoc exploration, and real-time dashboards — choose ClickHouse for predictable performance and cost. That momentum means better tooling, more connectors, and a growing community ready to help with migrations.

Quick context: enterprise interest in ClickHouse surged after 2025 funding rounds and product maturity. That makes today a practical time to evaluate and migrate, provided you follow a disciplined plan.

High-level migration strategy (inverted pyramid)

1. Assess and prioritize — inventory workloads, SLAs, cost drivers.
2. Proof-of-Value — pick a representative pipeline and run a pilot.
3. Schema & query translation — map types, keys, and SQL dialects.
4. ETL rework — adapt ingestion, CDC, and transforms.
5. Validation & testing — reconcile, compare, and monitor.
6. Cutover — choose phased dual-write or full-switchover strategy.
7. Optimize & operate — tune MergeTree, compression, and observability.

Step 1 — Assess and prioritize (what to measure first)

Start with a precise inventory: list tables, row counts, daily inserts, query frequency, and top slow/expensive queries. Capture these attributes for prioritization:

• Top-100 queries by cumulative runtime and scans.
• Tables with heavy daily inserts or high cardinality keys.
• Schemas that use VARIANT/OBJECT/ARRAY and advanced SQL features.
• ETL jobs by frequency and complexity (hourly batch, streaming CDC).
• SLAs: query latency targets, freshness requirements, and retention needs.

Use Snowflake query & usage history along with explain plans to characterize which workloads are good first candidates. Prioritize read-heavy, analytical queries over tiny transactional workloads — ClickHouse excels at OLAP.

Step 2 — Proof-of-Value (PoV): pick a representative use case

Pick a single dashboard or analytics domain that represents your typical workload: moderate daily volume, some nested data, and a mix of aggregates & filters. Build a PoV that exercises:

• Bulk backfills and incremental loads.
• Typical SELECTs and long-window aggregations.
• JOIN patterns: small-dimension joins and large fact filters.

Measure raw performance, cost-per-query, and complexity of translating SQL. Keep the PoV scope deliberately small to gain quick wins.

Step 3 — Schema translation: mapping Snowflake types to ClickHouse

Schema translation is the heart of the migration. ClickHouse optimizes storage and retrieval differently — its table definitions are built around the MergeTree family and an ORDER BY key that drives sorting and locality.

Core mapping rules

• Snowflake NUMBER → ClickHouse Int/UInt/Decimal: choose precision-aware Decimal for monetary data; use Int64 for integer IDs.
• Snowflake FLOAT → ClickHouse Float32/Float64 (check rounding behavior).
• Snowflake TIMESTAMP / TIMESTAMP_TZ → ClickHouse DateTime64 (specify precision and timezone handling explicitly).
• Snowflake VARIANT / OBJECT / ARRAY → ClickHouse JSON functions or Nested types. For frequent queries on fields, extract columns into typed fields instead of keeping VARIANT as-is.
• Nullability: ClickHouse supports Nullable(T) — prefer explicit types to avoid unexpected null semantics.

Design the MergeTree key

ClickHouse does not have traditional primary keys; the ORDER BY clause defines physical sort order. Choose ORDER BY to enable range scans and efficient GROUP BYs. Typical patterns:

• Time-series data: ORDER BY (date, event_type, user_id)
• Event tables: ORDER BY (event_date, event_hour, event_id)

Also choose an appropriate index_granularity for the expected query patterns — higher granularity reduces index size but can increase read cost.

Example schema translation

-- Snowflake table (simplified)
CREATE TABLE events (
  event_id NUMBER(38),
  user_id VARCHAR,
  event_time TIMESTAMP_NTZ,
  properties VARIANT
);

-- ClickHouse translation
CREATE TABLE events (
  event_id UInt64,
  user_id String,
  event_time DateTime64(3),
  prop_campaign String, -- extracted field
  prop_revenue Decimal(18,2)
) ENGINE = MergeTree()
PARTITION BY toYYYYMM(event_time)
ORDER BY (event_time, user_id)
SETTINGS index_granularity = 8192;

Step 4 — Query translation: common SQL differences & examples

SQL dialects differ. Snowflake supports many analytical primitives and semi-structured conveniences; ClickHouse implements a rich set but with different names and semantics. Below are common translations and gotchas.

Window functions

ClickHouse supports window functions, but syntax variations and performance characteristics differ. For heavy windowed aggregations, test both window functions and array_agg + arrayJoin rewrites.

Semi-structured data

Snowflake's VARIANT is flexible; in ClickHouse you often need to extract frequently-used fields during ingestion for speed.

-- Snowflake
SELECT properties:campaign::STRING as campaign FROM events;

-- ClickHouse
SELECT JSONExtractString(properties, 'campaign') AS campaign FROM events;

Joins and subqueries

ClickHouse supports joins but behaves differently for large equality joins. Use JOIN only when the right side is small or pre-aggregated; otherwise prefer map-like approaches with dicts or pre-sharded distributed tables. Also, ClickHouse historically optimized for broadcast joins — test on production-like data.

Example complex translation

-- Snowflake
SELECT user_id,
  COUNT(DISTINCT session_id) as sessions
FROM events
WHERE event_time >= dateadd(day, -7, current_timestamp())
GROUP BY user_id;

-- ClickHouse
SELECT user_id,
  uniqExact(session_id) AS sessions
FROM events
WHERE event_time >= now() - INTERVAL 7 DAY
GROUP BY user_id;

Step 5 — ETL rework: ingestion, CDC and transformation patterns

ETL pipelines typically need the most rework. Whether you use batch loads, streaming CDC, or a modern ELT stack (dbt, Airflow, Dagster), adapt connectors and methods:

• Batch loads: use bulk INSERTs or ClickHouse's CSV/Parquet file ingestion via clickhouse-client or HTTP API; tune batch size (1–10M rows typical) to avoid many small files.
• Streaming/CDC: map your change stream to ClickHouse inserts. Use Kafka + ClickHouse Kafka engine with a consumer materialized view for exactly-once-like semantics, or use an external stream processor (Debezium → Kafka → consumer) with idempotent writes.
• Transformations: move complex transforms into an ETL layer or use dbt with a ClickHouse adapter; push down heavy aggregations to ClickHouse only when it improves end-to-end latency.

Idempotency and deduplication

ClickHouse historically lacks multi-statement transactions like Snowflake. Implement idempotent loads: use deduplication keys, ephemeral staging tables and MERGE-like logic (INSERT INTO … SELECT DISTINCT or use replacingMergeTree engine for dedupe via a version column).

Example CDC pattern

-- Kafka engine approach (simplified)
CREATE TABLE kafka_events (
  payload String
) ENGINE = Kafka()
SETTINGS kafka_broker_list = 'kafka:9092', kafka_topic = 'events';

CREATE MATERIALIZED VIEW mv_events TO events AS
SELECT
  JSONExtractUInt(payload, 'event_id') AS event_id,
  JSONExtractString(payload, 'user_id') AS user_id,
  parseDateTimeBestEffort(JSONExtractString(payload, 'event_time')) AS event_time
FROM kafka_events;

Step 6 — Validation & testing: reconciliation recipes

Validation is non-negotiable. Build automated tests at multiple levels:

• Row counts and totals: compare counts per day and key hashes between Snowflake and ClickHouse.
• Checksums: compute rolling checksums (e.g., cityHash64 or MD5 of concatenated key sets) to quickly detect mismatches.
• Sample-level comparisons: randomly sample rows and compare field-by-field after normalization.
• Business metrics reconciliation: compare top-level KPIs (DAU, revenue) using identical SQL logic.
• Performance and latency tests: measure 95th/99th latency of production queries.

Automate this with CI pipelines and data quality tools (Great Expectations, Soda, or dbt tests). Use a reconciliation cadence: every load, hourly summary, and nightly deep-compare.

Reconciliation SQL examples

-- Row count per partition
SELECT toYYYYMM(event_time) AS ym, count() as cnt FROM events GROUP BY ym ORDER BY ym;

-- Hash-sum per partition for quick diffs
SELECT toYYYYMM(event_time) AS ym, cityHash64(groupArray(concat(event_id, '::', user_id))) AS h FROM events GROUP BY ym;

Step 7 — Cutover strategies and fallbacks

Choose a strategy based on risk tolerance:

• Dual-write / shadow mode: write to both Snowflake and ClickHouse for a period, keep Snowflake as the source-of-truth, and monitor divergence. Low risk, slower to fully decommission Snowflake.
• Read switch with backfill: continue writing to Snowflake and backfill ClickHouse gradually; switch reads when validation is green.
• Blue-green: route a subset of user traffic or reports to ClickHouse and verify behavior before full cutover.
• Full switchover: only for small, well-tested workloads.

Maintain a rollback plan and ensure you can resume Snowflake reads quickly if critical mismatches appear. Keep snapshots of validation artifacts and reconciliation queries to speed troubleshooting.

Step 8 — Performance tuning and operations

After migration, focus on these operational areas:

• MergeTree tuning: set index_granularity, partitioning, and appropriate ORDER BY keys.
• Compression: choose codecs per column to trade storage vs CPU (ZSTD, LZ4, etc.).
• Sharding & replication: configure Distributed tables for scaling; use ReplicatedMergeTree for HA.
• Query optimization: use materialized views for precomputation and use the query_log for hotspots.
• Observability: capture system.metrics, query_log, and slow_query_log; integrate with Prometheus/Grafana.

Common pitfalls and how to avoid them

• Assuming 1:1 SQL feature parity — test every complex query; some functions or semantics differ.
• Underestimating schema design impact — ORDER BY choices change read costs dramatically.
• Poor deduplication strategy — can lead to inflated counts; use replacingMergeTree when appropriate.
• Ignoring streaming idempotency — dedupe at load or use consumer offsets and unique keys.
• Neglecting monitoring — ClickHouse requires active observability to catch long merges or replica lags early.

Operational checklist (pre-cutover)

1. Complete PoV and validate performance vs SLA.
2. Run automated reconciliation on 100% of new loads for at least one week.
3. Implement and test backfill and dedupe logic for historical data.
4. Configure backups, restores, and disaster recovery runbooks.
5. Train SRE/DBA teams on MergeTree internals and maintenance tasks.

Tooling and integrations to accelerate migration (2026)

By 2026 there are mature connectors and community tools to reduce friction. Look for:

• ClickHouse-native connectors for Kafka, Spark, Flink, and dbt.
• Data quality frameworks with ClickHouse stores (dbt+Great Expectations patterns).
• Managed ClickHouse offerings with autoscaling and backup integrations if you want to reduce operational load.

Case study snippet (example migration path)

A mid-size ad-tech company moved 40TB of event data from Snowflake to ClickHouse in 2025–2026. They followed this plan:

1. Inventory and prioritize (top 20 dashboards first).
2. PoV on one dashboard; measured 3–5x lower tail latency in ClickHouse.
3. Implemented dual-write for 30 days with automated reconciliation; fixed field-level mismatches from VARIANT parsing.
4. Cut reads progressively by traffic slice using feature flags; fully decommissioned Snowflake for analytics after 90 days.

Lessons learned: invest in schema extraction for JSON fields early and set expectations about join patterns and dedupe logic.

Advanced strategies and future-proofing

For teams planning beyond migration:

• Monitor for emerging ClickHouse features: vector search and advanced indexing became more common in late 2025 — evaluate them for embedding storage if your ML workloads require fast similarity queries.
• Design ETL as modular, testable stages to support future backend swaps with minimal rework.
• Adopt data contracts and schema registries to prevent silent breakages when event shapes evolve.

Checklist: quick-run migration summary

• Inventory workloads and select PoV.
• Map Snowflake types to ClickHouse types and design ORDER BY keys.
• Adapt ETL: batch, streaming, or CDC — ensure idempotency.
• Translate critical queries and test performance at scale.
• Automate reconciliation: counts, checksums, and KPIs.
• Choose cutover: dual-write, read switch, or blue-green.
• Tune MergeTree, partitioning, and observability post-cutover.

Final words — avoid surprises by testing early and often

Migrating analytics from Snowflake to ClickHouse is highly rewarding — lower query costs and predictable, low-latency analytics — but it requires careful planning across schema design, query translation, ETL architecture, and validation. Prioritize representative pilots, automate reconciliation, and keep the old system in read-only standby until your tests prove parity.

Rule of thumb: invest the time upfront to translate and extract frequently queried fields — this yields the biggest performance and validation wins.

Actionable next steps (30/60/90 day plan)

1. 30 days: Inventory, select PoV, and spin up a ClickHouse test cluster. Translate 10 top queries and run load tests.
2. 60 days: Implement ETL adapters, start dual-writes for the PoV, and set up automated reconciliation and monitoring dashboards.
3. 90 days: Extend dual-write to additional domains, run full backfills, and prepare for phased cutover based on validation success.

Resources and templates

Suggested tools: dbt ClickHouse adapter, clickhouse-client, Kafka engines, Great Expectations, Prometheus/Grafana. Use a standard validation suite (counts, checksums, KPI diffs) as a CI job before enabling production reads.

Call to action

If you’re planning a migration, start with a 1-week PoV: pick one dashboard, run the schema translation steps above, and run an end-to-end reconciliation. Need help designing the PoV or the reconciliation suite? Contact our engineering team for a migration assessment and a customizable checklist tailored to your stack.

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