migrationdatabasesETL

Migrating CI Artifacts from Snowflake-style Warehouses to ClickHouse

UUnknown

2026-02-27

10 min read

A 2026 migration playbook: move artifact analytics from Snowflake to ClickHouse with schema, ingestion, query, and rollback strategies.

Stop slow artifact analytics and brittle CI pipelines — a practical playbook for migration from Snowflake to ClickHouse

Hook: If your teams are frustrated by long Snowflake query times, expensive storage for high-cardinality telemetry, or slow artifact distribution analytics that block release velocity, migrating analytic workloads to ClickHouse in 2026 can deliver sub-second analytics and far lower TCO — but only with a careful plan. This playbook walks you step‑by‑step through schema translation, query rewrites, ingestion patterns, and robust rollback strategies that production teams can apply today.

Why migrate in 2026? Trends that matter

ClickHouse's ecosystem matured rapidly through late 2024–2025: major fundraising rounds and enterprise adoption accelerated engineering investment and connector support. Notably, ClickHouse Inc.'s late‑2025 funding validated the platform as a serious Snowflake challenger and spurred improvements in connectors, JSON handling, and cluster orchestration. That makes 2026 the right time to evaluate migration if your workloads need:

Consistently low-latency OLAP queries for high-cardinality telemetry and artifact analytics
Cost-effective storage for frequently queried historical datasets
Open-source friendly connectors for Kafka, CDC, and modern ETL frameworks

High-level migration phases (one-line playbook)

Assess — inventory schemas, queries, SLA, and data volumes
Pilot — run a subset of tables and queries in ClickHouse
Design — map schemas and engines, design ORDER BY/partitioning
Ingest — implement CDC or batch pipelines and backfills
Rewrite — translate SQL and validate results
Cutover — dual-write, canary, then switch reads to ClickHouse
Rollback — revert to Snowflake via pre-defined strategies

Phase 1 — Assess: what to inventory

Start with a lightweight audit that answers these questions:

Which tables are read-heavy vs write-heavy? (artifact metadata vs ingestion events)
Which queries must be sub-second? Which can be batched?
Data volumes: daily inserts, retention window, cardinality per column
Security & compliance requirements (encryption, PII handling, audits)
Existing ETL/CDC stack: Kafka, Debezium, Snowpipe, Airflow, dbt, etc.

Phase 2 — Pilot: pick a low-risk domain

For artifact and analytics workloads, telemetry for artifact downloads or release pipelines is a good pilot because it is high volume but typically non-critical for transactional systems. Plan the pilot to include:

1–3 tables representing different shapes: high-cardinality events, JSON-rich rows, and aggregated counters
A set of 10–20 representative queries — ad hoc analytics and dashboards
Instrumentation for correctness and latency comparison vs Snowflake

Schema translation: Snowflake -> ClickHouse

Snowflake and ClickHouse diverge in engine models and data typing. ClickHouse is columnar with table engines and an ORDER BY that acts as your physical sort key. Translating schemas focuses on datatypes, primary/ordering keys, partitioning, and engine choice.

Common datatype mapping

Snowflake TIMESTAMP_NTZ / TIMESTAMP_LTZ -> ClickHouse DateTime64(3) or DateTime64(6) (choose precision required)
Snowflake VARCHAR -> ClickHouse String
Snowflake NUMBER(p,s) -> ClickHouse Decimal(p,s) or Float64 (for analytics, prefer Decimal for money)
Snowflake BOOLEAN -> ClickHouse UInt8 (0/1) or Nullable(UInt8)
Snowflake VARIANT (JSON) -> ClickHouse String + JSON functions; where heavy JSON querying needed, extract fields to typed columns or use nested/Array(T) columns

Choose the right engine

Select a table engine based on write pattern and query shape:

MergeTree family (ReplacingMergeTree, SummingMergeTree, AggregatingMergeTree) — general-purpose OLAP use.
Distributed — logical distributed table that points to MergeTree replicas on shards.
Buffer engine — can smooth spikes for bursty ingestion before committing to MergeTree.
Kafka engine — for real-time reads from Kafka topics and immediate materialized views.

ORDER BY and partition key design

ClickHouse does not use a conventional primary key; ORDER BY controls data sorting and index granularity. Design ORDER BY to match the queries you run most:

Time-series queries -> ORDER BY (event_date, some_id)
High-cardinality joins -> ORDER BY (user_id, event_date) if most queries filter by user
For daily partitioning, use PARTITION BY to create month/day partitions: PARTITION BY toYYYYMM(event_date)

Query rewrites: practical patterns & examples

Translate SQL with a focus on semantics and performance. Below are common Snowflake SQL constructs and how to implement them in ClickHouse.

QUALIFY and window filters

Snowflake example:

SELECT * FROM events
WHERE event_type = 'download'
QUALIFY ROW_NUMBER() OVER (PARTITION BY artifact_id ORDER BY ts DESC) = 1;

ClickHouse equivalent:

SELECT * FROM (
  SELECT *, row_number() OVER (PARTITION BY artifact_id ORDER BY ts DESC) as rn
  FROM events
  WHERE event_type = 'download'
)
WHERE rn = 1;

Note: ClickHouse supports window functions but sometimes a GROUP BY + anyHeavy/argMax is more performant:

SELECT
  artifact_id,
  argMax(ts, ts) as latest_ts,
  argMax(user_id, ts) as last_user
FROM events
WHERE event_type = 'download'
GROUP BY artifact_id;

VARIANT / JSON handling and lateral flatten

Snowflake's VARIANT + FLATTEN is often used to expand arrays. In ClickHouse use arrayJoin and JSONExtract* functions or pre-extract fields during ingestion.

-- Snowflake
SELECT v.value:filename
FROM artifacts, LATERAL FLATTEN(input => metadata.files) v;

-- ClickHouse approach (if metadata is JSON string)
SELECT JSONExtractString(file, 'filename') AS filename
FROM artifacts
ARRAY JOIN JSONExtractArrayRaw(metadata, 'files') AS file;

Time travel and cloning

Snowflake features like time travel and zero-copy cloning have no direct ClickHouse equivalent. Implement these patterns instead:

Keep a versioned table or add valid_from/valid_to columns for point-in-time queries
Use clickhouse-backup or snapshot tools for fast backups and restores

Ingestion pipelines: batch, CDC, and streaming

Common ingestion patterns when replacing Snowpipe or bulk loads:

Batch (backfill and bulk loads)

For initial backfills, export Snowflake data into Parquet/CSV files and use ClickHouse's clickhouse-client HTTP interface or clickhouse-local:

# Example using curl to insert CSV
curl -sS 'http://clickhouse-host:8123/?query=INSERT+INTO+artifacts+FORMAT+CSV' --data-binary @artifacts.csv

Streaming & CDC

For near real-time ingestion, these patterns are reliable in 2026:

Kafka + ClickHouse Kafka engine → materialized views to MERGE into MergeTree tables
Debezium + Kafka + ClickHouse sink — CDC for low-latency replication from transactional DBs
Airbyte / Fivetran connectors — for managed CDC into Kafka or direct ClickHouse sinks (confirm vendor support & latency)

Example materialized view from Kafka engine:

CREATE TABLE kafka_events (
    key String,
    value String
  ) ENGINE = Kafka SETTINGS
    kafka_broker_list = 'kafka:9092',
    kafka_topic_list = 'artifact-events',
    kafka_group_name = 'ch-consumer',
    format = 'JSONEachRow';

  CREATE TABLE events_mv ENGINE = MergeTree()
    PARTITION BY toYYYYMM(event_date)
    ORDER BY (artifact_id, event_date)
  AS SELECT
    JSONExtractString(value, 'artifact_id') AS artifact_id,
    toDateTime(JSONExtractString(value, 'ts')) AS event_date,
    JSONExtractString(value, 'event_type') AS event_type
  FROM kafka_events;

Data validation & testing

Run these checks during pilot and after any batch backfill or cutover:

Row-count diff per logical partition (daily/hourly)
Checksum/hash of key fields between Snowflake and ClickHouse samples
Query result comparisons for representative analytics (tolerate floating point diffs)
End-to-end dashboard comparing latency and cost metrics

Performance tuning: practical knobs

ORDER BY is the most important design choice — optimize for your most selective filters
Tune index_granularity smaller for highly selective queries, larger for scan-efficient analytics
Use TTL policies to auto-drop or move old data to cheaper disks
Run OPTIMIZE FINAL sparingly for compaction after large backfills

Rollback strategies and safety nets

Every migration must assume failure modes. Prepare multiple rollback paths:

1) Dual-write and shadow reads

Start by writing to both Snowflake and ClickHouse in your ingestion layer. Use feature flags or routing logic in the application or API gateway to route a subset of reads to ClickHouse for validation (canary). This allows instant cutback to Snowflake by stopping reads to ClickHouse.

2) Read fallback at query layer

Implement a transparent fallback: query ClickHouse first and, if result is missing or stale, perform a Snowflake read. Example (pseudo):

result = query_clickhouse(q)
if result.empty or result.timestamp < watermark:
  result = query_snowflake(q)

3) Replay/backfill from event stream

If you detect inconsistency, replay events from Kafka or CDC logs to rebuild ClickHouse tables. Keep retention for Kafka topics or use an archive.

4) Snapshot restore

For catastrophic failures, use stored backups. Popular tools: clickhouse-backup for S3-compatible snapshots. Test restores regularly in a staging environment.

5) Soft-deletes & versioned rows

Add a soft_deleted or valid_from/valid_to approach to enable easy toggling of datasets without immediate physical delete.

Operational checklist before cutover

Agree SLA: acceptable lag, error budget, and query latencies
Prove parity: checksums and query result agreement across 99th-percentile queries
Implement monitoring: ClickHouse metrics (system.metrics), Grafana dashboards, alerting for replication lag, merge failures
Smoke test dashboards and CI jobs that depend on analytics data
Document rollback triggers and owners with runbooks

CI/CD & automation patterns

Make schema changes and backfills part of CI/CD. Example pipeline steps using GitHub Actions or GitLab:

PR with SQL migration files and dbt models (dbt-clickhouse adapter)
Run clickhouse-client --query in a staging environment for dry-run
Apply migrations and run incremental backfill job
Run validation suite comparing sample rows to Snowflake

# example shell step: apply migration
clickhouse-client --host=staging-ch --multiquery < migrations/2026_01_create_artifacts.sql
# run backfill
python pipelines/backfill_artifacts.py --from 2025-01-01 --to 2026-01-01

Case study: Artifact analytics migration (fictionalized, realistic)

Context: AcmeCI (500 engineers) used Snowflake for release artifact telemetry. Queries for per-build artifact popularity and geo-distribution were expensive and dashboards refreshed every 15 minutes. After a pilot, AcmeCI moved event ingestion to Kafka → ClickHouse and replaced their heavy Snowflake aggregate tables with MergeTree tables ordered by (artifact_id, event_time). Results:

99th percentile query latency dropped from 12 seconds to 280ms
Monthly analytics spend decreased by ~55%
Developers shipped new artifact-level metrics weekly thanks to faster iteration

Lessons learned:

Pre-extract critical JSON fields during ingestion — querying raw JSON is slower
Design ORDER BY for high-cardinality filters (artifact_id first)
Run a replayable CDC pipeline for safe backfills and reprocessing

Security, governance, and observability

Ensure encryption in transit and at rest, RBAC via ClickHouse users and external auth (LDAP/OAuth), and audit logging to capture sensitive operations. For governance, export schema definitions and table metadata into your catalog (DataHub, Amundsen) and keep lineage for ETL jobs.

Common pitfalls & how to avoid them

Ignoring ORDER BY design — leads to poor query performance. Prototype with realistic query shapes.
Overusing JSON — extract hot fields into typed columns for aggregation and joins.
No rollback plan — always have replayable sources and tested backups.
Underestimating compaction after backfills — schedule OPTIMIZE and monitor merges.

Advanced strategies and future-proofing (2026+)

As ClickHouse evolves, expect improved user-defined functions, enhanced JSON-native types, and more managed connectors. Architect with these future-proof patterns:

Keep event streams canonical and single source of truth (Kafka/GCS/Parquet)
Use column-level ingestion transforms so downstream engines can evolve
Automate table lifecycle policies (TTL + tiered storage) to control costs

Actionable takeaways (ready checklist)

Inventory top 50 queries and classify them by SLA
Pick 1–3 pilot tables: event stream, JSON-heavy metadata, aggregated counters
Design ORDER BY to match the most frequent filters and joins
Implement Kafka→ClickHouse materialized views for real-time ingest and a replayable backfill path
Enable dual-write and implement read-fallback for a safe cutover
Test backup & restore with clickhouse-backup before going to production

"Migrate the data path, not the operational headaches." — practical advice when moving OLAP workloads in 2026

Final checklist before switching reads to ClickHouse

Parity tests pass for critical queries
Alerts for replication lag and backfill failures are active
Rollback runbook and owner assigned
Monitoring dashboards validated and shared with on-call

Call to action

If you're planning a migration this quarter, start with a focused pilot and a replayable ingestion path. Need a pre-built checklist, sample Kafka→ClickHouse configs, and a tailored ROI estimate for artifact analytics? Contact binaries.live for a migration audit and a 2-week pilot blueprint that maps your Snowflake workloads to ClickHouse with tested rollback procedures.

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