Migrating Analytics Workloads to ClickHouse: A Practical Migration Checklist

Hook: Why your analytics team is considering a move from Snowflake/Redshift to ClickHouse

If your engineering team is struggling with exploding cloud costs, unpredictable query latency, or the need for sub-second OLAP for dashboards and analytics, you’re not alone. Over the past 18 months the market has shifted: ClickHouse has matured into a mainstream OLAP platform and entered the enterprise conversation as a credible Snowflake alternative. In 2025–2026 the ecosystem accelerated — managed ClickHouse services, dbt adapters, Kafka/CDC integrations, and operator tooling all improved — making migrations realistic for production analytics workloads.

What this guide delivers (fast)

This is a stepwise, pragmatic checklist and playbook for teams migrating analytics from Snowflake or Amazon Redshift to ClickHouse in 2026. You'll get a concrete migration plan covering: schema translation, ETL/ELT changes, benchmarking and validation, cost optimization, common pitfalls, and post-cutover operational practices.

Quick overview: When ClickHouse makes sense

• High-concurrency analytics where sub-second or low-second query latency matters for dashboards and embedded analytics.
• Large-scale time-series or event data where ClickHouse compression and MergeTree engines reduce storage costs.
• Cost-sensitive teams that can operate self-hosted clusters or use managed ClickHouse Cloud to reduce per-query costs vs a heavy Snowflake bill.
• Near-real-time ingestion requirements — ClickHouse integrates well with Kafka, CDC tools, and streaming ETL.

High-level migration phases

1. Discovery & sizing
2. Proof-of-concept (PoC) & benchmarking
3. Schema translation & ETL redesign
4. Parallel run, validation & cutover
5. Operate, optimize & iterate

Phase 1 — Discovery & sizing (what to measure first)

Start with measurement. You can’t migrate effectively without knowing which queries, tables, and pipelines drive cost and SLAs.

• Collect the top 100 queries by volume and cost (credits/CPU/time) in your Snowflake/Redshift account.
• Identify heavy tables by size, cardinality, and ingest rate. Tag those that are time-series or append-only (good fits for ClickHouse).
• Catalog transformations (dbt models, stored procedures, user-defined functions) and third-party integrations.
• Define SLOs for query latency and freshness. Determine expected concurrency at peak dashboard times.

Phase 2 — PoC & benchmarking (how to compare apples-to-apples)

Benchmarks must reflect real workloads. Don't rely solely on vendor numbers. Design tests that mimic your queries and concurrency profile.

1. Choose representative tables and queries (include filters, joins, window functions, aggregations).
2. Load the dataset: use scaled-down copies (e.g., 100GB/1TB) and then scale to full production for final tests.
3. Metrics to capture: 99th/50th percentile latency, throughput (queries/sec), CPU, memory, disk I/O, and cost per query (or cost/hour for managed services).
4. Tools: ClickHouse comes with clickhouse-benchmark; use JMeter or k6 for concurrency; use query logs from Snowflake/Redshift for trace replay.

Benchmark tip: Focus on tail latency under concurrent load — dashboards and BI are sensitive to the 95–99th percentile, not just averages.

Phase 3 — Schema translation: mapping concepts and data types

Schema translation is the most delicate part of migration. ClickHouse is a columnar OLAP engine optimized for append-heavy workloads and analytical queries — its schema and storage semantics differ from Snowflake/Redshift.

Data type mapping (practical mappings)

• Semi-structured: Snowflake VARIANT & Redshift SUPER → ClickHouse JSON functions (JSONExtract/JSONExtractRaw) or Nested / Tuple / Array types for structured arrays/objects.
• Timestamps: Snowflake TIMESTAMP_TZ / Redshift TIMESTAMP → ClickHouse DateTime / DateTime64 (choose precision). Use Date for partition-friendly date columns.
• Nullable: ClickHouse supports Nullable(T). Convert nullable columns explicitly; avoid making high-cardinality columns nullable unless required (nullable increases storage and CPU costs).
• Strings: VARCHAR/STRING → String. Consider dictionary encoding for low-cardinality strings via compression settings.
• Decimals: Fixed-point → Decimal(P,S) is supported, but be cautious with very large precision values — re-evaluate if float64 is acceptable for analytics.

Primary keys, uniqueness, and constraints

ClickHouse does not implement traditional relational constraints (no enforced foreign keys, unique constraints in the same way). Instead rely on application logic or materialized views to enforce uniqueness where necessary. For storage and query efficiency, design a good primary key/sorting key for MergeTree tables — it’s crucial for data pruning and performance.

Engine choices: MergeTree family and when to use them

• MergeTree — general purpose. Choose primary key columns that support query filters (time + high-cardinality dimension is common).
• ReplacingMergeTree — use for upserts and deduplication by sign or version column.
• SummingMergeTree / AggregatingMergeTree — useful for roll-ups where you can pre-aggregate on insert.
• CollapsingMergeTree — for event-driven insert/delete semantics (requires care).

Phase 4 — ETL/ELT redesign: move from Snowflake/Redshift patterns to ClickHouse patterns

ClickHouse changes the optimal placement of transforms. You’ll likely move from heavy, multi-stage ELT in the warehouse to a mix of streaming ingestion, lightweight transforms, and materialized views.

Ingestion patterns

• Batch loads via INSERT or using S3 table functions for large historical backfills.
• Streaming ingestion: use Kafka engine, Materialized Views, or third-party CDC (Debezium, Maxwell, Airbyte) into ClickHouse for near-real-time. For practical CDC and migration patterns see a relevant organiser migration case study.
• Data validation: add lightweight checks during ingestion (row counts, min/max timestamps) to catch schema drift early.

Transform placement

• Prefer ELT where transformations are in ClickHouse (materialized views and aggregation tables) for low-latency dashboards.
• Keep complex business logic in dbt or an upstream transformation system when repeatability and testing are required. The dbt ClickHouse adapter matured through 2025–2026 and can be used for model portability.
• Use materialized views for pre-aggregations and to avoid recomputing heavy joins at query time.

Phase 5 — Parallel run, validation, and cutover

Before switching BI or downstream consumers, run in parallel and validate results carefully.

• Dual-write for a period: write events into both Snowflake/Redshift and ClickHouse to validate parity.
• Row-level checksums and sample-based consistency checks: compare counts, aggregates, and key distributions.
• Run your top 100 dashboard queries against both systems and compare latencies and result consistency.
• Progressive cutover: move read-only dashboards first, then reporting workloads, then operational analytics.

Benchmarks: a practical benchmarking plan

Design benchmark suites that measure:

• Simple aggregations over wide time ranges (typical dashboard queries).
• High-cardinality joins and multi-way joins at scale.
• Window functions and analytical functions used by your models.
• Mixed read/write concurrency to model real production.

Collect these metrics per run: P50/P95/P99 latency, CPU/memory utilization, disk throughput, and cost per query/hour. Repeat runs at different cluster sizes and data scales to find the sweet spot for cost vs. performance.

Cost considerations & optimization strategies

ClickHouse can be significantly cheaper than managed warehouses if you optimize storage and compute properly — but misconfiguration or poor designs can erase savings.

Main cost drivers

• Storage (SSD or cloud object store for external volumes)
• Compute (CPU-heavy aggregations and peaks)
• Replication and replica count (higher replication = higher storage & network costs)
• Network egress for cross-region reads and S3 traffic

Practical cost optimizations

• Choose the right MergeTree primary key for effective data pruning — better pruning = fewer I/O scans = lower compute.
• Compress aggressively: ClickHouse column codecs (LZ4, ZSTD) give high compression; use ZSTD for better ratios at acceptable CPU cost.
• Partition by date and use TTLs to drop or move cold data to cheaper storage tiers automatically.
• Downsample historical data: keep high-resolution for recent windows and roll up older data into aggregates.
• Use external storage policies (S3) for very large cold datasets and cache hot partitions on SSDs.
• Leverage materialized views to avoid repeating heavy computations on demand.

Operational differences and what to watch for

Operationally ClickHouse is different from Snowflake/Redshift. Teams must plan for new operational responsibilities if self-hosting, or evaluate managed offerings.

• Backups & restore: Plan for object-store snapshots and regular checks. ClickHouse supports table-level backups to S3; test restores.
• Monitoring: Track MergeTree merges, parts count, long-running merges, disk pressure, and network saturation. Field reviews of edge cache and observability appliances can help inform your monitoring stack: ByteCache field review.
• Scaling: Horizontal scaling requires sharding and distributed tables; plan shard keys carefully to avoid hotspots. See architectures for low‑latency, sharded topologies.
• Security & governance: Implement RBAC, encryption, audit logging, and integrate with your IAM. Factor in regional rules like EU data residency requirements when choosing managed vs self-hosted.

Common pitfalls and how to avoid them

• Pitfall: Treating ClickHouse like a row-store. Fix: Redesign schema and queries to leverage columnar scans and projections.
• Pitfall: Using poor primary keys leading to full scans. Fix: Analyze query predicates and choose sorting keys that enable efficient range pruning.
• Pitfall: Overusing Nullable on high-cardinality columns. Fix: Denormalize or add sentinel values where possible.
• Pitfall: Expecting transactional (ACID) semantics. Fix: Rework upsert/delete logic using ReplacingMergeTree or use careful event-sourcing patterns.
• Pitfall: Underprovisioning memory for merges and queries. Fix: Baseline memory needs in PoC and size nodes for steady-state merges, not just peak concurrency.

Real-world checklist: step-by-step migration tasks

1. Inventory — list tables, sizes, queries, and ETL jobs.
2. Define SLOs — latency, freshness, concurrency, and cost targets.
3. Set up PoC — small cluster or managed instance; replicate a slice of production data.
4. Translate schema — map types, design MergeTree keys, and create distributed tables if sharding.
5. Adapt ETL — implement Kafka/CDC for streaming, refactor dbt models, or create materialized views.
6. Benchmark — run query and ingest benchmarks under realistic concurrency.
7. Validate — row counts, checksums, and business metric parity.
8. Dual-write — run both systems in parallel for a cutover window.
9. Cutover — route BI reads to ClickHouse incrementally and monitor.
10. Optimize — iterate on partitioning, compression, and materialized views based on observed workloads.

2026 trends you should factor into your migration decision

• Enterprise adoption: After significant funding and product maturations in 2025, ClickHouse has stronger enterprise features, improved managed cloud offerings, and higher ecosystem maturity in early 2026.
• Tooling parity: dbt adapters, BI connectors, and CDC integrations have stabilized, lowering the friction of migration compared to 2023–2024.
• Hybrid patterns: Teams increasingly adopt hybrid lakehouse architectures — ClickHouse as a fast OLAP serving layer over long-term object storage.
• AI/analytics convergence: Real-time feature stores and low-latency analytics are becoming a common use case; ClickHouse is often chosen for serving features in production ML pipelines. For predictions about product stacks and convergence read broader platform and product trends.

Actionable takeaways

• Start with a focused PoC around the handful of dashboards and queries that matter most — prove latency and cost improvements before a full migration.
• Invest time in choosing MergeTree primary keys — they drive most performance and cost outcomes.
• Plan for operation: monitoring, backups, merges, and shard balancing are new responsibilities for self-hosted clusters.
• Use TTLs, downsampling, and external storage to control long-term costs at scale.

Appendix: quick schema mapping cheat sheet

• Snowflake VARIANT → ClickHouse JSON functions or Nested/Array
• TIMESTAMP / TIMESTAMP_TZ → DateTime64 / DateTime
• NUMBER/DECIMAL → Decimal(P,S) or Float64 (careful with precision)
• BOOLEAN → UInt8 or LowCardinality(String) depending on usage
• VARCHAR → String (use dictionary compression for low-card columns)

Final checklist before flipping the switch

1. All critical queries tested and within SLOs.
2. ETL/CDC pipelines running stable in dual-write mode.
3. Monitoring alerts configured for merges, disk, and long queries.
4. Backup and restore tested end-to-end.
5. Teams trained on new operational tasks and runbooks available.

Closing — Why migrate now (and how to decide)

In 2026, ClickHouse is no longer a niche columnar engine — it’s a mature platform with enterprise features, managed cloud options, and a growing ecosystem. For teams fighting rising cloud warehouse bills or needing sub-second analytics, migrating can deliver big wins — but only if you approach it methodically.

Use the checklist above: measure first, PoC second, then translate schema and ETL with an eye toward ClickHouse's strengths (columnar compression, MergeTree, materialized views, streaming ingestion). Benchmark under production-like concurrency and validate metric parity before cutover.

Call to action

Ready to evaluate ClickHouse for your workloads? Start a focused PoC with the top 10 queries that drive your costs and dashboards. If you want a migration template tailored to your environment (schema mappings, recommended MergeTree keys, and a benchmark plan), download our free ClickHouse migration checklist and sample dbt models for ClickHouse.

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