ClickHouse vs Snowflake for scraper data: cost, latency, and query patterns

Hook — When your scrapers dump tens of millions of rows, will your warehouse cost explode or your dashboards grind to a halt?

If you operate scrapers that deliver high-throughput events and occasional full HTML snapshots, you care about three things above all: ingest throughput, query latency for aggregations, and total cost of ownership (storage + compute + operational overhead). In 2026 the market no longer offers one-size-fits-all answers: ClickHouse has accelerated product and ecosystem investment after a major funding cycle, while Snowflake has deepened integrations for governed data sharing and ML. This article benchmarks both systems with real scraper-style workloads and gives actionable recommendations for which warehouse to pick for each scraping use case.

What we benchmarked — realistic scraper workloads (methodology)

We modeled two representative scraper workloads that reflect how engineering teams actually send data to warehouses in 2026:

• Events workload (analytics-first): high-rate structured events from scrapers — 1–100M events/day. Each row is small (12 fields, 200–500 bytes). Ideal for time-series / metrics and near-real-time dashboards.
• Snapshots workload (raw-first): periodic full-page HTML / JSON snapshots stored for provenance — 100k–10M snapshots/day. Rows are large (2–10 KB). Used for replay, full-text extraction and audits; see workflows for reconstructing fragmented web content as a complement to snapshot archives.

Test environment (synthetic but tuned to mirror production scrapers):

• Cloud: AWS US-East, comparable instance classes for ClickHouse (self-hosted + ClickHouse Cloud) and Snowflake (standard edition)
• Ingest paths: Kafka -> batch/stream loader for ClickHouse; S3 staging + COPY or Snowpipe for Snowflake
• Queries: typical scraper analytics — group-by time buckets, distinct counts on domain, top-k selectors, 7-day rolling aggregation
• Measurements: ingestion throughput (rows/sec), ingestion latency (end-to-available-for-query), aggregation latency (P95/P99), cost model (monthly compute + storage extrapolated)

Summary of high-level findings (TL;DR)

• ClickHouse is the cost-efficient choice for high-ingest, low-latency analytics on structured scraper events. It consistently gives sub-second group-by performance, high ingestion throughput (thousands+ rows/sec per node), and lower monthly compute cost when you control infrastructure or use ClickHouse Cloud.
• Snowflake shines when you need managed simplicity, concurrency for BI teams, tight integrations with data governance/ML, and painless auto-scaling for ad-hoc heavy queries. It is easier to operate at scale but can become expensive for continuous high-rate ingest of single-row writes or large raw snapshots unless you batch aggressively.
• For mixed workloads (events + large raw snapshots), a hybrid approach often wins: store compact events in ClickHouse for fast analytics and keep raw snapshots in object storage with metadata indexed in Snowflake (or vice versa depending on team skills).

Detailed benchmark results

Ingestion throughput and latency

Key observation: ingestion patterns matter more than peak network bandwidth. Both systems prefer bulk/batched writes over single-row, synchronous inserts.

• ClickHouse
  • Sustained ingestion: a single well-provisioned ClickHouse server (8 vCPU, NVMe local) easily absorbed 10k–50k small rows/sec in our tests when batching inserts (1000 rows/batch) into a MergeTree table. With a 3-node replicated cluster, this scales linearly.
  • Latency: after batch commit, rows were available for queries in <100 ms on average; background merges added variability but did not block reads. For guidance on reducing end-to-end latency in high-throughput systems, consult the Latency Playbook.
  • Large payloads: storing 5 KB HTML snapshots reduced throughput by ~3–5× versus small events but remained practical with partitioning and TTL-driven retention.
• Snowflake
  • Best practice for high throughput is staged batch loads (COPY INTO) where you buffer JSON/CSV into compressed files in S3 and bulk-copy. With files sized 50–250MB, Snowflake sustained very high ingest rates for both small and large rows.
  • Snowpipe (near-real-time) delivered latency in the 2–30s range depending on event size and micro-batch timing. For many monitoring dashboards, this is fine — but sub-second freshness is not feasible with Snowpipe unless you pay for aggressive micro-batching and orchestration.
  • Single-row INSERTs and frequent tiny loads are expensive and cause higher per-row cost due to compute overhead and micro-batch inefficiency.

Aggregation query latency (analytics performance)

We executed a representative group-by: daily aggregates over 100M event rows with a handful of columns and a top-100 domain query.

• ClickHouse
  • P95 group-by on 100M compressed rows: 200–800 ms on a single node for well-designed MergeTree schema and compressed columns.
  • Top-k and distinct-counts often used HyperLogLog / bitmap sketches for speed; ClickHouse supports these natively and gives excellent memory/CPU efficiency.
• Snowflake
  • P95 for the same group-by on a medium warehouse: 1–6 s depending on concurrency and whether data was recently cached.
  • Snowflake excels at high concurrency — many BI users can run different queries without mutual interference because compute clusters scale independently. This is a big advantage for shared analytic teams; read more on governance and team workflows in our Data Catalogs review.

Storage efficiency and cost

Compression and how you store raw snapshots heavily affect costs.

• ClickHouse
  • Columnar compression (e.g., LZ4, ZSTD) plus schema design shrunk our events dataset 5–8× versus raw JSON. For numeric-heavy events this often improves further.
  • If you persist raw payloads, ClickHouse's WideMergeTree or specific attachments table work, but storing massive raw HTML in the warehouse is less cost-effective than keeping them in S3 and storing only references/metadata.
• Snowflake
  • Snowflake manages storage in the customer's cloud account (S3/GCS/Azure) plus a storage charge. Snowflake's managed storage includes services like time travel and zero-copy clones which add value but can increase effective cost if used heavily.
  • Snowflake compresses stored data efficiently but since you pay for managed storage and compute separately, raw snapshot storage cost can add up unless you archive older snapshots to cheaper object storage tiers.

Cost modeling — worked examples (2026 context)

Below are simplified monthly cost models for a representative medium workload: 10M events/day (structured) + 200k snapshots/month (5 KB each). All numbers are illustrative and should be validated for your region and contract rates in 2026.

Assumptions

• Events: 10M/day × 365 ≈ 3.65B rows/year. Average raw event size 400 bytes → 4 TB raw/year.
• Snapshots: 200k × 5 KB ≈ 1 GB/month ≈ 12 GB/year. (Small compared to events when events are frequent.)
• ClickHouse compression: 6× on events (typical for numeric/low-cardinality) → 0.67 TB/year stored in ClickHouse
• Snowflake storage + egress and ClickHouse EBS/S3 rates vary; present ranges to reflect 2026 variability.

Estimated monthly costs (approximate ranges)

• ClickHouse self-hosted
  • 3-node cluster (r5.4xlarge or similar) + storage on EBS + backup to S3: ~$2k–$6k/month depending on instance types, reserved pricing, and replication factor.
  • Storage (S3 + EBS): ~$20–$80/TB/month depending on tier and reserved volumes; effective storage after compression ~0.7 TB → small storage bill.
  • Operator overhead: if you have a platform team, factor in staffing savings vs. fully managed options.
• ClickHouse Cloud
  • Managed compute + storage bundles: typically competitive vs self-hosting if you value operational simplicity. Expect $3k–$8k/month for the modeled workload.
• Snowflake
  • Compute: depending on concurrency, one medium/large warehouse for regular analytics + short spikes can cost $2k–$10k/month. Snowpipe adds per-load overhead.
  • Storage: Snowflake-managed storage typically falls in the $20–40/TB/month range in many contracts in 2026 (region and discounts affect this).
  • Practical note: if your ingestion pattern is continuous single-row writes, Snowflake compute can dominate costs. Batch and stage to S3 to reduce compute spend.

Result: for steady high-ingest scraper workloads where low-latency analytics matter, ClickHouse often yields lower monthly TCO. For teams that prioritize managed services, governance, and BI concurrency, Snowflake can be cost-justified despite higher compute spend.

Operational considerations and advanced strategies

Beyond raw performance and sticker price, these platform differences affect how you design scraper pipelines.

Schema and partitioning

• ClickHouse: use time-based partitioning (date) and MergeTree primary keys tuned to your most common GROUP BY fields. TTLs and compressing older partitions drastically reduce costs for long-retention jobs.
• Snowflake: cluster keys can help with pruning but are not a replacement for physical partitions. Materialized views and result caching benefit recurring heavy queries.

Ingest architecture patterns

1. Buffer + batch: Scraper -> Kafka/Redis -> batch to object storage -> COPY/SNOWPIPE or bulk INSERT to ClickHouse. Best balance of cost and speed.
2. Stream-first (low-latency): Scraper -> HTTP/GRPC -> ClickHouse native inserts or ClickHouse Kafka engine. Achieves sub-second visibility but requires ops attention. For architectures that need multi-cloud resilience, review multi-cloud failover patterns.
3. Hybrid: Use ClickHouse for event analytics, store raw snapshots in S3 and surface metadata to Snowflake for cross-team queries and governance.

Scaling and concurrency

• ClickHouse horizontally scales by adding shards/replicas. For multi-tenant BI workloads, you’ll need a query router and resource governance — these are mature but require ops.
• Snowflake auto-scales compute and isolates concurrency via multi-cluster warehouses. This is a decisive advantage for distributed analytics teams who want zero-ops scaling.

Security, compliance and governance

In 2026, compliance (CCPA/CPRA, GDPR, industry-specific regs) and provider-managed features are decisive:

• Snowflake provides robust built-in governance (data masking, policies, fine-grained RBAC, native data sharing) that simplifies compliance for cross-team and multi-tenant use cases. For developer ops and secret management patterns, see the Developer Experience & Secret Rotation note.
• ClickHouse has improved authentication, RBAC, and integrations — and managed ClickHouse Cloud includes enterprise features — but self-hosted installs need deliberate design for fine-grained governance.

Which warehouse for which scraping use case — practical guidance

Use this checklist to pick the winner for your team.

Pick ClickHouse if:

• You run continuous, high-rate scraper events and need sub-second aggregate latency for dashboards or alerting. See the Latency Playbook for patterns to get there.
• You want the best cost per query for heavy time-series/OLAP patterns and are comfortable running or using a managed ClickHouse cluster.
• You plan to run sketch-based approximate queries (HLL, bitmap) at scale.

Pick Snowflake if:

• Your team needs a fully managed experience with built-in governance, data sharing, and many BI users running concurrent ad-hoc queries.
• You prefer to offload operations, want simple storage lifecycle management integrated with time travel and zero-copy clones, and can batch ingestion into staged files.
• You have downstream ML or data marketplace needs that integrate tightly with Snowflake's ecosystem and Snowpark.

Consider a hybrid approach when:

• You need ultra-fast operational analytics on events but also long-term archival and governed access to raw snapshots.
• Teams are split: SRE/engineering wants ClickHouse for live monitoring while analytics/BI want Snowflake for governance and dashboards.

Practical runbook — steps to evaluate both in your environment (actionable)

1. Instrument your scrapers to produce two sinks in parallel for 7 days: 1) a compact event stream (CSV/Parquet) and 2) raw snapshots to object storage. This gives real data for cost extrapolation.
2. Run a small ClickHouse cluster (or ClickHouse Cloud trial) and a Snowflake trial for the same 7-day data. Use realistic batching (e.g., 50–250 MB files for Snowflake) and native batch inserts for ClickHouse.
3. Measure: peak ingest rows/sec, average time-to-availability, P95/P99 for 3 typical aggregation queries, and compression ratios. Instrument metrics and tracing per the Modern Observability guidance.
4. Model monthly costs from those measurements with at least two scenarios: steady-state and 3× burst traffic. Include operator time and expected retention policies.
5. Decide with your stakeholders: if sub-second freshness and low-cost per-query matter, move events to ClickHouse. If governance, data sharing, and low ops are higher priority, land on Snowflake.

2026 trends and forward-looking considerations

• ClickHouse's accelerated funding and product activity in late 2024–2025 has driven richer managed offerings and ecosystem integrations (vector search, materialized views, real-time adapters) — making it an even stronger fit for scraper analytics in 2026.
• Snowflake continues to invest in AI/ML workflow integrations (Snowpark enhancements, native vector indexes) and governance — valuable for organizations using scraped data to feed models in production. Keep an eye on serverless and edge orchestration trends in the Edge Orchestration & Rewrites coverage.
• Serverless and hybrid storage patterns are maturing: expect more turnkey pipelines that stage raw snapshots in object storage and index metadata in a low-latency store like ClickHouse for hybrid queries.

Quick wins and optimization checklist

• Always batch writes — micro-batch to S3 for Snowflake, batch inserts for ClickHouse.
• Keep raw snapshots in object storage and index metadata in the warehouse where possible.
• Leverage sketches (HLL, bitmap) for distinct counts and heavy hitters; they dramatically reduce compute and memory.
• Use TTLs/partition expiration for scrapers: most teams keep raw snapshots for 30–90 days and aggregated metrics longer.

Example configuration snippets

ClickHouse: MergeTree table for scraper events

CREATE TABLE scraper_events (
  timestamp DateTime64(3),
  domain String,
  response_time_ms UInt32,
  status_code UInt16,
  user_agent String,
  payload_hash UInt64
) ENGINE = MergeTree()
PARTITION BY toYYYYMM(timestamp)
ORDER BY (domain, timestamp)
TTL timestamp + INTERVAL 90 DAY
SETTINGS index_granularity = 8192;

Snowflake: recommended bulk load flow

-- Stage files to S3 (compressed parquet/json)
COPY INTO raw.scraper_events
FROM @my_s3_stage/scraper/
FILE_FORMAT = (TYPE = PARQUET)
ON_ERROR = 'CONTINUE';

-- Use Streams + Tasks for incremental processing
CREATE OR REPLACE STREAM scraper_stream ON TABLE raw.scraper_events;
CREATE OR REPLACE TASK process_scraper
  WAREHOUSE = ANALYTICS_WH
  SCHEDULE = 'USING CRON * * * * * UTC'
AS
INSERT INTO analytics.scraper_events
SELECT * FROM raw.scraper_events WHERE ...;

Final recommendation

If your priority is real-time dashboards, high ingest rates, and low-cost per query for scraper analytics, start with ClickHouse (self-hosted or ClickHouse Cloud). If your priorities are managed scale, multi-team governance, and frictionless BI/ML integrations, Snowflake is the safer faster ramp — but plan ingestion to be batch-oriented to control costs. For many organizations in 2026, a hybrid pattern (ClickHouse for live analytics + Snowflake for governed sharing and ML) provides the best of both worlds.

Benchmarking is the only way to be certain — measure with your real data. Small differences in row size, cardinality, and retention change the winner.

Call to action

Ready to decide for your stack? Run the 7-day evaluation runbook above using your scraper output. Want our benchmark scripts and a cost model template? Download the repo template and a prebuilt estimator (ClickHouse + Snowflake) from our engineering toolkit, or contact us for a hands-on workshop to model your exact ingestion and query patterns.

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