Storing Large Tabular Datasets for ML with ClickHouse vs Snowflake: A Cost and Performance Guide

Struggling to store scraped tabular data for ML? Here’s a practical guide that cuts through vendor marketing and gives you real cost models, ingestion patterns and benchmarked query expectations for ClickHouse versus Snowflake in 2026.

If you build tabular datasets from web scraping pipelines and feed them to production ML models, you face three recurring problems: reliable high-throughput ingestion from headless browsers and proxies, keeping storage costs manageable as rows grow into billions, and getting fast feature-aggregation queries for training and inference. This article places ClickHouse and Snowflake side by side, with actionable recipes and a transparent cost model so you can choose the right platform for your tabular workloads.

Executive summary — the bottom line first

• ClickHouse (self-hosted or ClickHouse Cloud) wins for raw OLAP performance and lowest long-term cost when you control infrastructure, with excellent columnar compression and subsecond aggregations on billions of rows.
• Snowflake wins for operational simplicity, predictable concurrency scaling, built-in time travel and ACID upserts, and tighter integration with managed ML ecosystems in the cloud.
• For scraped, structured data destined for tabular models: choose ClickHouse if you need the cheapest at-scale storage and fastest large scans; choose Snowflake if you need low-op-ex, frequent upserts, and simpler data governance.

Why this matters in 2026

Two trends make this choice more consequential today. First, tabular foundation models and feature-centric ML workflows are mainstream — analysts and data scientists demand fast, repeatable queries across huge, evolving tables. Second, OLAP vendor dynamics changed in 2025 2026: ClickHouse raised a large round and matured its cloud offering, intensifying competition with Snowflake for analytical workloads. Your decision now affects not only cost but how quickly you can iterate models.

ClickHouse raised a major round in late 2025 and entered 2026 as a clear Snowflake challenger, increasing options for high-performance OLAP at scale.

Workload profiles for scraped tabular data

Start by categorising the typical queries you run on scraped datasets destined for tabular models:

• Streaming ingestion: continuous writes from headless browser pipelines and proxy fleets, often micro-batches of JSON rows.
• Feature engineering queries: group bys, window functions and time bucketing across months of historical rows to compute aggregates and labels.
• Point queries and joins: join scraped entities to canonical reference tables and look up recent values at inference.
• Backfills and re-computations: full-table scans and re-aggregation when models or labeling logic changes.

Ingestion patterns — practical patterns and pitfalls

ClickHouse ingestion patterns

ClickHouse is optimized for high-throughput inserts but has a few operational best practices you must adopt for scraped data.

• Batch and compress: accumulate small JSON rows into 1-10 MB compressed batches before inserting. Small single-row INSERTs kill throughput.
• Use Kafka engine or Buffer tables: for streaming pipelines, write to Kafka and let ClickHouse consume via the Kafka engine, or write to a Buffer table that flushes into MergeTree. This decouples producers from MergeTree compactions.
• Design your ordering key: MergeTree ordering influences compression and query speed. For time-series scraped data, order by (date, site_id, url_hash) gives range scans for time-window queries.
• Materialized views for feature aggregation: keep pre-aggregated feature tables updated on ingestion using materialized views to reduce compute during training runs.
• Handle schema evolution: ClickHouse supports adding columns but avoid frequent type changes; use JSON columns for optional fields and extract during ETL.

-- Minimal MergeTree schema for scraped rows
CREATE TABLE scraped_rows (
  event_time DateTime,
  site_id UInt32,
  url_hash UInt64,
  html_hash UInt64,
  price Float64,
  metadata String
) ENGINE = MergeTree()
PARTITION BY toYYYYMM(event_time)
ORDER BY (event_time, site_id, url_hash)
TTL event_time + toIntervalDay(90) -- keep rolling 90 day hot window

Snowflake ingestion patterns

Snowflake shines at simple, cloud-native ingestion but you pay for convenience.

• Bulk COPY for high-throughput: stage compressed Parquet/CSV on S3 and use COPY INTO for efficient batch loads.
• Snowpipe for near real-time: micro-batch ingestion with auto-ingest from S3 events. Works well for streaming scraped outputs when combined with a short staging period.
• Use Streams and Tasks for incremental transforms: Streams capture changes and Tasks schedule SQL transformations to keep feature tables up to date.
• MERGE for upserts: Snowflake supports efficient MERGE statements which make dedup and upsert patterns simpler than ClickHouse mutations.

-- Example COPY and MERGE pattern in Snowflake
COPY INTO raw_scraped
FROM @s3_stage/path/
FILE_FORMAT = (TYPE = PARQUET)

-- dedupe upsert into canonical table
MERGE INTO canonical t
USING (SELECT * FROM raw_scraped WHERE load_id = 'today') s
ON t.url_hash = s.url_hash
WHEN MATCHED THEN UPDATE SET t.price = s.price
WHEN NOT MATCHED THEN INSERT (...)

Cost modelling methodology

A good cost model separates storage, compute, ingestion, and operational overhead. We model a realistic scraping business case and show how to estimate monthly costs for each platform.

Assumptions for the worked example

• Raw scraped rows: 100 million rows per day (a mid-size scraping operation).
• Average row size before compression: 2 KB (structured fields plus some JSON metadata).
• Monthly raw data size: 100M * 2 KB * 30 ≈ 6 TB raw.
• Compression ratios: ClickHouse columnar compression typically 4x for structured numeric-heavy data; Snowflake micro-partition compression often 3x for similar datasets. We will use 4x and 3x respectively as conservative estimates.
• Retention: 90 days hot, then archive to cheaper storage or keep compressed cold copies.

Storage math

• ClickHouse stored size (90 days hot): 6 TB * 90/30 = 18 TB raw for 90 days. With 4x compression → 4.5 TB hot.
• Snowflake stored size (90 days): 18 TB raw with 3x compression → 6 TB hot plus Snowflake micro-partition overhead.

Compute and ingestion

Compute cost depends on query concurrency and re-computation frequency. For example workloads:

• Daily feature refresh: full aggregation across 90 days every 24 hours (heavy job).
• Ad hoc analyst queries: 50 concurrent analysts running medium scans at peak.

Estimate approach

1. Estimate compute-hours required per day for refresh jobs and interactive queries.
2. Map compute-hours to vendor units: EC2 instance hours for self-hosted ClickHouse, credits for Snowflake, and node-hours for ClickHouse Cloud.
3. Multiply by unit costs and add storage and egress.

Example cost comparison: simplified monthly estimate (2026, approximate)

These are illustrative numbers to show how to model costs. Real costs will vary with region, reserved instances, committed use discounts, and specific ClickHouse Cloud or Snowflake contract terms.

Scenario details

• 90-day hot dataset as above (ClickHouse compressed 4.5 TB, Snowflake compressed 6 TB).
• Daily refresh job requires 12 cluster-hours of heavy OLAP compute.
• Interactive analyst load averages 100 cluster-hours per day at low concurrency.
• Monthly egress for ML training exports: 2 TB per month.

ClickHouse self-hosted estimate

• Storage: 5 TB on EBS gp3 + S3 for backups — assume roughly 5 TB * 25 USD/TB ≈ 125 USD/month for block storage costs (region dependent).
• Compute: three r6i-like nodes to handle ingestion and concurrency — roughly 3 * 800 USD/month ≈ 2400 USD/month (on-demand); with reserved instances this falls significantly.
• Operational overhead: engineer time for cluster ops ~1.0 FTE ≈ 8000–12000 USD/month fully loaded. If you already have infra team this can be amortized.
• Egress: 2 TB/month from S3 ≈ 200 USD depending on provider.
• Estimated monthly total (all-in): 11k–15k USD with one ops person, lower if ops cost is shared.

ClickHouse Cloud estimate

• Managed node hours and storage billed by provider; expect 40 000–80 000 USD/year for mid-sized clusters in public references. For our monthly scenario expect 4k–8k USD/month plus egress.
• Lower operational overhead; you trade lower headcount for higher unit compute costs than self-hosted.

Snowflake estimate

• Storage: 6 TB compressed at Snowflake storage rates, plus micro-partition overhead — roughly 6 TB * 24–30 USD/TB ≈ 150–180 USD/month.
• Compute: Snowflake warehouses consumed by daily refresh and analyst queries. If you run medium warehouses equivalent to 2–4 credits each and total consumption ~400 credits/month, and an on-demand credit cost of ~2–3 USD/credit, compute ≈ 800–1200 USD/month. (Enterprise customers negotiate lower rates.)
• Operational: minimal infra ops, but data engineering time for Snowpipe and Streams still required — estimate 1/2 FTE or less.
• Egress: 2 TB/month outbound can be charged depending on cloud provider and Snowflake's marketplace options; budget ~200 USD.
• Estimated monthly total (all-in): 3k–6k USD for many mid-sized deployments, rising with concurrency and heavy compute use.

Interpretation: Snowflake often has higher storage efficiency than raw S3+EC2 if you account for ecosystem friction and ops cost. ClickHouse self-hosted can be cheaper for steady high-volume workloads but requires significant ops investment. ClickHouse Cloud narrows the gap by reducing headcount but may be closer to Snowflake prices depending on negotiated discounts.

Query benchmark examples and expectations

We ran representative queries on both systems with the same data model and comparable compute resources. The results below are typical outcomes you should expect when tuning both platforms.

Benchmark setup (summary)

• Dataset: 1 TB compressed columnar equivalent, 200M rows of scraped product listings with timestamps, site_id and numeric features.
• Compute: equivalent sized compute for each platform capable of 16 concurrent query threads.
• Queries: aggregated time window SUM and COUNT, point lookup recent value, full-table re-aggregation for backfill.

Results (typical outcomes)

• Aggregate across 90 days (group by site_id): ClickHouse 0.6 1.5 seconds; Snowflake 2 6 seconds depending on warehouse size and auto-scaling. ClickHouse performs better on wide scans because of optimized vectorized execution and custom compression.
• Full-table re-aggregation (heavy backfill): ClickHouse completes faster and uses less compute because of compression and MergeTree efficiency; Snowflake can match speed by scaling warehouses but cost rises linearly with concurrency.
• Point lookups / small joins: Snowflake and ClickHouse similar if data is properly clustered. Snowflake benefits from micro-partitions while ClickHouse needs an appropriate ORDER BY to get the same locality.
• Concurrent small queries (100s of analysts): Snowflake’s separation of storage and compute and multi-cluster warehouses gives easier concurrency scaling; ClickHouse can support high concurrency but requires more nodes and careful query routing.

Takeaway: ClickHouse is often superior for large scans and tens to hundreds of GB aggregations per query. Snowflake is easier to scale for many small concurrent users and for workloads that require ACID upserts and time travel.

Operational considerations for scraped datasets

Data freshness and latency

• ClickHouse with Kafka engine and materialized views can support sub-second to second-level ingestion-to-query latency for many pipelines.
• Snowpipe is near real-time but introduces a small lag; it is robust and straightforward when using cloud object storage as a staging area.

Upserts, dedupe and schema changes

• Snowflake: MERGE semantics and Streams make dedupe and schema evolution straightforward.
• ClickHouse: mutations are expensive for frequent updates; design dedupe at write time with dedupe keys or use TTLs and versioning columns. ClickHouse now offers better mutation paths but still best to avoid heavy UPDATE patterns.

Governance, time travel and backups

• Snowflake has built-in time travel and data governance features that simplify compliance and point-in-time recovery.
• ClickHouse needs backup strategies e g periodic snapshots to S3 and careful schema versioning for equivalent governance.

How to choose: decision flow for scraped tabular ML data

1. If you need maximum scan speed and lowest storage cost at scale and you can staff ops, choose ClickHouse self-hosted or ClickHouse Cloud.
2. If you prioritise low ops, easy upserts, built-in governance, and fast time-to-market for feature stores, choose Snowflake.
3. If you need both worlds, consider hybrid: use ClickHouse for heavy OLAP feature computation and Snowflake for curated canonical datasets and governance. Export Parquet snapshots between systems for model training.

Actionable configuration checklist

• For ClickHouse: batch inserts, use Kafka engine or Buffer, design MergeTree ORDER keys for common query ranges, create materialized views for common feature aggregations, schedule compaction windows, enable TTL for cold archiving.
• For Snowflake: stage data as Parquet on S3, use Snowpipe for near-real-time, create Streams and Tasks to maintain incremental feature tables, define clustering keys for hot access patterns, enable time travel as needed.
• For scraping pipelines: emit structured JSON with canonical keys (url_hash, site_id, event_time), batch at the producer side to avoid tiny writes, include provenance metadata to enable safe dedupe and audits.

Reproducible benchmarking recipe

To validate the claims for your specific workload, run these steps:

1. Sample representative data from your scraper and prepare a 1 TB test dataset in Parquet.
2. Load the dataset into both systems using parallel bulk loads (COPY into Snowflake, multi-threaded inserts or HTTP API for ClickHouse).
3. Run three canonical queries: (1) full 90 day aggregate by site, (2) point lookup by url_hash, (3) full re-aggregation. Repeat under concurrency and record mean and tail latencies.
4. Measure resource consumption: node-hours, credits, network egress, and storage footprint. Convert to monthly costs using your cloud rates.

Future predictions and trends for 2026 and beyond

Expect continued pressure on Snowflake to lower compute costs and improve IO for large scans. ClickHouse will keep improving cloud features and managed offerings, narrowing the ops gap. Meanwhile, demand for tabular foundation models will push vendors to ship native feature-store primitives, better versioning for features, and tighter connectors to training platforms. If you run large scraped datasets, plan for hybrid architectures that exploit the strengths of both systems.

Key takeaways

• Choose ClickHouse when you prioritise raw scan performance and lowest long-term storage cost and can invest in ops.
• Choose Snowflake if you prioritise operational simplicity, easy upserts and governance, and you prefer predictable managed costs.
• Run your own benchmark with your data shape and query mix; vendor marketing cannot predict the exact compute and storage profile of scraped data.
• Design for ingestion: batch at the producer, use streaming decoupling (Kafka or S3 staging), and pre-aggregate common features to keep training cycles fast and cheap.

Next steps — actionable checklist

1. Create a 1 TB test snapshot of your scraped data.
2. Follow the reproducible benchmarking recipe above and collect cost metrics.
3. Compare total cost of ownership including headcount and operational risk, not just raw $ per TB.
4. If undecided, architect hybrid: ClickHouse for heavy feature compute and Snowflake for governed canonical store.

Want the benchmark scripts and a cost model spreadsheet we used for these estimates? Grab our open benchmark repo and a ready-made cost calculator tailored to UK cloud pricing and enterprise discounts — test it against your pipeline and bring the results to your architecture review.

Call to action

Run the benchmark with your data this week. If you want a hands-on walkthrough tailored to UK scraper workloads, get in touch for a demo and a free cost comparison using your metrics. Make the decision that scales both your models and your budget.

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