Comparing OLAP Options for Scraped Datasets: ClickHouse, Snowflake and BigQuery for Practitioners

Hook: Why your scraped datasets break conventional OLAP assumptions (and what to do about it)

If your scraping fleet produces millions of rows per hour, a hundred-plus columns per record, and frequent re-runs that require historic versions for audit or model training, traditional OLAP advice will fail you. You need a platform that handles high ingest rates, scales for wide, semi-structured tables, and supports reliable time-travel or versioning — all while keeping costs predictable. This article compares ClickHouse, Snowflake and BigQuery through the lens of scraped datasets and gives production-ready architecture patterns you can implement in 2026.

Executive summary — quick recommendations (read first)

• Real-time, low-latency analytics with heavy insert rates: ClickHouse (managed or self-hosted) is the best fit—lowest tail latency, excellent ingestion engines (Kafka/streaming), and strong compression. Use if you need sub-second dashboards and can run/operate clusters or buy ClickHouse Cloud.
• Time-travel, data governance, and SQL-first ML workflows: Snowflake wins for built-in time-travel, auditability, and a managed control plane; use it when retention/restore and fine-grained access controls are priorities.
• Serverless scaling and federated analytics on object storage: BigQuery is the right choice when you want simplified ops, tight GCP integration (BigLake/Iceberg) and pay-per-query simplicity — but watch streaming costs and scan costs.
• Hybrid golden path: Keep raw scraped batches in object storage (Parquet + Iceberg/Hudi), run incremental ETL to your OLAP of choice, and use materialized views or read-optimized tables for dashboards.

Why scraped datasets are a different workload

Scraped data tends to combine three properties that stress OLAP engines:

• High cardinality, high write rates: Continuous micro-batches or streaming inserts, often outpacing many analytic stores' best paths.
• Wide & semi-structured schemas: Scrapers add fields frequently; storing everything as nested JSON seems convenient but can blow query time and storage unless handled correctly.
• Time-travel & reproducibility needs: For labeling, model training, compliance, and debugging you often need historical versions of the extracted records.

2026 context: trends that matter

In late 2025 and into 2026 the market shifted: ClickHouse gained major funding and expanded managed offerings, BigQuery matured its BigLake/Iceberg integrations, and Snowflake doubled down on time-travel and data governance. At the same time, the rise of tabular foundation models has made high-quality, versioned tables a competitive advantage — structured datasets are now central to many AI pipelines. That matters for scrapers: you should design storage for downstream ML as much as for dashboards.

Core comparison — feature matrix from the scraper practitioner view

Ingestion & write patterns

• ClickHouse: Excellent for high-throughput inserts. Kafka engine, native HTTP insert endpoints, and bulk INSERTs work well. Low write latency but requires careful tuning (buffer sizes, merges).
• Snowflake: Best for reliable micro-batch ingestion using Snowpipe (continuous loading), COPY INTO from cloud storage, and staged loads. Good failure semantics and idempotency helpers.
• BigQuery: Serverless streaming inserts and load jobs. Simpler ops, but streaming ingest can be costly at scale; batching to GCS and then loading is usually cheaper.

Schema evolution & semi-structured data

• ClickHouse: Supports JSON/Map columns and nested arrays; schema evolution requires ALTER TABLE for new columns which is fast for MergeTree but needs coordination for distributed clusters.
• Snowflake: VARIANT column for semi-structured data makes schema-on-read easy; automatic parsing and easy flattening for SQL users.
• BigQuery: Native nested/repeated types (STRUCT/RECORD) are efficient. External table support for Iceberg or Parquet gives schema evolution without heavyweight migrations.

Time travel / versioning

• Snowflake: Built-in time travel and zero-copy cloning make snapshotting and forensic queries simple. Excellent for auditability and reproducibility.
• BigQuery: Offers time-travel for recent modifications and strong integration with data lake formats (Iceberg) for longer multi-version history.
• ClickHouse: Not a native time-travel engine. Use append-only tables, ReplacingMergeTree / CollapsingMergeTree patterns or keep raw objects in object storage with Iceberg/Hudi for full history.

Query latency & concurrency

• ClickHouse: Extremely low-latency analytical queries and high concurrency with proper cluster sizing. Best for dashboards fed by near-real-time streams.
• Snowflake: Concurrency is solved via virtual warehouses; latency is good for complex SQL, but cold virtual warehouses incur spin-up time.
• BigQuery: Fast for large scans and aggregations; serverless compute avoids cluster management. Query latency can be very good, but per-query resource unpredictability can affect tail latency.

Cost model & predictability

• ClickHouse: If self-hosted, predictable infra costs and often lowest $/QPS for heavy workloads. Managed ClickHouse Cloud reduces ops but is still generally cheaper for continuous workloads than serverless options.
• Snowflake: Storage + compute credits: storage is cheap and stable; compute (warehouses) can be sized and paused, giving cost control. Time-travel retention affects storage costs.
• BigQuery: Serverless: storage separate from query costs. On-demand querying is priced by bytes scanned; frequent exploratory queries can become expensive. Flat-rate slots help predictability for heavy query workloads.

Operational patterns for scraped datasets (practical advice)

1) Ingest pipeline patterns

Pick one of three ingest patterns based on your scale and SLAs:

1. Streaming-first (low-latency dashboards): Scrapers -> Kafka -> ClickHouse (Kafka engine or Materialized Views). Use compression, batching, and short-lived batch windows to avoid write amplification.
2. Micro-batch (cost-conscious, reliable): Scrapers -> S3/GCS Parquet (partitioned by day/source) -> Snowpipe / BigQuery Load Job / ClickHouse batch loader. This balances cost and reliability.
3. Append-only lake + OLAP compute (best for time-travel): Scrapers -> Parquet/ORC + Iceberg/Hudi -> Use query engine (BigQuery external tables, Trino, Spark) or run ETL into Snowflake/ClickHouse for optimized query patterns.

2) Schema design tips

• Store raw and normalized: Keep raw JSON blobs (Parquet + variant/VARIANT/JSON) in a raw layer, and produce cleaned, typed tables for analytics.
• Wide tables: pack carefully: If you have 200+ optional fields, consider vertical partitioning: base table (common columns) + side tables for rarely used fields stored as JSON.
• Use partitioning and clustering: Partition by scrape_date and cluster by high-selectivity columns (domain, source_id). This limits scanned data and cost.

3) Time-travel & versioning strategy

Don’t rely on a single engine to provide all history semantics. For robust reproducibility:

1. Keep raw Parquet files in object storage with an Iceberg/Hudi table for long-term versioning and snapshotting.
2. Use your OLAP engine for recent-time travel (Snowflake time travel / BigQuery recent time travel) for quick restores and queries.
3. For ClickHouse, maintain an append-only raw table or export snapshots to object storage periodically.

Concrete examples: table DDLs and ingest snippets

ClickHouse (streaming from Kafka -> MergeTree)

-- Create topic consumer table
CREATE TABLE scraped_raw
(
  scrape_ts DateTime,
  source String,
  url String,
  status Int32,
  payload String -- JSON blob
)
ENGINE = MergeTree()
PARTITION BY toYYYYMM(scrape_ts)
ORDER BY (source, url, scrape_ts)
SETTINGS index_granularity = 8192;

-- Materialized view consuming Kafka and populating scraped_raw
CREATE TABLE kafka_raw (key String, value String) ENGINE = Kafka(...);

CREATE MATERIALIZED VIEW kafka_to_raw TO scraped_raw AS
SELECT now() AS scrape_ts, JSONExtractString(value, 'source') AS source,
       JSONExtractString(value, 'url') AS url,
       JSONExtractInt(value, 'status') AS status,
       value AS payload
FROM kafka_raw;

Snowflake (batch loads via Snowpipe)

-- Stage and file format
CREATE FILE FORMAT my_parquet_format TYPE = 'PARQUET';
CREATE STAGE raw_stage url='s3://bucket/scraper/' FILE_FORMAT = my_parquet_format;

-- Table
CREATE TABLE scraped_clean (
  scrape_ts TIMESTAMP,
  source STRING,
  url STRING,
  status INTEGER,
  parsed_variant VARIANT
);

-- Snowpipe or COPY INTO
COPY INTO scraped_clean FROM @raw_stage;
-- Snowpipe auto-ingests new file arrivals for continuous loading

BigQuery (load job recommended for cost control)

# Python: push local batch Parquet files to GCS then load
from google.cloud import bigquery
client = bigquery.Client()
job_config = bigquery.LoadJobConfig(source_format=bigquery.SourceFormat.PARQUET)
uri = 'gs://bucket/scraper/2026-01-01-*.parquet'
load_job = client.load_table_from_uri(uri, 'project.dataset.scraped', job_config=job_config)
load_job.result()

Cost guidance and a reproducible cost model

Exact pricing changes often; instead of fixed numbers, use the following cost model to compare options on your own data:

1. Estimate daily ingest volume (GB/day) and retention days.
2. Calculate storage = GB/day * retention * storage_unit_cost.
3. Estimate query scan volume per day (GB scanned) and compute pattern (concurrent dashboards, heavy joins). For serverless engines, cost scales by bytes scanned; for warehouse-based, cost scales by vCPU-hours.
4. Account for streaming or ingest charges (some providers bill per 100k streaming rows or per MB streamed).

Implement this as a simple spreadsheet. To bias decisions quickly:

• Heavy continuous ingestion + predictable dashboards -> ClickHouse (self-hosted or ClickHouse Cloud) is usually more cost-effective.
• Irregular large analytical scans with strong governance -> Snowflake or BigQuery but calculate scan cost for BigQuery carefully.
• Long-term storage with occasional rebuilds -> keep raw Parquet in object storage and only pay OLAP compute when needed.

Architectural recipes for common production needs

Recipe A: Low-latency backoffice for scraping at scale (ClickHouse)

• Scrapers -> Kafka (partition by source) -> ClickHouse Kafka engine -> MergeTree/ReplicatedMergeTree optimized with compression and ORDER BY (source, scrape_ts).
• Materialized views for pre-aggregations (status counts, url health) and TTLs to drop raw payload after X days.
• Daily snapshot export of raw Parquet to S3 for long-term time travel (Iceberg) and model training.

Recipe B: Auditable ML dataset pipeline (Snowflake + Iceberg)

• Scrapers -> Parquet to S3 -> Iceberg table for raw append-only storage (long-term versioning).
• Snowflake loads cleaned batches from S3 (COPY INTO); use Snowflake Time Travel for small restores and Iceberg for full lineage.
• Use Snowpark to transform and produce feature tables; use zero-copy cloning for experiments and labeled datasets.

Recipe C: Minimal ops, serverless analytics (BigQuery + GCS)

• Scrapers -> batch Parquet to GCS partitioned by date and source -> BigQuery load jobs or external Iceberg tables for immediate querying.
• Use scheduled queries to build read-optimized tables and BigQuery materialized views for fast dashboards.
• Use flat-rate slots if query volume makes on-demand scanning expensive.

Operational pitfalls & how to avoid them

• Too many small files: Causes poor throughput on loads. Batch writes to Parquet using sensible file sizes (100MB–1GB).
• SELECT * on wide tables: Scans massive data; use column projection and precomputed aggregates.
• Relying on a single copy for time-travel: Single-engine time-travel is temporary. Keep raw lake snapshots for long-term reproducibility.
• Streaming costs blowup: If using provider streaming inserts (BigQuery streaming, Snowflake streams), monitor costs and prefer micro-batches when sensible.

Case study (realistic): 1 TB ingested/day, 30-day hot retention

Walkthrough of considerations (not vendor sticker prices):

• Storage: 1 TB/day * 30 days = 30 TB hot. Keep raw files in compressed Parquet (good compression reduces this). Object storage is cheapest for long-term.
• Compute: If dashboards query the last 7 days heavily, keep a copy optimized in ClickHouse or Snowflake for those sliding windows and archive older data to the lake.
• Time-travel: Keep Iceberg snapshots for full history; rely on Snowflake time-travel for short-term rollbacks and ClickHouse for fast operational metrics.

How to choose: a decision checklist

• Do you need sub-second dashboards? → ClickHouse.
• Do you need built-in long-ish time travel and fine-grained access control? → Snowflake.
• Do you want serverless ops and expect many ad-hoc large scans? → BigQuery.
• Is cost predictability paramount and you have ops capacity? → consider self-hosted ClickHouse with an object-lake backup.

Pro tip: Treat your object lake (S3/GCS) as the source of truth for scraped data. OLAP stores should be optimized derivatives for the specific query patterns you care about.

Advanced strategies (2026-forward)

• Iceberg + compute separation: Use Iceberg on top of object storage to get reliable snapshotting and schema evolution, and query it from BigQuery (BigLake), Trino, or Spark; this enables multi-engine consumption and true time-travel semantics.
• Feature stores & tabular foundation models: In 2026, teams are treating cleaned scraped tables as first-class features for tabular models. Use Zero-copy clones (Snowflake) or snapshot exports for training datasets to avoid rebuilds.
• Cost-aware query routing: Route expensive ad-hoc queries to a separate cluster/warehouse or to a sandbox BigQuery project with quota limits to avoid noisy-neighbor billing surprises.

Checklist before you commit to a platform

• Run a representative benchmark: ingest rate, typical queries, and worst-case concurrent dashboards.
• Estimate 30/90/365-day storage and include time-travel retention overhead.
• Plan for schema evolution: will you prefer nested storage or typed columns?
• Confirm integrations: Kafka, Snowpipe, BigQuery streaming, Iceberg/Hudi support, and your orchestration tools (Airflow, dbt, etc.).

Final recommendations (practical takeaways)

• Start with a lake-backed architecture: Store raw Parquet with Iceberg/Hudi. This gives you low-cost durable history and allows switching OLAP engines later.
• Pick OLAP by primary workload: Real-time -> ClickHouse; Governed analytics & ML -> Snowflake; Serverless ad-hoc & GCP-native -> BigQuery.
• Optimize for file size and partitioning: Prevent small-file problems and choose partitions that reflect query predicates (scrape_date, source).
• Measure costs with a small POC: Use your real scrape data for a week and compare scan costs, compute latency, and ingest overhead before rolling out.

Resources & next steps

• Run a 2-week proof-of-concept with sample scale: Kafka -> ClickHouse vs. Parquet -> Snowflake vs. Parquet -> BigQuery. Measure tail latency and $/query.
• Instrument end-to-end: track bytes ingested, bytes scanned, query latency percentiles, and cost per dashboard refresh.
• Design your retention & time-travel policy: cold archive everything older than X days into object storage with Iceberg snapshots.

Call to action

If you manage scraper fleets or a data platform, start with a reproducible POC using your 3 most common queries and 24–72 hours of real scraped data. If you'd like, we can provide a downloadable checklist and cost-model spreadsheet tailored to your ingest profile — drop a data sample (anonymized) and we'll show a comparative costing and architecture recommendation within 48 hours.

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