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Selecting an OLAP for Web Analytics in 2026: ClickHouse, Snowflake, and the Edge

wwebscraper

2026-02-13

11 min read

Compare ClickHouse, Snowflake, and Raspberry Pi edge collectors for web analytics—cost, latency, and hybrid architectures for 2026.

Hook: If your web analytics pipeline must be fast, affordable and privacy-safe in 2026, this guide cuts through the noise

Engineering teams building product analytics face three persistent tradeoffs: cost, latency, and operational burden. In 2026 those tradeoffs look different — ClickHouse has accelerated adoption and investor interest, Snowflake’s serverless analytics continue to dominate enterprise sharing and governance, and edge collectors (increasingly on Raspberry Pi 5-class hardware with AI HAT+ 2 accelerators) let you move processing closer to users. This buyer’s guide helps technical buyers compare ClickHouse, Snowflake, and edge collectors for web analytics workloads and pick an architecture that aligns to your SLOs and budget.

The 2026 landscape in one paragraph

ClickHouse’s rapid growth and funding (major rounds through 2025–2026) sharpened the low-latency, high-ingest OLAP market. Snowflake remains the enterprise default for governance, data sharing, and large-scale SQL analytics. Meanwhile, edge collectors — powered by Raspberry Pi 5 and new AI HAT+ 2 modules (late 2025/early 2026) — make it practical to preprocess, anonymize, and buffer telemetry at the network edge. The question for buyers is not which is objectively best, but which mix of systems minimizes total cost of ownership while meeting latency, scale, and compliance goals.

What makes web analytics different (and why OLAP choices matter)

High write volume with small events (millions to billions/day).
High-cardinality queries (user IDs, session IDs, URL paths, custom dimensions).
Real-time and exploratory queries for product teams (sessionization, funnels, cohort analysis).
Regulatory needs: deletion/erasure, PII masking, regional storage.

Evaluate OLAP by three metrics aligned to those characteristics: ingest throughput and latency, query latency and concurrency, and cost (ingest, storage, compute, egress). See our guidance on storage costs for how hardware and flash choices change the TCO numbers.

Key evaluation criteria (quick checklist)

Ingest latency: time from browser/server event to the event being queryable.
Query latency: typical aggregation response times for 1–1000 concurrent queries.
Cost per TB-month and cost per million events: amortized storage + compute + ingestion costs (see A CTO’s Guide to Storage Costs).
Operational overhead: ops team size or managed service footprint.
Compliance features: PII masking, selective retention, region-aware storage.
Data sharing & tooling: BI connectivity, ML SDKs (Snowpark, ClickHouse integrations).

ClickHouse (2026 status and buyer notes)

Why it’s in the conversation: ClickHouse continues to be one of the most cost-effective, low-latency OLAP engines for high-cardinality, high-ingest analytics workloads. Investor momentum and product maturity in 2025–2026 have pushed managed ClickHouse offerings and richer cloud integrations.

ClickHouse raised major capital during 2025–2026, signaling strong market interest in low-latency OLAP alternatives to Snowflake.

Strengths

Low-latency ingest and queries — seconds for most aggregations and sub-second for well-indexed queries.
Very cost-efficient for raw compute+storage when self-hosted or via managed ClickHouse clouds.
Built for time-series and event data (MergeTree families, TTL, materialized views).
Native streaming integrations (Kafka engine, RabbitMQ connectors).

Weaknesses & operational notes

Self-hosted clusters require seasoned ops: schema design, index tuning, compaction tuning.
Deletes and GDPR-style erasure can be expensive (mutations are costly at scale); plan for TTL partitions and per-user deletion strategies.
Query concurrency at global scale requires careful sharding/replication strategy.

When to pick ClickHouse

Choose ClickHouse when you need sub-second aggregation queries, you control your ops team, and you want low $/query for high-volume event ingestion.

Example ClickHouse schema and ingest (compact)

CREATE TABLE events
(
  timestamp DateTime64(3),
  user_id String,
  session_id String,
  url String,
  event_type String,
  properties String
) ENGINE = ReplacingMergeTree(timestamp)
PARTITION BY toYYYYMM(timestamp)
ORDER BY (user_id, session_id, timestamp);

-- Kafka-engine ingestion example (server-side)
CREATE TABLE events_kafka ENGINE = Kafka()
SETTINGS kafka_broker_list = 'kafka:9092', kafka_topic_list = 'events', kafka_group_name = 'ch_events';

CREATE MATERIALIZED VIEW events_mv TO events AS SELECT * FROM events_kafka;

Snowflake (2026 status and buyer notes)

Snowflake remains the go-to for enterprises needing governed, shareable analytics across teams and organizations. Since 2024–2026 Snowflake improved streaming ingestion mechanics, Snowpark ML integration, and marketplace/data-sharing features that appeal to complex data ecosystems.

Strengths

Serverless scaling handles concurrency spikes transparently via multi-cluster warehouses.
Governance & data sharing are best-in-class (Data Clean Rooms, masking policies, role-based access).
Easy compliance features: time-travel for audit, built-in masking, and managed storage across regions.
Strong ecosystem: BI connectors, partner integrations, Snowpark for in-database compute.

Weaknesses

Higher per-query / per-ingest cost for continuous high-cardinality event workloads unless you optimize storage and compute.
Sub-second ingest historically lagged ClickHouse; streaming features have improved, but cost can be high for sustained real-time workloads.

When to pick Snowflake

Choose Snowflake when your priority is governance, ease-of-use, and multi-team data sharing, and when you can accept higher cost for serverless convenience and extensive tooling.

Ingest pattern example (Snowpipe streaming)

-- Stage events (S3 / cloud storage)
-- Use Snowpipe to auto-ingest and Streams + Tasks for near-real-time processing
COPY INTO analytics.events
FROM @s3_stage/events
FILE_FORMAT = (TYPE = 'JSON');

-- Example stream for incremental processing
CREATE OR REPLACE STREAM events_stream ON TABLE analytics.events;
CREATE TASK process_events
  WAREHOUSE = 'small_wh'
  SCHEDULE = 'USING CRON  * * * * * UTC'
AS
INSERT INTO analytics.events_materialized
SELECT ... FROM analytics.events WHERE METADATA$ACTION = 'INSERT';

Edge collectors and Raspberry Pi in 2026

Edge collectors change the ingestion and privacy equation. Instead of pushing raw events directly to a central OLAP, you can preprocess, redact, sample, and batch at the network edge. Advances in Raspberry Pi 5 hardware and accessory AI HAT+ 2 modules (late 2025/early 2026) make local ML-based redaction and lightweight feature extraction practical and cost-effective.

Why use an edge collector?

Reduce client-to-server latency for measurement while centralizing heavier processing — an edge-first approach helps here.
Privacy by default — redact PII before it leaves the region or device.
Regional buffering for intermittent connectivity and to reduce egress costs.
Edge feature extraction using on-hardware ML (AI HATs) for sampling or enrichment.

Raspberry Pi 5 practicals

Hardware cost: Raspberry Pi 5 boards are inexpensive (low-double-digit USD in most SKUs as of 2025–2026); the AI HAT+ 2 accessory is priced around $130 and brings on-device inferencing.
Power: Plan ~6–12W depending on workload; power redundancy and monitoring are important for remote deployments.
Storage: NVMe boot or local SSDs preferable to microSD for durability at high write volumes.
Software: use lightweight collectors like Fluent Bit, Vector, or a small Go/Rust service for batching and protobuf/avro serialization.

Simple edge collector pattern (Raspberry Pi)

# pseudocode: batch + compress + forward
while true:
  collect events to local buffer
  if buffer.size >= 1000 or time >= 10s:
    compress = lz4(buffer)
    post to central endpoint (HTTPS, auth token)
    if fail: retry with exponential backoff
    flush buffer on success

For practical orchestration patterns and hybrids see our field guide on hybrid edge workflows.

Cost and latency tradeoffs — worked example

Assume a mid-market product with 100M events/day, average raw event size 500 bytes (50 GB/day raw). With compression and columnar storage, this is typically 8–20GB/day stored for analytics (estimate: 300–600GB/month).

Rough TCO comparison (ballpark estimates, 2026)

ClickHouse (self-hosted cluster): For 100M/day, a 3–6 node cluster (compute-optimized instances + attached SSD) can run anywhere from ~$1K–$6K/month depending on instance selection and replication. Expect ops effort for tuning.
ClickHouse (managed/cloud): Managed offerings typically cost more than self-hosted but less than Snowflake for raw ingest/queries — expect ~$2K–$8K/month for this workload, variable with query patterns.
Snowflake: With heavy streaming ingestion and high concurrency, monthly costs easily reach multiple thousands to tens of thousands (e.g., $5K–$30K+/month), due to compute credits for continuous ingestion and ad-hoc queries. Snowflake favors variable consumption pricing and charges for compute and storage separately.
Edge collectors (Raspberry Pi fleet): Hardware CAPEX is modest — a fleet of 50 Pi nodes at $100–200 each is $5K–10K. Monthly connectivity and management (~$100–$1000) plus central OLAP costs (above). Edge lowers upstream egress and central compute by filtering and batching, shifting costs to orchestration and device management.

These are high-level estimates; exact costs depend heavily on query patterns, replication needs, and retention windows. Use a day-0 pilot to measure actual ingest and query cost footprints — and consider running a short internal pilot similar to the micro-app pilots in our micro-apps case studies.

Latency comparison (ingest-to-query and query latency)

ClickHouse: ingest-to-query: seconds; query latency: <1s to a few seconds for OLAP aggregations if schema and indexes are tuned.
Snowflake: ingest-to-query: minutes with batch COPY/standard Snowpipe; seconds with optimized streaming features but at higher compute cost; query latency: seconds for most warehouse sizes, but tail latencies grow under concurrency if not using multi-cluster warehouses.
Edge collectors: capture latency (browser → edge) is usually <100ms on local networks; edge → central batch latency can be tuned to be seconds to minutes depending on network and batching SLOs.

Example hybrid architectures (practical recommendations)

1) Low-latency product analytics (ClickHouse primary)

Use edge collectors or lightweight CDN-based collectors to receive events.
Stream events to Kafka/ClickHouse via materialized views for near-real-time dashboards.
Use ClickHouse for exploratory fast queries; offload long-term raw storage to cheap object store using periodic snapshots.

Collect events centrally (edge collectors optional for privacy).
Ingest to Snowflake via Snowpipe / streaming for near-real-time needs, and store immutable event tables for compliance.
Use Snowpark for ML feature engineering and built-in governance for sharing across orgs.

3) Hybrid: Edge + ClickHouse + Snowflake (best of all worlds)

Edge collectors (Raspberry Pi fleet or CDN workers) do PII redaction, local sampling, and enrichment.
Stream high-volume real-time funnels and dashboards to ClickHouse for low-latency product monitoring.
Periodically (hourly/daily) offload compressed raw events to cloud storage and ingest to Snowflake for long-term analytics, BI, and data-sharing needs.

This hybrid is common for companies that need both fast product analytics and governed BI/ML workflows.

Operational playbook (practical, actionable steps)

Start with an event schema: version it. Keep events compact and strongly typed (JSON Schema/Protobuf).
Deploy edge collectors for buffering and PII redaction if you operate in multiple regions or need low client latency.
Choose ClickHouse when you need frequent near-real-time dashboards; choose Snowflake when governance and sharing are primary requirements.
Run a 30–90 day pilot with real traffic. Measure ingest cost, query latencies, and failure modes. See hybrid and micro-app pilots like the ones in our case studies.
Automate deletion via TTLs and implement data subject request workflows that map to your chosen OLAP. For ClickHouse, plan partitioning strategies to minimize mutation costs — follow edge-first best practices for distributed retention.
Monitor SLOs for tail latencies, queue backpressure, and disk compaction. Use alerts for increasing mutation queues or growing partitions. Operational playbooks for hybrid setups are available in our field guide.

Small code recipes

Client: batch + lz4 + HTTPS forward (pseudo)

POST /collector/batch HTTP/1.1
Host: edge.example.com
Authorization: Bearer 
Content-Encoding: lz4
Content-Type: application/octet-stream

[lz4-compressed protobuf batch]

ClickHouse sessionization example

SELECT
  user_id,
  session_id,
  min(timestamp) AS session_start,
  max(timestamp) AS session_end,
  count() AS events
FROM events
WHERE timestamp >= now() - INTERVAL 7 DAY
GROUP BY user_id, session_id
ORDER BY session_start DESC
LIMIT 100;

Decision framework — which to pick

If sub-second dashboards and low $/query for high-volume ingestion are critical: ClickHouse.
If governance, data sharing, and teams across the company need easy access with minimal ops: Snowflake.
If privacy, regional compliance, or offline buffering matter: Edge collectors (Raspberry Pi) as a front-line component in a hybrid architecture.
If you need both fast dashboards and enterprise governance: Hybrid ClickHouse + Snowflake + Edge.

Final takeaways and 2026 predictions

In 2026 the sensible default for product teams is a hybrid approach: use edge collectors to normalize and protect data at the source, ClickHouse for low-latency product analytics, and Snowflake where governed data sharing and heavy ad-hoc analytics are required. Expect these trends to continue through 2026:

Edge-first analytics will grow as Pi-class hardware and edge SDKs mature. See edge-first patterns.
ClickHouse and other columnar OLAPs will continue to attack Snowflake’s turf on real-time use cases with increasingly managed options.
Snowflake will retain strength in governance, marketplace, and cross-company data products.

Checklist before you buy

Run a 30-day pilot with representative traffic and queries.
Measure true ingest-to-query latency and cost per million events end-to-end.
Verify deletion workflows for compliance and test recovery from compactions.
Plan for operational staffing: who runs the cluster, the edge fleet, and the monitoring?

Call to action

If you’re evaluating OLAP backends for web analytics, start with an measurable pilot that mirrors your production traffic. If you want a second opinion, our team at webscraper.app runs architecture reviews for analytics pipelines — we’ll help size ClickHouse clusters, estimate Snowflake spend, and prototype Raspberry Pi edge collectors so you can choose the optimal tradeoffs for cost, latency and compliance. Book a technical review or start a free trial to map your real costs and latencies in 14 days.

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