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Designing a Cost‑Efficient Marketing Analytics Stack: ClickHouse + Google’s Total Campaign Budgets

UUnknown

2026-01-26

11 min read

Combine Google Search’s 2026 total campaign budgets with a ClickHouse analytics stack to validate pacing, attribute spend, and cut analytics costs.

Hook: Stop guessing at budget impact—measure and optimize it

Marketing teams are under constant pressure to hit weekly promos, launch offers, and prove ROI—without a runaway ad bill. Google Search's 2026 rollout of total campaign budgets removes one point of friction by letting Google pace spend across a defined period. But automation creates new analytics demands: how do you validate pacing, attribute conversions fairly, and detect wasted spend in near real time without burning money on analytics infrastructure?

This article shows how to combine Google Search's total campaign budgets with a ClickHouse-backed marketing analytics pipeline to get precise, cost-efficient attribution, pacing visibility, and reporting. You'll get architecture patterns, ClickHouse schemas and queries, ETL examples, and operational best practices tuned for 2026 trends.

Executive summary (what you’ll get)

Why Google’s total campaign budgets change the data you must capture.
Reference architecture for an ETL pipeline that feeds ClickHouse for fast, low-cost analytics.
ClickHouse table patterns, schema examples and production-ready SQL for spend allocation and attribution.
Cost control, data retention, and operational tips to keep analytics spending predictable.

Why this matters in 2026

In early 2026 Google extended total campaign budgets (previously available for Performance Max) to Search and Shopping campaigns. Marketers can now define a total budget across days or weeks and let Google smooth spend automatically. That reduces manual daily tuning—but it also shifts the measurement challenge.

With the platform optimizing spend internally, teams must reconcile campaign-level budget policies with click-level and conversion-level telemetry to answer questions like:

Did Google burn the total budget too early or underspend near the deadline?
How do I apportion the total budget across conversions and channels when Google changes pacing?
Can I detect overspend or anomalous pacing within hours (not days)?

At the same time, ClickHouse has matured into a go-to OLAP engine for high-cardinality ad event data. With major fundraising and ecosystem growth in late 2025 and early 2026, it offers a high-performance, cost-efficient analytical store for marketing telemetry—perfect for driving near-real-time attribution and pacing analysis. If you're wondering how to keep cloud bills predictable while scaling analytics, start with our guide to cost governance & consumption discounts.

Bottom line: Let Google optimize delivery with total campaign budgets—use ClickHouse to validate, attribute, and operationalize insights at scale without paying cloud-warehouse premiums.

High-level architecture

Here's a practical pipeline that balances timeliness, cost, and maintainability:

Ingest: Pull Google Ads API (campaign, budget, and performance reports) and click-level events (via server-side event collection, GA4 or first-party click tracking).
Stream/Stage: Buffer events in Kafka or S3; use small batches to control costs.
Transform (ETL): Normalize ad IDs, map spend to UTC/day buckets, sessionize users, and join conversion events to click events.
Store: Write to ClickHouse using MergeTree tables with partitioning, compression and TTLs.
Model: Build attribution logic (last-click, fractional, data-driven) as ClickHouse materialized views or materialized-projection tables.
Visualize & Alert: Grafana/Looker dashboards and alerts read from ClickHouse for pacing, ROAS and anomaly detection. For teams deciding buy vs build for small tooling and micro-apps in your stack, see this cost-and-risk framework.

Why ClickHouse?

High throughput for event joins and time-series aggregation at low infra cost.
Advanced features (MergeTree variants, TTL, projections, codecs) let you balance storage and query latency.
Proven for ad-tech workloads — the ecosystem growth in 2025–2026 means more connectors and managed services. If you’re planning a migration strategy or avoiding downtime while moving clusters, our multi-cloud migration playbook is a good reference.

Ingest: what to capture from Google Search (total campaign budgets)

Google Ads exposes campaign budget and performance endpoints via the Ads API. For total campaign budgets you should capture:

campaign_id, campaign_name, start_date, end_date, total_budget (currency & micros)
daily_spend, hourly_spend (if available) as Google publishes campaign performance reports
budget_pacing_status (if present in the API), budget_remaining
ad_group_id, ad_id, keyword_id, match_type for downstream analysis

Also ingest click-level and conversion events (server-side or GA4):

click_id (gclid or first-party click token), timestamp, campaign_id, ad_id, landing_page, user_pseudonym
conversion_id, conversion_timestamp, conversion_value, conversion_type

Practical ETL advice

Use incremental pulls from Google Ads API (reporting.date_range) rather than full exports.
Emit events into Kafka or write compressed Parquet to S3. For cost efficiency, favor hourly Parquet batches if sub-minute latency isn't required.
Normalize timezones to UTC and store both event_time and event_date for partitioning.

ClickHouse schema patterns (production-ready examples)

Design tables to support high-cardinality joins and fast aggregations. Use MergeTree, partition by month, and choose a primary key that supports time-range queries.

Event table: clicks

CREATE TABLE analytics.clicks (
    event_date Date,
    event_time DateTime,
    click_id String,
    user_id String,
    campaign_id UInt64,
    ad_group_id UInt64,
    ad_id UInt64,
    landing_page String,
    referrer String,
    params Nested(k Array(String), v Array(String))
  )
  ENGINE = MergeTree()
  PARTITION BY toYYYYMM(event_date)
  ORDER BY (campaign_id, event_time)
  SETTINGS index_granularity = 8192;

Spend table: campaign_budget_reports

CREATE TABLE analytics.campaign_spend (
    report_date Date,
    campaign_id UInt64,
    total_budget UInt64,        -- micros
    spend_micros UInt64,
    budget_start Date,
    budget_end Date,
    raw_report JSON
  )
  ENGINE = MergeTree()
  PARTITION BY toYYYYMM(report_date)
  ORDER BY (campaign_id, report_date);

Conversions table

CREATE TABLE analytics.conversions (
    conv_id String,
    conv_time DateTime,
    click_id String,
    campaign_id UInt64,
    conversion_value Float64,
    conv_type String
  )
  ENGINE = MergeTree()
  PARTITION BY toYYYYMM(conv_time)
  ORDER BY (campaign_id, conv_time);

Attribution & budget allocation strategies

When Google controls pacing, the critical task is allocating the actual spend to conversions for fair reporting. Two practical approaches work well in most setups:

1) Click-level cost assignment (preferred when you have click_ids)

Map spend to clicks using reported spend per click when available, or allocate campaign spend proportionally across clicks in the reporting window. Then join clicks to conversions via click_id.

-- Pseudocode: prorate campaign daily spend across clicks
  WITH daily_clicks AS (
    SELECT campaign_id, report_date, count() AS clicks
    FROM analytics.clicks
    GROUP BY campaign_id, report_date
  ),
  campaign_spend AS (
    SELECT campaign_id, report_date, sum(spend_micros) AS spend
    FROM analytics.campaign_spend
    GROUP BY campaign_id, report_date
  )
  SELECT c.conv_id,
         c.conv_time,
         c.conversion_value,
         cs.spend * 1.0 / dc.clicks AS spend_allocated_per_click
  FROM analytics.conversions AS c
  JOIN analytics.clicks AS cl ON cl.click_id = c.click_id
  JOIN daily_clicks AS dc ON dc.campaign_id = cl.campaign_id AND dc.report_date = toDate(cl.event_time)
  JOIN campaign_spend AS cs ON cs.campaign_id = cl.campaign_id AND cs.report_date = toDate(cl.event_time);

2) Windowed fractional allocation (when click-level tokens are missing)

If you can’t connect conversions to clicks via click_id (e.g., redirects strip tokens), allocate spend by channel/time window and then fractionally assign to conversions by share of conversions or weighted by conversion value.

-- Example: allocate campaign spend to channels proportionally by conversions
  SELECT
    campaign_id,
    channel,
    report_date,
    spend_micros * conversions_in_channel / total_conversions AS spend_allocated
  FROM (
    SELECT campaign_id, report_date, sum(spend_micros) AS spend_micros
    FROM analytics.campaign_spend GROUP BY campaign_id, report_date
  ) AS spend
  JOIN (
    SELECT campaign_id, channel, toDate(conv_time) AS report_date, count() AS conversions_in_channel
    FROM analytics.conversions
    GROUP BY campaign_id, channel, report_date
  ) USING (campaign_id, report_date)
  JOIN (
    SELECT campaign_id, report_date, sum(conversions_in_channel) AS total_conversions
    FROM ... GROUP BY campaign_id, report_date
  ) USING (campaign_id, report_date);

Practical ClickHouse optimizations for cost control

ClickHouse gives you knobs to reduce storage and compute costs while preserving query speed:

Partitioning by month reduces queried data for typical dashboards.
TTL to drop or aggregate raw events after N days (e.g., keep raw clicks 90 days, keep aggregates for 2 years).
Compression codecs (ZSTD, LZ4) and low-cardinality optimizations for strings — cut storage significantly.
Projections & Materialized Views for heavy aggregations (ROAS by campaign/hour) to avoid repeating expensive scans.
Sampling for exploratory queries—ClickHouse supports sampling expressions to run fast previews without scanning full tables.

Monitoring, pacing alerts and forecasting

With total campaign budgets, you need to monitor both absolute spend and pacing relative to the campaign schedule. Key metrics:

budget_burn_rate = spend_to_date / elapsed_time_fraction
projected_end_spend = spend_to_date / elapsed_time_fraction * total_period
ROAS and CPA by campaign/ad_group/ad

Example ClickHouse query for burn rate and projection:

SELECT
    campaign_id,
    sum(spend_micros) AS spent_today,
    (spent_today / toFloat64(toUnixTimestamp(now()) - toUnixTimestamp(min(budget_start)))) AS burn_rate_per_sec,
    spent_today / least(1, elapsed_fraction) AS projected_total_spend
  FROM analytics.campaign_spend
  WHERE report_date BETWEEN budget_start AND budget_end
  GROUP BY campaign_id;

Wire these queries into Grafana with alert rules (e.g., projected_total_spend > total_budget * 1.02) to trigger notifications and human review. If you need clear prompt patterns for automated alerts and incident messages, this collection of prompt templates can help you reduce noise in alerting copy and automation.

Attribution model recommendations for 2026

Choose the model that matches your data fidelity and business needs:

Click-level cost attribution: best when you have reliable click tokens (gclid or first-party). Lowest bias.
Model-based fractional attribution: use when you need multi-touch credit and have event-level data—implement within ClickHouse or a downstream ML layer. For notes on monetizing training data and how product changes affect ML ops, see this write-up.
Budget-based allocation: pragmatic fallback when only campaign-level spend is accessible—allocate by conversions or values per period.

Data-driven models in 2026 increasingly use hybrid solutions: ClickHouse for fast feature calculation and a small ML service for weighting. Compute features in ClickHouse (time-to-conversion, touch counts, device, funnel position) and feed to a lightweight model (PyTorch or scikit-learn) for per-conversion credit assignments. If you’re building and shipping small ML services, think about release practices described in our binary release pipelines feature.

Privacy, compliance, and data governance

Keep these constraints front-of-mind:

Hash or pseudonymize user identifiers before storing; avoid storing raw PII.
Implement TTLs to honor retention policies (GDPR, CCPA) and reduce cost via ClickHouse TTLs. For building privacy-first capture and storage patterns, see privacy-first document capture best practices.
Log data lineage—capture which API or ETL job produced each row for audits.

Cost-efficiency checklist

Start with hourly batch writes (Parquet > S3 > ClickHouse) before moving to streaming if you need sub-minute dashboards.
Use materialized views for frequent aggregates; drop raw detail after validation and TTL.
Partition thoughtfully and use compression codecs to cut storage.
Limit high-cardinality joins at query time by precomputing keys and denormalized views.
Monitor ClickHouse cluster CPU and IO; scale nodes when queries become IO-bound not CPU-bound. For an in-depth look at cloud cost governance patterns that pair with these operational moves, read cost governance & consumption discounts.

Real-world example: 72-hour launch campaign

Scenario: a 3-day product launch with a total campaign budget of $100k. Google paces delivery to use the budget by the end date. You need to:

Report hourly spend vs. target pacing.
Allocate spend to conversions for ROAS calculation.

Implementation steps:

Ingest campaign_spend hourly and append to analytics.campaign_spend.
Ingest click events with click tokens in near-real-time (e.g., 5–15 minutes).

Create a materialized view that computes hourly spend and conversions per campaign/ad.

CREATE MATERIALIZED VIEW analytics.hourly_campaign_metrics
      ENGINE = AggregatingMergeTree()
      PARTITION BY toYYYYMM(report_time)
      ORDER BY (campaign_id, report_time) AS
      SELECT
        toStartOfHour(event_time) AS report_time,
        campaign_id,
        sumState(spend_micros) AS spent,
        countStateIf(conversion_id, conversion_id != '') AS conversions
      FROM ...;

Use Grafana panels to show actual vs expected pacing and alerts for projected overspend.

Operational tips and pitfalls

Don’t assume Google’s pacing equals even daily spend—expect intra-day variance. Monitor hourly.
Validate Google Ads API spend against bank billing—API rounding and currency micros can cause small mismatches.
Plan for partial attribution: not every conversion will map to a click. Use conservative defaults and label unknowns for later reprocessing.
Test ETL changes on a shadow dataset before enabling production writes—materialized views can persist bad logic quickly.

2026 trends and future-proofing

Key trends to design for:

Ever-growing first-party telemetry and server-side tagging—expect more reliable click-level tokens and more data to ingest.
ClickHouse ecosystem expansion (new managed offerings and connectors in 2025–2026) makes self-hosted analytics easier to operate at scale. Consider edge and indexing strategies discussed in edge-first directory design patterns for resilient connectors.
ML-based attribution becomes mainstream—design feature pipelines in ClickHouse to serve models cheaply and at scale.
Privacy-first measurement solutions will demand stronger aggregation and noise-injection strategies. Build hooks for aggregated reporting to preserve utility.

Actionable implementation checklist (30/60/90 days)

30 days

Enable Google Ads total campaign budget reporting and schedule hourly pulls into a staging location (S3/Kafka).
Deploy a ClickHouse test cluster and one MergeTree table for campaign_spend.
Build a Grafana dashboard showing daily spend vs total_budget for active campaigns.

60 days

Ingest click-level events with click tokens and map to campaign IDs.
Create joins / materialized views for spend-to-click prorating and report ROAS per campaign.
Configure TTLs and compression, and test cost under realistic loads. If you need to decide whether to buy a managed connector or build one in-house, our framework can help.

90 days

Implement a fractional or data-driven attribution model (offline first, then real-time scoring).
Automate pacing alerts and integrate with Slack/ops channels. Consider using clear alert templates to reduce false positives—see prompt templates for inspiration on clean automation copy.
Run a retrospective on accuracy vs. billing for at least two campaigns and iterate on mapping logic.

Final thoughts

Google's total campaign budgets free marketers from manual pacing; ClickHouse frees analytics teams from steep warehouse bills while providing the speed to validate pacing and build robust attribution. Put them together with pragmatic ETL, robust privacy, and forecasting alerts, and you’ll have a modern marketing analytics stack that is both precise and cost-efficient. For deeper operational guidance on running resilient, low-downtime services and pipelines, consider the evolution of binary release pipelines and how CI/CD affects analytics reliability.

For teams in 2026, the goal isn’t eliminating automation—it's making automation measurable and accountable. Use ClickHouse as the single source of truth for spending, conversions, and attribution to close the loop between campaign policy and business outcomes. If you’re preparing to add ML features or monetize model outputs, review implications in monetizing training data.

Call-to-action

Ready to validate your Google total campaign budgets with a ClickHouse-backed pipeline? Start with a reference repo, ETL templates, and ClickHouse table definitions that you can deploy in hours—not weeks. Contact our engineering team for a walkthrough, or spin up the starter kit to get hourly pacing dashboards and cost-attribution reports in your environment. If you need a migration or multi-cloud strategy to avoid platform lock-in, our multi-cloud migration playbook is a good companion document.

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