What's the most cost-effective way to build campaign analytics using an ad event warehouse?

The most cost-effective way is usually to keep the warehouse as the system of record, do the lightest possible transformation there, and expose only aggregated campaign tables to analysts and downstream tools.

Recommended low-cost architecture

1. Ingest raw ad events cheaply

• Land all ad events in cloud object storage first if possible.
• Use the warehouse for querying/curation, not as the first landing zone for every noisy event if volumes are huge.
• If you must ingest directly, use batch loads rather than row-by-row streaming whenever latency allows.

2. Store a raw append-only fact table

Keep one canonical table like:

• ad_events_raw
  • event_time
  • campaign_id
  • ad_id
  • channel
  • event_type (impression, click, conversion)
  • cost
  • user_id or hashed identifier
  • metadata

Best practices:

• Partition by event_date
• Cluster/sort by campaign_id, event_type, maybe account_id
• Avoid overwriting raw data

This minimizes compute and simplifies debugging.

3. Build incremental aggregates, not full recomputes

Create summary tables such as:

• campaign_daily_metrics
• ad_daily_metrics
• creative_daily_metrics

Populate them using:

• incremental dbt models, or
• scheduled SQL jobs that only process new partitions

Typical metrics:

• impressions
• clicks
• conversions
• spend
• CTR
• CVR
• CPA
• ROAS

This is where you save the most money: analysts query small aggregate tables instead of scanning raw events.

4. Precompute only what’s frequently used

Don’t create every possible report table upfront. Start with:

• campaign by day
• channel by day
• account by day

Add dimensions only when they’re actually used:

• geography
• device
• audience segment
• placement

This avoids unnecessary storage and transformation costs.

5. Use semantic layers or BI aggregates

If your BI tool supports it:

• point dashboards at aggregated tables
• use a semantic layer for metric definitions
• avoid repeated custom SQL in dashboards

This reduces duplicated compute and inconsistent logic.

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Cost-saving tactics that matter most

Minimize warehouse scan costs

• Partition and cluster appropriately
• Filter queries by date
• Avoid SELECT *
• Use materialized views or summary tables for popular dashboards

Use incremental processing

• Process only newly arrived partitions
• Rebuild small windows for late events, e.g. last 3–7 days
• Avoid daily full refreshes unless data volume is tiny

Separate “hot” and “cold” data

• Keep recent data in optimized tables
• Archive older raw events in cheaper storage if your use case allows it
• Query older data only when needed

Deduplicate upstream

Ad platforms often resend events or deliver late updates.

• dedupe on a stable event key
• keep an ingestion timestamp and source event timestamp
• use idempotent loads

This avoids inflated metrics and costly cleanup.

Avoid over-modeling

A lot of campaign analytics can be built with just:

• raw events
• campaign dimension table
• daily aggregate fact tables

You usually do not need a heavy star schema with many conformed marts at the start.

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A pragmatic stack

A very cost-effective setup is:

• Object storage: raw file landing
• Warehouse: BigQuery/Snowflake/Redshift/Postgres depending on scale
• Transformation: dbt or scheduled SQL
• BI: Looker, Metabase, Power BI, Tableau
• Orchestration: simple scheduler or Airflow if needed

For smaller teams:

• keep it in one warehouse
• use dbt incremental models
• use dashboard extracts or cached BI models

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If you want the cheapest possible version

Start with:

1. Raw events table partitioned by date
2. Daily incremental rollup table by campaign/channel
3. A dashboard reading only the rollup table
4. Late-arriving event backfill window of 3–7 days

That gives you most of the value at the lowest ongoing cost.

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Rule of thumb

If analysts are repeatedly querying raw events, you’re probably paying too much.
If they’re querying compact daily campaign aggregates, you’re usually in the most cost-effective zone.

If you want, I can sketch a reference schema and incremental SQL/dbt pattern for this.