You're manually exporting CSVs from Google Ads, Meta Ads Manager, and GA4, then copying numbers into a spreadsheet to answer: "What's our actual customer acquisition cost across all channels?"
This takes 3 hours. You do it weekly. That's 156 hours per year pulling reports.
The fix: a marketing data warehouse. Not the 18-month IT project, but a practical system you set up in one afternoon that costs $180 to $200/month and eliminates manual reporting.
The Problem: Platform Data Doesn't Play Together
Google Ads deletes data after 2 years. Meta keeps it for 37 months. GA4 stores event-level data for 2 months unless you change settings. When you need to compare this Q4 to three years ago, half your data is already gone.
Three things warehouses fix:
- Keep all historical data forever - Compare any time period to any other time period
- Answer cross-channel questions - "Which campaigns drive highest LTV customers?" requires ads, transactions, and CRM data in one place
- Build custom metrics - Platforms give you ROAS. You need LTV by first-touch channel, which requires joining 3+ data sources
Why BigQuery (And Not Snowflake or Redshift)
Three options dominate: BigQuery, Snowflake, Redshift.
BigQuery wins for most marketing teams because:
- Zero infrastructure - No servers to manage or performance to tune
- Transparent pricing - $5 per TB scanned, typical query costs $0.002-0.01
- Native Google integrations - Free automated transfers from Google Ads, GA4, YouTube via BigQuery Data Transfer
Choose Snowflake if you're already all-in on dbt. Choose Redshift if your entire stack is AWS. Otherwise, BigQuery.
Full technical comparison: Best Cloud Data Warehouse for Marketing
Step 1: Set Up BigQuery (15 Minutes)
What you need:
- Google Cloud account (free $300 credit)
- Credit card for verification
Quick setup:
- Go to Google Cloud Console
- Create project: name it
marketing-warehouse - Enable BigQuery: APIs & Services → Enable APIs → "BigQuery API" → Enable
- Create dataset: BigQuery menu → Create dataset
- Dataset ID:
marketing_data - Location:
US(orEUfor GDPR)
- Dataset ID:
- Test: Run
SELECT 'Live' AS statusin query editor
Done. Your warehouse exists.
Set permissions: IAM & Admin → Grant Access → Add team members
- BigQuery Data Viewer = read only
- BigQuery Data Editor = can create tables
Pro tip: If using an ETL tool like Dataslayer, create a service account with BigQuery Data Editor role. This keeps your personal credentials separate and makes team handoffs easier.
Full setup guide: BigQuery Quickstart
Step 2: Get Data Into Your Warehouse
Two realistic options:
Option A: Build Python Scripts (DIY)
Time investment: 40+ hours initial build
Ongoing: 15-20 hours/month maintenance
Cost: ~$15/month infrastructure
What you handle:
- OAuth refreshes (expire every 60 days)
- Rate limits (Google Ads: 15,000 ops/day)
- Schema changes when APIs update (Meta changes quarterly)
- Error monitoring and retry logic
- Incremental updates to avoid reloading everything
Multiply by 6-8 platforms. Most marketing teams skip this unless they have engineers.
Option B: Use Dataslayer (Automated)
Time investment: 15 minutes setup
Ongoing: ~1 hour/month
Cost: $35-299/month (depending on sources)
Quick setup:
- Sign up, free 15-day trial
- Select BigQuery destination
- Connect sources: Google Ads → Authenticate → Select accounts → Schedule daily sync
- Repeat for Meta, LinkedIn, GA4, etc. (2 min per source)
First sync: 10-30 minutes (historical backfill)
Daily syncs: 2-5 minutes (incremental only)
Real value: Dataslayer maintains connectors when APIs change, handles OAuth automatically, adds new platforms without you coding.
Step 3: Create Unified Views (30 Minutes)
Raw API data is messy. Meta uses "amount_spent." Google Ads uses "cost_micros." LinkedIn uses "costInLocalCurrency."
Create one unified view:
Now query this view instead of dealing with platform differences. Add TikTok? Add another UNION ALL block.
Your first analysis query:
One query. All platforms. Total ROAS by channel.
More on modern data approaches: ETL vs ELT for Marketing
Step 4: Build Dashboards (1 Hour)
Looker Studio (Free)
- Go to Looker Studio → Create Report
- Add data → BigQuery → Select
marketing_data.unified_campaigns - Build:
- Scorecards: Total spend, revenue, ROAS
- Time series: Daily spend and revenue trends
- Bar chart: ROAS by platform
- Table: Top 20 campaigns
Auto-refreshes when anyone opens it. No exports.
Google Sheets (For Finance/Execs)
Use Dataslayer's BigQuery connector to pull warehouse data into Sheets. Finance wants "Meta spend last month"? Send them a Sheet that updates daily automatically instead of running manual queries.
How it works: Extensions → Dataslayer → Select BigQuery → Choose your view → Set refresh schedule. The Sheet pulls fresh data every morning without anyone running queries manually.
Power BI (Enterprise)
Power BI Desktop → Get Data → BigQuery → Enter project → Select view → Build dashboards
Real Example: DTC Brand's $180/Month Warehouse
Company profile:
- DTC fashion, $2.4M revenue/year
- 5-person marketing team
- $40K/month ad spend
- No data engineer
Sources connected: Google Ads, Meta Ads, TikTok Ads, GA4, Shopify, Klaviyo
Architecture: Ad platforms → Dataslayer → BigQuery → Looker Studio + Sheets
Monthly cost breakdown:
Results after 6 months:
- Saved 25 hours/month on manual CSV exports
- Found attribution gap: Meta showed 3.2× ROAS in-platform, warehouse showed 2.1× true ROAS with first-touch attribution
- Shifted $8K/month from Meta to Google Shopping based on LTV data
- Preserved 2022 data when Google Ads hit 2-year retention limit
CMO: "We thought we needed a data analyst. We just needed our data in one place."
See how 150+ agencies build similar warehouses for clients: Read case studies
Best Practices: Keep Your Warehouse Healthy
Weekly Data Quality Checks
Check for missing dates:
Should return zero rows.
Detect duplicates:
Should also return zero rows.
Cost Optimization Tips
Always filter by date first. BigQuery partitions by date automatically. WHERE date >= '2025-01-01' only scans 2025+ data, not your entire 3-year history.
Pre-aggregate common queries. If you run "monthly spend by platform" 20 times/day, create a scheduled query that calculates it once daily. Query the 365-row summary table instead of the 10M-row raw table.
Use clustering. After date partitioning, cluster by platform and campaign_id. Queries filtering these fields scan even less data.
Full optimization guide: BigQuery Best Practices
Access Control
Three permission levels:
- Analysts: BigQuery Data Viewer (read only)
- Data team: BigQuery Data Editor (create tables/views)
- ETL service: Service account with Data Editor (for Dataslayer)
Never share personal credentials. Use service accounts for automation.
Security setup: Google Cloud IAM Documentation
The hardest part isn't the setup. It's starting. Marketing teams waste 15-20 hours/month on manual reporting. Your warehouse pays for itself in 4 weeks through time savings alone, plus better attribution typically improves ad efficiency 10-20%.
FAQ
How long does setup take?
BigQuery: 15 minutes. Dataslayer connectors: 2-3 minutes each. SQL views: 30-60 minutes. Dashboards: 1 hour. Total: 5-7 hours for 6-8 sources.
Do I need SQL skills?
Basic SQL helps but isn't required. If you can write WHERE date >= '2025-01-01', you can query a warehouse. Dataslayer provides SQL templates for common marketing queries.
What if we use Snowflake or Redshift?
Dataslayer connects to all three with identical setup. This guide focuses on BigQuery because it's most accessible, but concepts apply to any cloud warehouse.
What happens when data grows 10×?
BigQuery scales automatically. A 10× data increase = 2-3× query costs, not 10×, because of date partitioning. Query speed stays the same.
Should I keep old data?
Yes. BigQuery storage costs $0.02/GB/month. If you have 500 GB, that's $10/month. Keeping 3 years of history costs less than one hour of analyst time and enables year-over-year comparisons.
What's the ROI?
If your team spends 20 hours/month on manual reporting at $100/hour loaded cost = $2,000/month. Warehouse costs $180/month. Payback: 3 weeks. Plus better attribution typically improves ad efficiency 10-20%.
About Dataslayer: Marketing data integration platform connecting 50+ advertising, analytics, and e-commerce sources to BigQuery, Snowflake, Looker Studio, and Google Sheets. Trusted by 2,000+ marketing teams. ISO 27001 and ISO 27701 certified.
