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If you're managing marketing data from multiple platforms and spreadsheets are becoming unmanageable, Amazon Redshift offers a scalable alternative for centralizing everything in one data warehouse. This guide walks through why marketing teams choose Redshift, how to set it up, what it costs, and whether it's right for your needs.
Redshift is Amazon's cloud data warehouse service, built on PostgreSQL. It handles large datasets and complex queries, exactly what you need when consolidating ad spend, conversions, impressions, and engagement metrics from dozens of sources into unified reports.
Instead of manually downloading CSVs from Google Ads, Facebook, LinkedIn, and TikTok each week, you query everything in one place using SQL. No more version control issues or "which spreadsheet has the latest data?" confusion.
Why Marketing Teams Use Amazon Redshift
Cost predictability vs pay-per-query models
Redshift charges based on cluster size, not query volume. You pay a fixed hourly rate whether you run 10 queries or 10,000.
Compare this to pay-per-query models where costs spike unpredictably. If your team runs frequent exploratory queries, testing attribution models, analyzing creative performance across 50 campaigns, variable pricing can be problematic. One analyst accidentally querying unpartitioned tables can rack up hundreds in a single day.
Redshift works well for teams running 100+ queries daily with consistent data volumes. When analysts need freedom to explore data without worrying about each query's price tag.
Variable pricing wins for small teams with sporadic analysis needs. When you're just starting with data warehousing and want zero infrastructure management.
Integration with AWS ecosystem
If you're already using AWS services, Redshift connects natively without data transfer fees. Store raw marketing data exports in S3 buckets, then load into Redshift. Connect to QuickSight for visualization. Trigger automated data refreshes with Lambda functions when new ad platform data arrives.
Some marketing teams use Lambda to detect when their marketing automation platform exports campaign data to S3 overnight. Lambda automatically loads it into Redshift, so dashboards are updated by morning.
When Redshift makes sense
Good fit if you're managing 10+ advertising accounts across multiple platforms, need 2+ years of historical campaign performance data, have a technical team comfortable with SQL and AWS, or need to join marketing data with product usage data from your app database.
Bad fit if you're just tracking 2-3 campaigns, no one on your team has SQL experience, monthly ad spend is under $10,000, or you need visual drag-and-drop interfaces instead of SQL.
Setting Up Redshift for Marketing Data
You'll need an AWS account with billing enabled, an IAM user with RedshiftFullAccess policy, a VPC in your preferred region, and a security group allowing inbound access on port 5439.
Never expose Redshift clusters to 0.0.0.0/0 (all internet traffic). Whitelist only your office IP or VPN ranges. Marketing data often includes customer email addresses, conversion values, and PII depending on your tracking setup.
Choosing the right cluster configuration
Redshift offers two main node types:
- dc2.large: 160 GB SSD storage, handles up to 500,000 ad campaigns with 12 months historical data, costs around $180/month for single-node cluster.
- ra3.xlplus: Storage scales independently up to 32 TB, handles millions of ad impressions daily with 24+ months retention, costs around $780/month plus storage.
Most marketing teams starting out can handle 10-20 ad accounts comfortably with one dc2.large node. You can resize later using Elastic Resize (takes 10-15 minutes).
Schema design for marketing analytics
Create separate schemas for each data source to avoid table name conflicts:
CREATE SCHEMA google_ads;
CREATE SCHEMA facebook_ads;
CREATE SCHEMA linkedin_ads;
Sample table structure for Google Ads campaigns:
CREATE TABLE google_ads.campaign_performance (
date DATE NOT NULL,
account_id VARCHAR(20),
campaign_id VARCHAR(20),
campaign_name VARCHAR(255),
impressions INTEGER,
clicks INTEGER,
cost DECIMAL(10,2),
conversions DECIMAL(8,2)
)
DISTKEY(campaign_id)
SORTKEY(date);
DISTKEY(campaign_id)SORTKEY(date) physically orders data by date, making time-range queries significantly faster, often 10x or more.
For marketing data, use date as your SORTKEY since most queries filter by time periods.
Connecting Marketing Platforms to Redshift
Manual CSV exports take 2-3 hours weekly for 5 platforms and are prone to human error.
Platform APIs with custom scripts require writing Python code using libraries like google-ads and facebook-business-sdk. You'll handle OAuth token refresh, API rate limits, schema changes, and error logging. Development time runs 40-60 hours initially, plus 5-10 hours monthly maintenance.
ETL tools automate the connection between marketing platforms and Redshift. You select which metrics to pull, choose Redshift as destination, and data refreshes daily. Setup takes 15-30 minutes per data source with no code required. This makes sense when your team's time costs more than typical ETL subscriptions.
What You'll Actually Pay
Check the AWS Redshift pricing page for current rates as of February 2026. Realistic scenarios:
Small team (5-10 ad accounts): 1 dc2.large node, 50 GB data (6 months), 200 queries/day = ~$180/month
Mid-size team (20-50 accounts): 2 dc2.large nodes, 300 GB data (18 months), 1,000 queries/day = ~$360/month
Enterprise (100+ accounts): 1 ra3.4xlarge node, 2 TB data (24 months), 5,000 queries/day = ~$1,100/month plus storage
Hidden costs
Data transfer fees apply if your Redshift cluster is in us-east-1 but your team queries from Europe. Keep BI tools in the same region.
Automated snapshots are free for 1 day retention, then you pay for backup storage. For a 500 GB cluster with 7-day backup retention, budget roughly $80-90/month extra.
Cost optimization
Pause dev/test clusters during nights and weekends to save 65% on non-production environments.
Reserved instances offer discounts: 1-year commitment gets 34% off, 3-year gets 57% off. Only do this after 3 months of stable usage.
Archive campaigns older than 18 months to S3. Storage costs are similar, but you free up cluster capacity for active analysis.
Essential SQL Queries for Marketing Analytics
Compare cost-per-acquisition across platforms weekly:
SELECT
DATE_TRUNC('week', date) AS week,
'Google Ads' AS platform,
SUM(cost) AS spend,
SUM(conversions) AS conversions,
SUM(cost) / NULLIF(SUM(conversions), 0) AS cpa
FROM google_ads.campaign_performance
WHERE date >= CURRENT_DATE - 90
GROUP BY 1, 2
UNION ALL
SELECT
DATE_TRUNC('week', date) AS week,
'Facebook Ads' AS platform,
SUM(spend) AS spend,
SUM(conversions) AS conversions,
SUM(spend) / NULLIF(SUM(conversions), 0) AS cpa
FROM facebook_ads.ad_performance
WHERE date >= CURRENT_DATE - 90
GROUP BY 1, 2
ORDER BY week DESC, platform;
The NULLIF prevents division-by-zero errors when no conversions occurred.
Find underperforming campaigns with average CPC 50% higher than your account median:
WITH campaign_stats AS (
SELECT
campaign_name,
SUM(clicks) AS total_clicks,
SUM(cost) AS total_cost,
AVG(cost / NULLIF(clicks, 0)) AS avg_cpc,
PERCENTILE_CONT(0.5) WITHIN GROUP (ORDER BY cost / NULLIF(clicks, 0)) AS median_cpc
FROM google_ads.campaign_performance
WHERE date >= CURRENT_DATE - 30
GROUP BY campaign_name
)
SELECT
campaign_name,
total_clicks,
total_cost,
avg_cpc,
avg_cpc - median_cpc AS cpc_deviation
FROM campaign_stats
WHERE avg_cpc > median_cpc * 1.5
ORDER BY cpc_deviation DESC
LIMIT 20;Redshift vs BigQuery vs Snowflake
Choose Redshift if your company uses AWS infrastructure, you need VPC isolation for compliance, your team knows PostgreSQL, or you run 500+ queries daily.
Choose BigQuery if you're starting with data warehousing, use Google Workspace, have unpredictable query volume, or want zero infrastructure management. (We have a detailed comparison of cloud data warehouses for marketing if you want to explore all three options in depth.)
Choose Snowflake if you need multi-cloud deployments, data volume varies dramatically, or budget allows $200-500/month minimum.
Common Setup Mistakes
Not setting WLM (Workload Management) queues means Redshift allows 15 concurrent queries by default. If your team runs 20 simultaneous reports, 5 will queue. Set up WLM to prioritize executive dashboards (2 slots), analyst queries (10 slots), and ETL loads (3 slots). Without proper configuration, one runaway query blocks everyone.
Forgetting to VACUUM and ANALYZE means deleted rows waste space. Run weekly:
VACUUM DELETE ONLY google_ads.campaign_performance;
ANALYZE google_ads.campaign_performance;
Marketing teams sometimes notice queries slowing after several months of daily updates. Updating campaign names daily creates new rows while leaving deleted rows. A VACUUM operation can reclaim significant storage space.
Not using compression encodings wastes storage. Campaign name columns typically compress 8-12x. For 1 million campaigns, this reduces storage from 500 MB to 50 MB. Let Redshift auto-detect compression or run ANALYZE COMPRESSION on sample data first.
Frequently Asked Questions
How much data can a single Redshift node handle?
A dc2.large node with 160 GB storage typically holds 12-18 months of data for 10-15 advertising accounts. The exact amount depends on what you're tracking. If you're pulling creative-level performance with thousands of individual ads, you'll fill up storage faster than if you're only tracking campaign-level summaries.
Can I connect Redshift to Google Looker Studio?
Yes, through the PostgreSQL connector since Redshift is built on PostgreSQL. Fair warning though: Looker Studio can be sluggish when pulling large datasets directly. Most teams create summary tables in Redshift first (pre-aggregated by day or week) and connect Looker Studio to those instead of querying millions of raw rows.
Do I need a data engineer to manage this?
Not right away. If someone on your marketing team knows SQL and feels comfortable following AWS documentation, you can handle basic setup and day-to-day querying. You'll probably want engineering help once you're dealing with 50+ GB of data that needs performance tuning, or when you're building complex ETL pipelines pulling from 10+ sources, or if you need to implement proper data governance and access controls.
What happens if my Redshift cluster crashes?
Redshift takes automatic snapshots to S3 every 8 hours (you can adjust this). If your cluster fails, restoring from the most recent snapshot typically takes 15-30 minutes. For teams running mission-critical dashboards where even 30 minutes of downtime is unacceptable, you can set up Multi-AZ deployment for automatic failover, just know this doubles your infrastructure cost.
Is Redshift overkill for a small marketing team?
Probably, yeah. If you're spending under $20,000/month on ads across 5 or fewer accounts, you're better off starting with Google Sheets or BigQuery's free tier. Redshift starts making sense when you're managing 15+ ad accounts, spending 10+ hours a week on manual reporting, or when you need to join advertising data with customer data from your CRM or product database.
What about Redshift Serverless instead of managing clusters?
Redshift Serverless eliminates cluster management entirely. You just pay for the compute you use. For marketing teams that only run reports sporadically (maybe a few times per week instead of constantly), Serverless can cut costs by 30-40% compared to keeping a cluster running 24/7. The downside is there's a 5-10 second cold-start delay when the service spins up after sitting idle.
Getting Started
Start by identifying your biggest reporting pain point. If manual data consolidation takes 5+ hours weekly, automate loads. If spreadsheets crash with 100,000+ rows, Redshift handles billions without issues. If you need to join marketing data with customer databases, SQL joins make this straightforward.
The key is starting small, one data source, one cluster node, one month of historical data, then expanding as your team's SQL skills grow.
