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Marketing teams today collect data from dozens of sources—ad platforms, CRMs, email tools, analytics platforms, and more. But scattered data leads to incomplete insights and wasted ad spend. A marketing data warehouse brings all this information together, giving you a single source of truth for attribution, reporting, and optimization.
This guide covers the top marketing data warehouse solutions for 2026, evaluated on integration capabilities, ease of use, real-time processing, and value for marketing teams. Whether you need enterprise-scale infrastructure or a purpose-built attribution platform, you'll find the right fit here.
Best for: Marketing teams who need attribution insights, not just data storage
Cometly is an AI-powered marketing attribution platform that unifies data from ad platforms, CRMs, and websites to track customer journeys and optimize ad spend in real time.
Unlike traditional data warehouses that store information without context, Cometly is built specifically for marketers who need to understand what's driving conversions. It captures every touchpoint across your marketing funnel and connects them to actual revenue outcomes.
The platform's AI analyzes your unified data to surface actionable recommendations—showing you which campaigns to scale, which audiences convert best, and where to reallocate budget. This means you get insights immediately, not after weeks of custom query writing.
Multi-Touch Attribution: Track customer journeys across all marketing touchpoints to see the complete path to conversion.
Server-Side Tracking: Bypass browser limitations and ad blockers for accurate data collection that captures what client-side tracking misses.
AI Campaign Recommendations: Get specific suggestions on which ads and audiences to scale based on actual conversion data.
Conversion Sync: Feed enriched conversion data back to Meta, Google, and other ad platforms to improve their targeting algorithms.
Real-Time Analytics Dashboard: Monitor campaign performance as it happens with live data updates across all channels.
Marketing teams and agencies running multi-channel paid advertising campaigns who want attribution insights without building custom data infrastructure. Ideal if you need to prove marketing ROI and optimize ad spend based on actual customer journey data.
Custom pricing based on ad spend volume. Demo available to explore features and discuss pricing tailored to your marketing budget.
Best for: Enterprise teams needing unlimited scale and cross-organizational data sharing
Snowflake is a cloud-native data warehouse platform with extensive marketing data connectors and near-unlimited scalability for enterprise teams.
Snowflake's architecture separates storage from compute, meaning you can store massive amounts of marketing data affordably while only paying for processing power when you actually run queries. This makes it cost-effective for organizations with large datasets but variable analysis needs.
The platform excels at handling both structured campaign data and semi-structured event logs from various marketing tools. Its data sharing capabilities let you securely share datasets with agencies, partners, or other business units without copying data.
Storage-Compute Separation: Pay only for the storage and processing you use, with independent scaling for each.
Native Marketing Connectors: Pre-built integrations for major ad platforms, analytics tools, and CRM systems.
Cross-Organization Data Sharing: Share live datasets with external partners without ETL processes or data duplication.
Multi-Format Support: Handle JSON, Avro, Parquet, and other semi-structured formats alongside traditional tables.
Enterprise Security: End-to-end encryption, role-based access control, and compliance certifications for regulated industries.
Large marketing organizations with complex data needs, multiple teams accessing shared datasets, and requirements for enterprise-grade security and governance. Works well if you have dedicated data engineering resources.
Usage-based model starting around two dollars per credit, with varying consumption based on compute usage. Storage priced separately at approximately forty dollars per terabyte monthly.
Best for: Teams heavily invested in Google's advertising and analytics ecosystem
Google BigQuery is a serverless, highly scalable data warehouse with native integrations to Google Ads, GA4, and the broader Google Cloud ecosystem.
If you're running Google Ads campaigns or using GA4 for analytics, BigQuery offers the tightest possible integration. You can export raw Google Ads and GA4 data directly into BigQuery with a few clicks, giving you access to event-level detail that standard reporting interfaces don't surface.
The serverless architecture means you never manage infrastructure. BigQuery automatically scales to handle your query load, whether you're analyzing a few thousand rows or billions of ad impressions.
Native Google Platform Exports: One-click data exports from Google Ads and GA4 with automatic schema updates.
Serverless Auto-Scaling: No cluster management required—queries scale automatically based on complexity and data volume.
BigQuery ML Integration: Build and deploy machine learning models directly on your marketing data using SQL.
Real-Time Streaming: Insert live event data with sub-second latency for real-time campaign monitoring.
Generous Free Tier: First terabyte of queries free monthly, plus ten gigabytes of free storage.
Marketing teams already using Google Ads and GA4 who want deeper analysis of campaign performance and customer behavior. Particularly valuable if you have SQL skills but limited data engineering resources.
Pay-per-query model with first terabyte of queries free monthly. Storage costs approximately two cents per gigabyte monthly. Flat-rate pricing available for predictable high-volume workloads.
Best for: Teams building custom attribution models with machine learning
Databricks is a unified analytics platform combining data warehouse capabilities with advanced machine learning for teams building custom attribution models.
Databricks bridges the gap between traditional data warehousing and advanced analytics. Its lakehouse architecture lets you store raw marketing data in open formats while still getting warehouse-like query performance.
The platform truly shines when you need to build sophisticated models beyond standard attribution. Think custom propensity scoring, lifetime value prediction, or multi-touch attribution algorithms tailored to your specific customer journey. The collaborative notebook environment makes it easy for data scientists and analysts to work together.
Lakehouse Architecture: Combine the flexibility of data lakes with the performance and structure of warehouses.
Native ML Integration: Built-in machine learning frameworks and AutoML capabilities for predictive marketing models.
Delta Lake Technology: ACID transactions and time travel features for reliable data management.
Collaborative Notebooks: Shared workspaces where teams can develop queries, models, and visualizations together.
Multi-Language Support: Work seamlessly in Python, SQL, R, or Scala depending on your team's expertise.
Marketing organizations with data science teams who need to build custom attribution models, predictive analytics, or advanced segmentation beyond what off-the-shelf tools provide.
Usage-based pricing starting around seven cents per DBU for jobs compute. All-purpose compute costs more. Requires underlying cloud infrastructure costs on AWS, Azure, or GCP.
Best for: Automating data pipeline management without engineering overhead
Fivetran is an automated data integration platform with pre-built connectors for marketing tools, designed to pipe data into your warehouse of choice.
Fivetran isn't a warehouse itself—it's the bridge that gets your marketing data into one. The platform excels at eliminating the engineering work typically required to maintain data pipelines. When Facebook Ads updates their API or Google Analytics changes their schema, Fivetran automatically adapts.
The incremental syncing approach means you're not constantly re-importing entire datasets. Fivetran tracks what's changed and only moves new or updated records, making data refreshes fast and cost-effective.
300+ Pre-Built Connectors: Ready-made integrations for major ad platforms, CRMs, email tools, and analytics systems.
Automatic Schema Management: Connectors update automatically when source systems change their data structure.
Incremental Data Syncing: Only new or changed data gets transferred, reducing processing time and costs.
Multi-Warehouse Support: Works with Snowflake, BigQuery, Redshift, Databricks, and other major platforms.
Transformation Layer: Built-in dbt integration for modeling and transforming data after extraction.
Marketing teams who've chosen a data warehouse but need a reliable way to get data from multiple marketing tools into it without building custom integrations.
Based on monthly active rows processed. Standard plan starts at one dollar per million active rows. Credit-based pricing available for larger volumes.
Best for: Organizations already invested in the AWS ecosystem
Amazon Redshift is an AWS-native data warehouse offering cost-effective storage and querying for organizations already invested in the Amazon ecosystem.
If your company already runs infrastructure on AWS, Redshift integrates seamlessly with your existing setup. Data stored in S3 buckets can be queried directly through Redshift Spectrum without moving it into the warehouse, saving on storage costs for infrequently accessed historical data.
The Redshift Serverless option removes cluster management complexity, automatically scaling compute resources based on your query workload. This makes it practical for marketing teams with variable analysis needs throughout the month.
Deep AWS Integration: Native connections to S3, Lambda, Kinesis, and other AWS services marketers commonly use.
Redshift Serverless: No infrastructure management required—compute scales automatically with demand.
Materialized Views: Pre-compute complex marketing reports for instant dashboard loading.
Federated Queries: Query across Redshift, RDS databases, and Aurora without data movement.
Spectrum for S3 Queries: Analyze data directly in S3 without loading it into the warehouse first.
Marketing organizations already using AWS for other infrastructure who want tight integration with existing systems and predictable AWS billing.
On-demand pricing starts at twenty-five cents per hour for small clusters. Serverless pricing at thirty-six cents per RPU-hour. Reserved instances offer significant discounts for committed usage.
Best for: Real-time event collection with identity resolution across channels
Segment is a customer data platform that collects, cleans, and routes marketing data to warehouses while providing identity resolution and real-time event tracking.
Segment sits between your marketing tools and your data warehouse, acting as a central collection point for customer interactions. It excels at tracking events in real time—when someone clicks an ad, visits your site, or converts, that data flows through Segment immediately.
The identity resolution feature is particularly valuable for marketing attribution. Segment stitches together anonymous sessions, known user profiles, and cross-device activity into unified customer profiles, giving you a clearer picture of the customer journey.
Real-Time Event Tracking: Capture customer interactions as they happen across web, mobile, and server-side sources.
Identity Resolution: Merge anonymous and known user data across devices and channels into unified profiles.
Warehouse Sync: Automatically route cleaned, structured data to Snowflake, BigQuery, or Redshift.
Protocols for Data Quality: Enforce tracking plans to ensure consistent, accurate event data across teams.
Personas for Audiences: Build and sync audience segments to ad platforms and marketing tools.
Marketing teams who need real-time event tracking, identity resolution, and the flexibility to route data to multiple destinations including data warehouses.
Free tier supports up to one thousand visitors monthly. Team plan starts at one hundred twenty dollars per month. Business tier pricing scales with event volume.
The right marketing data warehouse depends on what you actually need from your data. If your primary goal is understanding attribution and optimizing ad spend without building custom infrastructure, Cometly delivers marketing insights out of the box. The platform captures every touchpoint, applies AI to surface recommendations, and feeds better data back to your ad platforms—all without requiring a data engineering team.
For enterprise organizations with dedicated data teams, Snowflake and BigQuery offer the scalability and flexibility to build custom solutions. Snowflake excels when you need cross-organizational data sharing and have variable compute needs. BigQuery is the natural choice if you're deeply invested in Google's advertising ecosystem and want seamless GA4 and Google Ads integration.
Teams building sophisticated custom models should consider Databricks, which combines warehouse capabilities with machine learning tools. If you've already chosen a warehouse but need help getting data into it, Fivetran automates the pipeline management. AWS-centric organizations will find Redshift integrates smoothly with existing infrastructure. And if real-time event tracking with identity resolution is your priority, Segment provides that layer before data reaches your warehouse.
The landscape has shifted from "store all your data" to "store your data and extract actionable insights." Purpose-built platforms like Cometly recognize that most marketing teams don't have months to spend building attribution models—they need to optimize campaigns today. Ready to elevate your marketing game with precision and confidence? Discover how Cometly's AI-driven recommendations can transform your ad strategy—Get your free demo today and start capturing every touchpoint to maximize your conversions.
Learn how Cometly can help you pinpoint channels driving revenue.
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