CDP vs data warehouse: Which should you use and why
Aug 05, 2025
Customer data platform (CDP) vs data warehouse is sort of like Batman vs Superman for people who know SQL. They are seen as rivals and rely on different tools but often end up working together.
There are dozens of articles trying to convince you one or the other is the "right" choice, but, in reality, both (or neither) might be right for you. This article will help you understand what they do and decide whether a CDP and/or data warehouse is right for you.
What is a CDP?
Even by modern data stack standards, the name "customer data platform" is frustratingly vague – it doesn’t actually explain any of the underlying functionality.
In a nutshell, a CDP does three things:
- Ingests data from many different sources / touchpoints, like your website, app, ads, analytics, email, helpdesk, and more.
- Creates and stores a combined customer profile based on all these sources. Data scattered between platforms gets aggregated in one place.
- Sends customer data to destinations like ad platforms, CRMs, and more.
Teams need this because they:
- Have many sources of data
- Want a single source of truth for their “customer record”
- Need a combined customer record for tracking and improving ad campaigns, personalization, lifecycle marketing, and more.
A CDP’s ability to do identity resolution (i.e. figuring out what data belongs to whom) is key here.
For example, if a customer visits your website, signs up for your mobile app, and sends you an email, these three touchpoints could be treated as three separate customers. A CDP stitches these together into one customer by using persistent identifiers, relying on deterministic data (e.g. IDs and emails), and even using probabilistic data (e.g. IP address, device type, browser, and OS).
Creating unified records for all your customers makes your data much more accurate and actionable.
- Marketers sending customer segments to paid ads platforms to enhance ad targeting.
- Growth teams using segments to target personalization and experimentation (A/B testing).
- Salespeople enriching customer profiles with usage data, lifecycle marketing engagement, and more.
- Analysts getting more accurate and de-duplicated data in their analytics and business intelligence tools.
What is a data warehouse?
A data warehouse is a flexible way to:
- Store a variety of data
- Keep it for an extended period of time
- Support business decision-making.
This could include customer data (like the CDP), but also employee records, AI training data, transactions, references, and anything else you can think of. Data warehouses are like your production database, but built to store greater volumes of structured, and (sometimes) unstructured data, for longer.
It does this by having a significantly different structure from traditional databases, usually made up of three parts:
Data-in: This usually relies on extracting data from another platform, transforming it to fit existing data, and loading it into the warehouse. This is known as ETL (extract, transform, load). The warehouse itself can also extract data from sources, load it, and then run transformation jobs on it. This is known as ELT (extract, load, transform). Why they couldn't come up with more unique and less confusing acronyms is beyond me.
Data layer. Where the data lives, along with all the metadata and schema needed to make use of it. Data is segmented and, typically, governance and security rules can be set up here.
Data-out: The warehouse itself usually has systems (AKA engines) to efficiently run the type of workloads you need, for example, aggregate analytics queries.
Once you’ve extracted data from the data warehouse, you usually rely on other tools to make use of it, such as business intelligence tools for visualization or CDPs with reverse ETL functionality for activation.
- Analysts building in-depth reports and forecasting based on historical data.
- Executives viewing regular reports on KPIs, revenue, growth, churn, usage, and more using sources like analytics, payments, CRM, and more.
- ML engineers preparing and processing data for forecasting, machine learning, and AI. Prepare historic, clean datasets for ML models.
- Security and compliance teams storing access logs and audit trails for regulations like GDPR, HIPAA, SOC 2, CCPA.
How do a CDP and a data warehouse compare?
| Aspect | CDP | Data warehouse |
|---|---|---|
| When companies adopt | Early for marketing campaigns | Growth for consolidation and reporting |
| Data sources | Customer touchpoints (website, apps, ads, email) | Business systems (database, analytics, CRM) |
| Data flow | Ingest → process → activate | Ingest → store → analyze |
| Data ingestion | Primarily real-time or near real-time | Typically batched, but real-time is possible |
| Storage timeframe | Medium to long-term | Long-term and historical by design |
| Target users | GTM, marketing, sales, customer experience teams | Data engineers, analysts, compliance, executives |
| Scalability | Scales with volume but often limited by vendor pricing/models | Cloud-native warehouses scale with storage and compute needs |
| Complexity | Turnkey with lower technical lift thanks to prebuilt connectors and UI | Orchestrated and higher lift due to modeling, ETL, and schema design |
| Usage | Activation, personalization, audience segmentation | Analytics, reporting, machine learning, regulatory compliance |
| Output | Segments, customer profiles, and real-time syncs to tools (ad platforms, lifecycle marketing) | SQL queries, reports, dashboards, ML pipelines |
| Modeling | Abstracted or automated with pre-built schemas | Manual, requires dbt or SQL expertise |
| Privacy | Consent management, field-level suppression, blocking at source | Warehouse masking, row-level security, custom policies |
| Pricing model | Per event or record | Storage + compute |
Which should you choose?
Actually, you will need both eventually – it's more about when you should adopt them. If I had to graph the importance of each over time, it would look like this:
Early-stage companies likely won’t find a data warehouse useful, but a CDP quickly provides significant help collecting and activating customer data to power early marketing efforts.
As companies mature, their reporting and analysis requirements grow and increase in complexity. Doing reporting in each individual tool doesn’t cut it anymore. Having consolidated data becomes more important, and so does the data warehouse.
This doesn’t mean abandoning your CDP, though. Teams can continue using the CDP to send data to the data warehouse. This means they don’t need to rearchitect their data stack to start using a data warehouse. They can just add the data warehouse as a source.
This creates a stack that looks like this:
As a company grows and adds more non-customer data, like logs and ERP data, the data warehouse becomes increasingly important as a source of truth. This also satisfies additional access, governance, and security requirements a data warehouse can handle.
Again, a CDP remains important as a company matures; it just decreases in relative importance compared to the rest of the data stack. For example, it enables marketers to do all sorts of advanced personalization and lifecycle marketing, but they are just one of many roles needing to access the data at this point.
Another way CDPs are useful later is reverse ETL. This means getting data out of the data warehouse and into all the tools a CDP has integrations with. At this phase, the stack might look like this:
At huge scale, use cases fragment significantly. Each function will likely have their own set of specialized requirements that a data warehouse (if adopted) will likely play a large role in, but that’s beyond the scope of this post.
PostHog is both a CDP and data warehouse
If you spend any time with PostHog, you’ll quickly notice we have both a CDP (data pipelines) and a data warehouse. You might wonder: hey, I thought it was supposed to be one or the other? Well, we’ve broken that rule.
We and our customers have found both to be essential so in our effort to “equip every developer to build successful products,” we’ve built both:
Our data pipelines enable teams to send data captured into PostHog anywhere, from Slack to webhooks to lifecycle marketing platforms to data warehouses. They also enable teams to customize and transform these destinations and data before sending it.
Our data warehouse enables teams to sync data from the tools they already use like Stripe, Hubspot, Postgres, S3 and query it alongside the event data they already have in PostHog. We provide a full SQL editor as well as visualizations for this data.
When compared with either of the stacks mentioned above, PostHog enables teams to have one that looks like this:
Rather than the data spaghetti created by separate CDPs, data warehouses, and other tools, PostHog provides them all in a unified platform. This means teams have more of the tools they need as they grow and there is less need for migration or reimplementation. We can fulfill the use cases teams need whatever stage they are at, whether it is marketing campaign enrichment early or complex analysis from consolidated datasets later.
If you’re curious, see the product pages or just sign up and get started; both our CDP and data warehouse come with a generous free tier.