Marketing data warehouses explained

You’ve built a solid data stack. Warehousing, orchestration and governance are all in place. But then marketing launches a new campaign, adds another platform and asks for performance insights by Friday.

The inputs? A patchwork of inconsistent naming, last-minute spreadsheet uploads and APIs that don't play by the rules. You're left decoding a taxonomy built for marketers, not for machines, and turning chaos into clarity under pressure.

This isn't a technical debt problem. It's a marketing data problem.

For BI, analytics and IT teams, supporting marketing shouldn't mean firefighting or reverse-engineering what “conversion” means for the fifth ad platform this quarter. You need a setup that brings structure, standardization and sanity to marketing data without slowing the business down.

That starts with understanding how a data warehouse works in a marketing context. Most data warehouses need a specialized marketing data integration layer like Funnel to work smoothly. Once you nail data integration + warehousing into your stack, you’re on your way to a world filled with reliable, analysis-ready, governance-friendly, and headache-free marketing data. 

What is a data warehouse?

A data warehouse is like a giant digital repository for your business data. Just like a library stores books, a warehouse stores structured data from different departments like sales, finance, product and marketing. Everything is kept in one secure place and well-organized, so it’s easy to find, compare and analyze when you need it.

Most businesses use a data warehouse because it’s more efficient than trying to manage data across lots of separate tools. Rather than paying for multiple storage systems or building custom databases for every team, you keep everything in one location. This centralized approach saves time, reduces costs and gives you a single version of the truth, no matter which team is accessing it.

In the past, data warehouses were installed and managed on-site. Today, most businesses use cloud-based platforms like Google BigQuery, Amazon Redshift or Snowflake. These tools can handle large datasets, support advanced queries and connect easily to dashboards and BI tools like Tableau or Power BI. 

Why use a warehouse for marketing data?

A warehouse lets you store your marketing data in one place, including historical data that you don’t want to lose because of data retention limits on other platforms. It holds huge amounts of data securely, so it’s an important tool for companies that want an enterprise-grade data setup.

The goal when using a data warehouse for marketing is to centralize your data in the warehouse, where it can easily be exported to analytics and reporting tools. That way, teams can analyze performance across channels, track trends over time and connect marketing efforts to business outcomes.

Because of the speed at which decisions need to happen, data warehousing is quickly becoming a key priority for marketers. In fact, the global market was valued at over $34 billion in 2024 and could more than double by 2033. 

Businesses are moving toward systems that support fast, structured and comprehensive marketing analytics. This is because data is king, and without reliable pipelines, you just can’t get the holistic overview and depth of insights you need to be a strategic competitor in 2025 and beyond.

However, because of naming inconsistencies, schema drift and other common marketing data shenanigans, a data transformation layer is the natural and often must-have complement to a data warehouse for marketers.

Warehouses work best with data that is structured and fairly consistent — think sales transactions, product inventory or website traffic logs — but marketing data is anything but consistent. So, most successful data warehouse setups rely on a data integration tool like Funnel to make marketing data ready for reporting. 

Can’t I just use a marketing data warehouse?

Technically, there aren’t dedicated marketing data warehouses. Warehouses store different types of business data, not just marketing. So when you hear the term “marketing data warehouse,” what that usually means is a data warehouse that’s being used for marketing data.

There’s also an alternative option for businesses that don’t need a separate data warehouse in their pipeline. Funnel is a marketing intelligence platformdata integration layer that extracts, transforms and loads clean data into a data warehouse. But for some companies, it can also be used as a marketing data warehouse on its own. So if you’re looking for a data warehouse solution just for marketing data, make sure to consider Funnel. It may just offers the long-term storage and structuring you need - without requiring a separate warehouse. 

Why you need a transformation layer with your data warehouse for marketing data

Your data warehouse alone just isn’t enough to make your marketing data usable. Here’s just one example of why: 

Let’s say you’re running Meta Ads. The IT team plugged your campaign into the warehouse via APIs, and when you log in each day for your stand-up, you can see the campaign results on your Power BI app. Great, right?

Sure, it is great. Until a small API change happens overnight, like Meta renaming a metric. Then, suddenly, your dashboard breaks, the insights are gone and the developers’ day goes out the window. They scramble to reconfigure the API and correct (or, in some cases, rebuild) the dashboard instead of spending their time on more strategic tasks.

A data warehouse only stores exactly what you give it. Without active monitoring and auto-remediation, you’re left fixing problems like this constantly. It’s not just the manual management of API’s and schema drift, it’s the downstream damage they cause, like error messages on the dashboard you rely on to determine where your next dollar goes.

Here’s another way to look at it: you can’t toss ingredients in a pot, turn the heat on, and expect to make chicken soup. You need everything that goes into that pot to make sense, first. So, you brown the meat, sweat the onions and chop the veggies. All your inputs are properly transformed before you start cooking everything together.

In the same way, you can’t just load all of your marketing data into a warehouse and expect it to provide you with revenue-boosting revelations. For a warehouse to truly support marketing, the data needs to be clean, connected and ready for analysis.

That’s where your insights truly get cooking. 

What a data warehouse CAN do for marketing data

Marketing teams deal with a stack of tools, platforms and channels that never really stops growing. Bringing all that data together in one place is one of the biggest challenges in marketing measurement, and that’s before you even start to tackle the issue of trying to analyze it.

Let’s look at three reasons why businesses have turned to data warehousing to solve some of these marketing use cases:

1. Centralized reporting across platforms

A data warehouse gives marketing teams a single place to combine data from Google Ads, Meta, LinkedIn, email platforms and more. Instead of jumping between platforms or exporting data manually, teams can track all performance data side by side. This makes it easier to compare channels, measure ROI and spot trends.

2. Long-term access to historical data

Most ad platforms limit how far back you can query data. A data warehouse stores that data indefinitely. That means marketers can run year-over-year comparisons, build trend reports and feed consistent data into attribution models without worrying about what platforms do or don’t keep.

3. Shared visibility with the rest of the business

Because data warehouses are used across teams, marketing data can finally live alongside sales, finance and product data. That creates a clearer picture of how marketing drives revenue or how campaigns impact customer behavior. It also makes it easier for your analysts and BI team to include marketing data in dashboards and forecasting models.

4. Protection against platform retention limits

Storing raw data in a warehouse protects against platform retention limits, that is, how much historical data platforms like Meta and GA4 will store for you. Combining your data warehouse with a tool like Funnel, which stores unprocessed marketing data before transformation, you gain unique levels of flexibility to revise logic without ever losing history. That means simple rollback, version history storage and easy comparison reporting against previous periods.

But while a data warehouse is powerful, it is not built to handle everything marketing teams need. When marketing teams bypass integration processes, spreadsheets proliferate, metrics diverge and no one knows what number to trust. Centralizing data in a warehouse helps — but only when paired with strong governance and access controls like those Funnel provides.

What a data warehouse CAN’T do for marketing

While a data warehouse gives marketers a place to store and analyze data, it does not handle the steps needed to make that data usable. 

Here’s where data warehouses fall short for marketing teams:

They don’t collect data from marketing platforms

A warehouse can’t pull data directly from ad platforms, CRMs or analytics tools. That requires setting up and maintaining external connectors or ETL pipelines, which can be time-consuming and require engineering support.

Funnel makes data collection easy with hundreds of connectors that offer pre-defined schemas, automatic API updates, and built-in quota management.

They don’t clean or standardize data automatically

Marketing data arrives in different formats, with changing schemas and inconsistent naming. A warehouse stores the data as-is. It doesn’t organize metrics, fix naming issues or apply logic across sources, which means teams have to do a lot of manual work before the data is usable or use a data integration tool.

One platform’s ‘conversion’ might include clicks, and another, only purchases. Without harmonizing these definitions upstream, your warehouse becomes a dumping ground for misaligned metrics, not a source of truth.

They don’t refresh data automatically

A warehouse holds data, but it doesn’t keep it up to date on its own. Unless you have automated systems feeding in fresh data, reports can quickly become outdated. This is a problem for teams that need accurate, always-on dashboards.

These gaps are why many teams pair their warehouse with Funnel: a marketing data integration tool purpose-built to handle collection, cleaning and delivery at scale.

How do I choose the right data warehouse to work with my marketing data?

The data warehouse you choose should match the way your business uses and scales its marketing data. The right solution depends on how complex your data sources are, how often your team queries them and who needs access across the organization.

There are various options available, and some of them can be very technical and require a lot of effort to maintain. But modern solutions are designed to be user-friendly. 

Data warehouses can range from small, department-specific setups to large-scale, enterprise-grade systems that connect marketing with finance, sales and product teams. You can start small with just a handful of sources and expand as your needs grow.

Whichever way you go, your choice of platform should reflect your data maturity and operational needs. If your team is ramping up campaign tracking, attribution or multi-channel analytics, those factors should guide your selection.

The most popular data warehousing solutions

The most popular cloud-based data warehouse products, such as Snowflake, Google BigQuery, Microsoft Azure and Amazon Redshift, offer columnar storage and data pipelining. This results in fast query performance, cost-effectiveness and streamlined analytics data pipelines. So, as all of these cloud data warehouses are well equipped to cover most business cases, any one of them can be a good starting point if this is your first time building one.

These platforms support large volumes of marketing data and can be configured to handle semi-structured formats common in ad and analytics platforms. They also integrate with most modern BI tools (like Power BI, Tableau or Google Looker Studio), orchestration platforms and identity management systems.

Most of these platforms are pretty similar to each other, so to help you make a better decision on the data warehouse you want, try to narrow down your choices to one or two. Start by understanding the type of data you wish to store and how much storage capacity you will need. You will also need to plan what kind of queries you want to execute and how that need will grow in the coming one to two years. That should give you enough information to choose the right one for your business.

Some marketing data pipelines are more storage-heavy, others more query-intensive. Knowing how your team consumes data, like daily reports, real-time dashboards or ad hoc queries, will help you match the platform’s performance model to your usage patterns.

Check within your organization and talk to your IT team; the best cloud-based solution might already be available and is being used by other departments. Reach out to your trusted advisor or technology partner to get expert advice to decide which data warehousing solution is best for your business.

For some ideas on what kind of questions to bring to IT, consider the following.

What to evaluate before making a decision

When it comes to choosing the right data warehouse for your business, you’ll want to document the following so you can compare apples with apples across vendors:

1. Define the use case: Decide on what kind of outcomes you want to achieve and if there will be other teams involved. This information can help design the data architecture to drive these outcomes and support other teams. If there are special requirements such as data storage location, security certifications, or row/column level security, make sure to check if the data warehouse vendor offers that before making the decision.
2. Weigh up the data cost: Storing big data can be cheap using file storage, but the cost of processing can pile up in the long run if you do not plan correctly.
3. Query performance: Decide on how much data you will process, how long you can wait for that process, and how much you are willing to spend. Some platforms might charge a premium for varying levels of performance.
4. Factor in maintenance: Plan for who will maintain and track the adding/removing of metrics, adding/removing of access, and if that will be done in-house or in collaboration with a technology partner.
5. Discern vendors: Keep in mind which business productivity suite is being used within the organization. If you are a Microsoft enterprise customer, there is an advantage to choosing Azure, as your organization might already have some technical knowledge and an agreement in place.
6. Decide if you’ll build or buy: Consider your internal resources. Is your team equipped to manage configurations, monitor pipelines and troubleshoot API failures, or will you need a managed layer on top of your warehouse to simplify operations?

In addition to these factors, you should also be aware that selecting a warehouse isn’t just a technical decision. It’s about matching your infrastructure to your business needs. That means weighing factors like refresh frequency, campaign attribution depth and how much data volume or concurrency your reporting requires.

Here’s an overview to help you evaluate your options from a technical perspective:

Regardless of which warehouse you choose, Funnel abstracts the complexity of getting your marketing data there. It helps you maintain a clean separation of concerns: marketers own data definitions and business logic, analysts build reports and engineers stay out of the daily pipeline firefighting.

How can Funnel help export your marketing data to a data warehouse?

Funnel simplifies one of the most complex and failure-prone steps in the marketing data pipeline: exporting clean, harmonized data from hundreds of platforms into your warehouse. It acts as the operational layer between fragmented marketing platforms and structured analytics environments.

Funnel supports multiple export destinations, including cloud data warehouses, file storage systems and on-premise servers, so your team can choose the architecture that best fits your reporting and engineering needs.

Export options tailored to your data architecture

You can export data from Funnel to:

• Cloud-based data warehouses like BigQuery or Snowflake via direct integrations
• File storage platforms such as Google Cloud Storage, Amazon S3 or Azure Blob Storage for downstream ingestion
• On-premise environments via secure SFTP — a good option for organizations with stricter infrastructure or compliance requirements

Each delivery method gives you control over how and where data is processed, enriched and routed, reducing the need for brittle ETL code or duplicated logic across teams. Let’s look at these export options in detail. 

1. Direct-to-warehouse delivery (e.g., BigQuery, Snowflake)

Funnel offers managed connections to all the major cloud data warehouses. These integrations are designed to be plug-and-play, removing the need for custom ETL or SQL scripting.

• Define schemas, select or remove fields, and manage exports through Funnel’s UI.
• New fields automatically propagate to your warehouse, ready for reporting.
• No transformation scripts required, freeing up resources while improving agility.

This approach is ideal for teams prioritizing scalability and speed, without compromising on data governance or consistency.

Case in point: Sephora

Sephora streamlined its ecommerce analytics by integrating Funnel with its cloud warehouse. By reducing the overhead of data transformation, their team could focus more on business intelligence and less on engineering handoffs.

2. File-based delivery for custom pipelines (e.g., Redshift, Azure Synapse)

If your organization uses a data warehouse that Funnel doesn’t connect to directly, such as Redshift, Azure Synapse, Teradata or Vertica, Funnel supports exporting data to a staging layer first. This gives you full control over how data is enriched and loaded into your warehouse.

Supported export options include:

• Google Cloud Storage, to be ingested via Cloud Data Fusion.
• AWS S3, with AWS Glue or your own custom ingestion logic.
• Azure Blob Storage, integrated through Azure Data Factory.

This approach is ideal for teams with custom pipelines who want flexibility in managing schema transformations, validation or enrichment before data enters the warehouse.

Case in point: Regatta

Regatta, a leading retail brand, uses Funnel to export harmonized campaign data to AWS S3. From there, their internal pipelines manage transformation and ingestion into their preferred warehouse, allowing their analytics team to retain full control over structure and timing.

3. SFTP delivery for on-premise and secure environments

For teams working in tightly controlled IT environments, such as finance, government or legacy systems, Funnel supports exports via SFTP directly to your internal servers.

This method is ideal when:

• Data cannot leave private infrastructure due to compliance or policy.
• You rely on batch imports into on-prem systems.
• BI or analytics tools monitor designated file drop zones.

Exports via SFTP maintain compatibility with internal tools and allow for precise control over ingestion logic.

Case in point: Appsflyer

Appsflyer’s data team used Funnel to securely transfer harmonized data to their internal systems, ensuring marketing and product analytics were unified without introducing instability or extra overhead.

Here’s what Elad Stauberg, Senior Marketing Operations Analyst, had to say about Appsflyer’s experience with Funnel: 

"The benefits are manifold. Not only are we saving countless hours each month, but the streamlined data and reporting are directly contributing to better decision-making and budget optimization."

Turn your warehouse into a strategic marketing asset with Funnel

A data warehouse is a powerful foundation for marketing analysis, but it isn’t built to handle the messy, fast-moving reality of marketing data on its own. That’s where a tool like Funnel makes the difference.

Funnel handles the collection, standardization and delivery of marketing data so your warehouse can do what it does best: store and organize it for long-term reporting. Together, they give you a system that’s scalable, reliable and ready to power marketing decisions.

The result? Fewer data headaches, faster insights and more time spent optimizing campaigns instead of fixing broken pipelines.

Whether your team needs plug-and-play access to BigQuery or a custom export flow through S3 and Glue, Funnel gives you the control and flexibility to make your marketing data warehouse work the way you need it to.

Choose your destination and follow the right export path with Funnel’s flexible delivery options.

But what if you don’t already have a data warehouse and you’re only looking for a warehousing solution for your marketing data? Funnel can help there, too. Some companies use Funnel as a marketing data warehouse because they don’t require a complete ETL and data warehouse setup for other business data.

Here’s how Funnel can act as a stand-alone data warehouse for your marketing data: 

• Pulls data from hundreds of sources, including Google Analytics 4, Facebook, Meta, Salesforce, and Hubspot
• Stores and structures marketing data so it can be queried
• Long-term storage for historical context and scalability
• Exports to desired destinations for analysis and reporting

Build a marketing data foundation that scales

Using a data warehouse for your marketing data gives your team the structure, visibility and flexibility needed to support better decisions across the business. But the value only comes when that data is consistent, connected and easy to work with. 

Your data warehouse helps you centralize and scale your data. Funnel ensures what you store is clean, current and consistent so your warehouse becomes a reliable foundation, not a graveyard for messy marketing metrics.

FAQ

What’s the difference between a data warehouse and a data lake?

A data warehouse stores structured, cleaned data for fast analytics, while a data lake holds raw or semi-structured data for flexible exploration, machine learning or advanced modeling. Businesses use warehouses for stable reporting but turn to data lakes when they need scale, variety or looser schema requirements. Many modern stacks use both.

Can a data warehouse replace a marketing dashboard?

No. A data warehouse stores and organizes data but doesn’t visualize it. You still need dashboards or BI tools to turn that data into reports. Funnel helps make sure the data is complete and clean before it reaches your warehouse, so what shows up in your dashboard is consistent and trustworthy.

Can you modify marketing data in a data warehouse?

Not easily. A data warehouse is designed to store stable, analysis-ready data. It’s not meant to be a place for reshaping or normalizing it. Most teams handle transformation before data enters the warehouse using ETL/ELT solutions or specialized marketing data integration tools like Funnel. This keeps warehouse data consistent, reliable and easier to query at scale.