Data integration vs. ETL — Which path is right for you?

Should you use an ETL tool or a specialized marketing data integration tool for your data? 

ETL (extract, transform, load) is a type of data integration. It pulls data from a source, transforms it to match a predefined format and then loads it into a destination system like a data warehouse or dashboard.

Data integration tools made for marketing emphasize preserving raw data and deferring transformation. This lets teams store everything as-is and shape the data only when needed, offering greater flexibility and long-term value.

ETL tools work well for many business use cases, including finance, operations and healthcare data. However, because of the way the process works, there is the potential for some pretty serious issues with marketing data management. 

Picture this: you have a successful campaign, but when you go to pull the report, you open your dashboard to find that last week’s conversions have disappeared. 

This is a potential challenge when using traditional ETL for marketing data integration. When marketing data is transformed before being stored, historical context is lost. By the time numbers hit your dashboard, too much has changed. Instead of popping the champagne, you’re left explaining outcomes that no longer reflect reality.

The problem with traditional ETL for marketing 

Marketing moves faster than most companies’ data infrastructure. Teams juggle more platforms, track more granular metrics and face tighter deadlines than ever. Yet many still rely on ETL workflows designed for slower and more stable departments like finance or operations.

ETL follows a fixed sequence: extract data, transform it into a predefined format and then load it into a destination like a data warehouse. This assumes your data needs are known upfront and locks in structure before reality has a chance to unfold. While ETL still works well in stable environments, marketing requires a more flexible approach.

Marketing is anything but static. Budgets shift weekly. Attribution models evolve. APIs change without warning. 

In this context, locking data into a rigid format at the moment of extraction limits your ability to respond.

The risks this introduces are easy to overlook, until they become major blockers, like when:

1. You lose access to raw data

Once ETL applies its logic, the original values are gone. If something was calculated incorrectly or if you need to revisit old performance using a new model, there is no going back. You are stuck with what was captured at the time, even if business needs have changed.

2. Destination changes become expensive

Traditional ETL pipelines are tightly coupled to specific tools. Switching from Snowflake to BigQuery or adding a new BI tool that has unique structure requirements might require a complete rebuild. That creates bottlenecks and delays every time the tech stack evolves.

3. Inconsistencies create reporting breakdowns

Marketing data rarely arrives in a clean, uniform format. Different sources use different naming conventions, date formats and update schedules. ETL systems often apply transformation rules before reconciling these differences, which leads to mismatched metrics and mistrust in the results.

The impact on marketing and data teams isn’t good.

These issues show up in missed deadlines, broken dashboards, lost context and decision-making delays. They also prevent marketers from adapting quickly, responding confidently and proving their impact.

An integration-first model using a marketing-specific tool like Funnel flips this approach. Instead of transforming data on arrival, it stores raw source data in its original format. Transformation becomes optional, reversible and adaptable to your reporting needs as they evolve.

For marketers, this is a strategic shift away from rigid, engineering-led pipelines toward data infrastructure that’s built for speed, flexibility and autonomy.

Traditional ETL systems were built for predictability. Marketing operates in constant motion. The gap between those two realities is where most data issues begin.

Funnel was designed to close that gap. By storing raw data before transformation and putting marketers in control of structure, logic and delivery, it replaces brittle pipelines with a flexible foundation built for change.

Five ways Funnel outperforms traditional ETL

Funnel solves the problems ETL creates by taking a fundamentally different approach to how data is stored, transformed and delivered.

Here are just five of the ways Funnel works better for marketers: 

1. More agility in reporting

Funnel keeps your raw data intact, so you can recut metrics, adjust attribution or revisit past performance without rework.

How? By storing every data point as it comes in from the source, untouched and uncompressed. No transformation logic is applied until you decide what’s needed. This means nothing is lost, overwritten or permanently altered before you’ve had a chance to work with it.

When campaign strategies evolve or business questions change, your data can adapt. Want to reprocess last year’s performance with new attribution windows? Adjust metrics based on revised KPIs? Test a new segmentation model before rolling it out? All of that becomes possible without re-pulling data or rewriting pipeline logic.

This isn't just about having a backup. It's about having full command of your historical data, with the flexibility to revisit and rethink performance at any point.

2. Faster time to insight

In ETL workflows, exporting data to a new tool often means rebuilding the pipeline. Every added destination becomes a project. Funnel eliminates that barrier.

With Funnel, the same validated dataset can be delivered to multiple destinations — Looker for visualization, Google Sheets for quick ops reporting, BigQuery for modeling — without spinning up separate processes or compromising consistency.

This lets teams use the right tool for the right job without waiting on engineering. You reduce duplication, maintain data integrity across platforms and avoid the cost of pipeline sprawl.

It’s one pipeline that serves every output, not a tangle of custom jobs that break every time something changes.

3. More uptime, less stress

API changes are a fact of life in marketing. Fields are added, renamed or deprecated with little warning. For traditional ETL setups, these changes mean broken pipelines, failed jobs and emergency patches that derail your roadmap.

Funnel absorbs API or schema changes automatically, so reporting doesn’t break every time Meta or Google tweaks something. 

Instead of reacting to outages, your team stays focused on using data, not fixing it. Platform volatility becomes a background issue, not a blocker.

This resilience is what makes reliable reporting possible in a marketing environment that never stands still.

4. More trust in the numbers

With standardized naming and early error detection, marketers get accurate data they don’t have to second-guess.

Data integrity is not something you can tack on at the end of a pipeline. It has to be designed into the system from the beginning.

Funnel validates and standardizes data before export. It catches schema mismatches, enforces naming conventions and flags structural anomalies early. You can trace every transformation and understand exactly how numbers are calculated and where they came from.

This reduces risk across the board. Reports don’t break. Metrics don’t shift silently. Teams aren’t left wondering whether the dashboard matches the raw numbers.

5. More independence, less backlog

Most ETL tools assume a technical user who can write SQL, interpret pipeline failures and troubleshoot job dependencies. Funnel assumes the opposite. 

In fact, Funnel empowers marketers to explore data, build reports and answer business questions — without waiting on engineering.

You can explore performance by channel, build custom metrics, segment campaigns and push data to your preferred tools, all from a workspace that understands how marketing data behaves.

This removes the friction between marketing and technical teams. The backlog of analytics requests dwindles, and marketers have more autonomy to act on their insights.

It’s not just faster. It creates space for better decisions, stronger reporting and a marketing function that operates with confidence.

These five advantages are not just technical improvements. They remove the blockers that slow down marketing teams and replace them with a system that is faster, more reliable and easier to scale. But what does this look like in the real world?

What switching from ETL to a marketing data intelligence platform can look like

Imagine a mid-sized ecommerce company scaling its marketing across Meta, Google Ads, Pinterest and TikTok. The team tracks key metrics like ROAS, CAC and LTV across multiple markets. On the surface, everything seems to be working. Underneath, the data pipeline is strained.

They are using a custom-built ETL workflow. It extracts data from each platform, applies fixed transformation logic and loads the output into a dashboarding tool like Looker. This setup worked when the business was smaller. But as campaign complexity increased and reporting needs expanded, the cracks began to show.

Each API change risks breaking the pipeline. Each new metric request means writing SQL, testing updates and hoping nothing else fails. When data is overwritten, there's no way to recover it. Marketers spend more time waiting for fixes than analyzing performance. Engineers are stuck maintaining a fragile system instead of building new capabilities.

Now, imagine the same team using an integration-first platform purpose-built for marketing data like Funnel.

Instead of transforming data immediately, they store everything in its original form. Raw data is always accessible. New metrics or logic changes do not require a rebuild, but can simply be created in the no-code user interface, in addition to existing metrics. They can export the same validated dataset to Looker, Google Sheets and BigQuery without duplicating work or introducing errors.

The marketing team gains control. The engineering team steps out of the critical path. Reports are trusted, timelines improve and the business runs with more confidence.

You’ll see this pattern across the industry.

Power Digital and Arm Candy both outgrew Supermetrics as client demands increased. Broken reports and platform limitations made it impossible to scale. By switching to Funnel, they gained reliable pipelines, flexible delivery and full control over their data without leaning on engineers.

These aren’t outliers. They’re part of a broader shift away from rigid ETL toward integrated systems that actually match the speed and complexity of modern marketing.

Funnel makes that shift possible, and the results speak for themselves.

A better foundation for modern marketing needs

Traditional ETL was built for stability, not speed. It locks data into rigid formats, assumes your needs are fixed and breaks when the real world doesn’t cooperate. This approach can be a great fit for departments with stable data management needs and industries that want data transformed before it’s loaded into a data warehouse, like finance and healthcare, but it can cause downstream problems for marketing.

Modern marketing teams need something else: raw data on demand, flexible transformation and the ability to deliver clean, trusted numbers anywhere they’re needed. This type of pipeline is the baseline for fast, confident decision-making.

Integration-first matches how marketers already work — fast, iterative and data-driven. Because when growth is the goal, the right foundation isn’t optional. It’s everything.