The 10 best data integration solutions for marketing in 2025

You’ve just wrangled five spreadsheets for marketing, renamed twenty campaign fields and built a dashboard no one will use until next week. Tomorrow? You’ll do it all again.

Marketing data is relentless — fragmented, inconsistent and always changing. Even the smartest data stacks start to crack under its weight.

Nearly 40% of analysts spend over half their work week prepping data rather than analyzing it. That’s not just inefficient — it’s expensive.

It’s time to look at which tools are solving this better, how different data integration methods stack up and what to consider when building for speed, accuracy and scale.

Here’s a look at the best data integration solutions for marketing data in 2025:

1. Funnel: Funnel is purpose-built for marketing data, reducing engineering dependencies for marketers and cutting support tickets for engineers.
2. Supermetrics: Pulls marketing data into spreadsheets and BI tools quickly with a lightweight plug-and-play setup.
3. Stitch Data: A fast, developer-friendly ETL tool for moving raw data into warehouses with minimal transformation.
4. Adverity: A flexible platform combining marketing data integration with advanced analytics and visualization tools.
5. Improvado: A customizable pipeline built for marketing teams that want more control, scalability and hands-on support.
6. Windsor.ai: Focused on attribution, unifying cross-channel marketing and customer journey data for performance insights.
7. Hevo Data: A no-code ETL platform offering real-time data syncing into modern warehouses with minimal setup.
8. Fivetran: Fivetran is a general-purpose data pipeline platform that centralizes data from across the business, supporting a wide range of use cases for both technical and non-technical teams.
9. Panoply: A no-code platform that blends data integration and warehousing for fast end-to-end analysis.
10. Airbyte: Open-source ELT with deep customization and modular connectors built for engineering-led teams.

Why automated data integration is so important for marketing data

Marketing data isn’t just scattered, it’s unstable. APIs change without warning, schemas shift frequently and most platforms only retain data for a limited time. If your pipeline breaks or lags, you risk losing valuable performance data permanently.

Automation is not just about speed. It safeguards against data loss, reduces manual cleanup and gives your team more confidence in the numbers. Modern data integration connects platforms, adapts to schema changes and delivers clean, consistent data to your warehouse or reporting tools automatically.

Generic ETL and ELT tools offer flexibility, but they aren’t built with marketing in mind. They require engineers to manually define schedules, handle schema drift and write transformations that make sense of campaign-level data. The big problem here is that workflows often need constant tuning to account for fast-moving changes in naming conventions, attribution windows or spend structures.

This raises a familiar question: build or buy? Building gives you full control. However, it also locks your team into ongoing maintenance. Every new campaign, every updated API, every change in business logic becomes another ticket. Over time, this adds up to significant tech debt and slows down your team.

Buying a managed solution shifts that responsibility. It gives you ready-to-use connectors, built-in logic for marketing platforms and automation that adapts as data changes. Instead of fixing pipelines, your team can focus on modeling, analysis and decision-making.

Some tools require code. Others are low-code or no-code and designed for domain experts. The key is choosing one that can cope with the unique challenges of marketing data and removes the operational overhead that slows teams down.

Choosing the right integration approach isn't just about what data you need. It also depends on how your team works, how quickly your platforms evolve and how much technical overhead you’re prepared to manage. Each type of integration solution comes with its own trade-offs in flexibility, usability and long-term ownership.

Types of data integration solutions

Many data integration tools provide comprehensive features, but they aren’t always fit for purpose when it comes to marketing data.

Some are built for engineers. Some are built for marketers. And some try to do everything but end up requiring more work than they save.

Here’s how the main categories break down, what they’re good for and where they fall short for marketing teams that need fast, flexible access to performance data.

Marketing-specific ETL tools

Examples: Adverity and Supermetrics

These tools speak to marketers. With built-in connectors for ad platforms, analytics tools and CRMs, they make it easy to pull data into dashboards or spreadsheets — no code required. They’re great for speed and accessibility but hit limits when it comes to complex transformations, orchestration or scaling across large teams.

Generic ETL and ELT tools

Examples: Fivetran, Airbyte, Stitch Data and Hevo Data

These tools are flexible, powerful and built for engineering teams. They support a wide range of sources and destinations, making them ideal for centralizing business data across departments. But with that flexibility comes responsibility — engineers must manage everything from pipeline logic and retry handling to schema drift and transformation scripts. Without deep marketing context, valuable campaign data can become delayed or misaligned.

Integrated data platforms with warehousing

Example: Panoply

This type of platform bundles data ingestion and storage into a single product. That makes setup easy, especially for smaller teams with limited resources. But the simplicity can become a bottleneck when marketing teams need more control, customization or advanced data governance tools built into the stack.

Marketing attribution tools

Example: Windsor.ai

Attribution tools are built to measure performance. They ingest data from marketing channels and apply proprietary models to show how touchpoints drive conversions. While helpful for top-line insights, their integrations are tightly scoped and rarely support broader marketing analytics needs outside their own ecosystem.

Purpose-built marketing data integration

Example: Funnel

Funnel is purpose-built for marketing data integration. It handles the collection, storage and transformation of data from hundreds of platforms, with built-in features tailored to the quirks of marketing sources. Its key differentiator is the ability to apply transformations to stored historical data, allowing teams to clean, enrich and align campaign data even after it's been collected. This reduces backfills, reprocessing and manual schema updates. For marketers, it means more autonomy. For engineers, it means fewer support tickets and more time to focus on high-impact work.

Understanding the types of tools you can use is one piece of the puzzle. Just as important is how they move and process data, whether through ETL, ELT or more modern, marketing-friendly approaches.

Comparison of data processing approaches

The way we move and process data has evolved significantly over the last two decades.

ETL came first, designed at a time when storage was expensive and compute power was limited. Teams needed to clean and structure the data before loading it into costly warehouses. This approach made sense when inputs were stable and predictable.

As cloud data platforms grew, ELT became more popular. It flipped the process to extract and load raw data first, then transform it inside the warehouse using SQL or tools like dbt. This gave analysts more flexibility and allowed teams to store everything, just in case they needed it later.

This is where modern data integration comes in. Instead of relying on rigid, batch-driven logic, modern pipelines are designed to handle the messy reality of marketing data:

• They capture API data exactly as it’s received, preserving all fields, formats and values without forcing it into a predefined structure.

• They store that raw data immediately and securely, keeping it separate from transformation steps. This protects against data loss if a pipeline breaks and allows teams to reprocess historical data when needed.

• They apply monitored, incremental transformations, which continuously update dashboards and reports as new data comes in. This makes it easier to trace issues and ensures teams are always working with up-to-date numbers.

For engineering teams, this means fewer surprises and less reprocessing. For marketing teams, it means cleaner data, faster access and more trust in the numbers.

Here's a breakdown of how data processing using ETL, ELT and modern data integration solutions differs:

So, how do these different integration solutions handle processing behind the scenes? Factors like how they adapt to API changes and how much operational effort they require can impact everything from data freshness to team bandwidth.

Automated data integration platforms tend to offer the best experience for most marketing teams. They strike a balance between control and convenience, delivering reliable data with far less manual work. 

Let’s dive into how the top data integration solutions manage complexity behind the scenes so your team can focus on results. 

Top 10 data integration solutions for 2025

Knowing which tools solve which problems is critical when the cost of the wrong choice is time, complexity or lost visibility.

This breakdown highlights the top 10 data integration solutions for 2025, comparing how they handle functionality, usability and support for marketing data.

Here’s what sets each platform apart:

1. Funnel

What it is:

A fully managed platform built to collect, unify and send marketing data from hundreds of ad, analytics and CRM platforms to destinations like BI tools, spreadsheets or data warehouses.

Why it works:

Funnel is purpose-built for marketing teams. It requires no code and no manual data prep, freeing up engineering resources for high-level tasks. With prebuilt, managed connectors, automated data mapping and a marketer-friendly interface, teams can unify fragmented data fast and at scale.

Key features:

• Built specifically for marketing data.
• Stores and enriches historical data for deep analysis.
• Supports complex transformations without external tools.
• Fast time to value without stressing engineers.
• Scales across dozens or hundreds of data sources.
• Unified view across platforms and channels.
• Clean UI and workflow designed for marketers.

What sets it apart:

Unlike generic ETL tools or lightweight connectors, Funnel lets you apply powerful transformations directly to historical marketing data. This unlocks more flexible, scalable and insight-rich analytics, without needing a separate transformation layer or custom workflows.

Takeaway:

Funnel is easy to use for marketing, so data teams aren’t pulled in for day-to-day needs. With fully managed connections, you get data you can trust, and unique features allow for deeper insights and more strategic reporting than you’d get out-of-the-box with tools limited to generalized in-transit processing.

2. Supermetrics 

What it is:

A lightweight connector tool that pulls data from popular advertising and analytics platforms into spreadsheets, data warehouses and reporting tools.

Why it works:

Supermetrics offers a fast setup and integrates directly with tools like Google Sheets, Excel and Looker Studio. It’s a straightforward choice for teams that want to move data without building custom pipelines.

Key features:

• Focuses on common destinations like Sheets and Excel.
• Easy to set up with minimal configuration.
• Supports a wide range of advertising platforms.
• Works well for recurring reports and dashboards.
• Popular with small teams and agencies.

What to consider:

Supermetrics is efficient for simple use cases but lacks advanced transformation, historical data management or workflow orchestration. It requires more effort to scale across many sources or support deeper analysis.

Takeaway:

Supermetrics is a fast, no-frills option for moving marketing data into familiar tools, best suited for teams with lightweight reporting needs.

3. Stitch Data

What it is:

A cloud-based ETL platform that extracts data from a wide range of sources and loads it into data warehouses like BigQuery, Snowflake or Redshift.

Why it works:

Stitch is simple to set up, requires little infrastructure management and offers transparent volume-based pricing. Its connector library includes many marketing tools, though the platform is designed for general-purpose use cases across business functions.

Key features:

• Easy setup with low engineering overhead.
• Connects to dozens of marketing and business systems.
• Sends data to major warehouses like Snowflake and BigQuery.
• Clear, usage-based pricing.
• Useful for centralizing data without building custom pipelines.

What to consider:

Stitch is not purpose-built for marketing and focuses on basic extraction and loading. It offers limited support for transformation or historical enrichment, so you might need additional tools for marketing-specific reporting and analysis.

Takeaway:

Stitch Data is a capable ETL solution for general data movement but may fall short for marketing teams that need tailored logic, enrichment or campaign-level visibility.

4. Adverity

What it is:

A data integration and analytics platform built around marketing intelligence. It combines data pipelines with built-in dashboards for visualizing performance across channels.

Why it works:

Adverity offers a wide range of out-of-the-box connectors for advertising and analytics platforms. It includes a native analytics layer, allowing users to explore and report on marketing performance without switching tools.

Key features:

• Combines data integration with built-in dashboards and reporting.
• Offers a central workspace to build cross-channel metrics.
• Supports advanced data transformations and custom logic.
• Enables campaign-level performance tracking out of the box.
• Designed to serve both marketing and data operations teams.

What to consider:

Adverity is feature-rich but can require configuration to fully align with specific reporting needs. Some teams may find the bundled analytics layer more rigid than using their preferred BI tools.

Takeaway:

Adverity is a strong choice for teams that want both data integration and built-in marketing analytics, though it may be best suited to organizations willing to adopt its full ecosystem.

5. Improvado

What it is:

A marketing data aggregation platform that consolidates and standardizes data from a wide range of advertising, analytics and CRM sources.

Why it works:

Improvado supports large data volumes and is often used in complex marketing environments with custom reporting requirements. It’s known for offering dedicated support and flexibility for teams with specific configuration needs.

Key features:

• Centralizes marketing data from hundreds of platforms.
• Allows for custom metric definitions and data mappings.
• Scales well for multi-brand or multi-region use cases.
• Offers dedicated onboarding and customer success resources.
• Integrates with most major BI and warehouse tools.

What to consider:

Improvado offers flexibility but often requires more hands-on setup and ongoing involvement from data teams. It may be better suited for organizations with existing technical resources and highly customized reporting needs.

Takeaway:

Improvado is a flexible option for teams with complex marketing data requirements, though it typically requires more technical input and is less turnkey than platforms focused on ease of use and fast setup.

6. Windsor.ai

What it is:

A marketing data pipeline and attribution platform that centralizes data from ad, web and CRM platforms and helps model multi-touch attribution.

Why it works:

Windsor.ai focuses on connecting channel-level performance data with attribution modeling to give teams a clearer view of what drives conversions across touchpoints. It’s often used by performance marketers looking to optimize spend across complex customer journeys.

Key features:

• Connects to ad, analytics and CRM platforms.
• Supports multi-touch attribution and custom conversion paths.
• Offers prebuilt connectors and a web-based interface.
• Integrates with BI tools and data warehouses.
• Enables granular performance insights across channels.

What to consider:

Windsor.ai is attribution-led and may require additional setup to support broader marketing reporting or full data transformation needs. It can be a good fit for performance teams but may not replace a full-scale integration platform.

Takeaway:

Windsor.ai is a useful option for marketers focused on attribution and channel optimization, though it may not provide the same breadth or ease of use as platforms built for full marketing data integration.

7. Hevo Data

What it is:

A no-code data integration platform that consolidates data from various cloud-based sources, including some marketing platforms, into destinations like BigQuery, Redshift and Snowflake.

Why it works:

Hevo offers real-time data syncing, automated schema handling and a user-friendly interface. It’s often used by teams looking for a fast way to unify cloud data without needing to write or maintain code.

Key features:

• Connects to cloud-based tools including CRM, finance and some marketing sources.
• Real-time syncing with built-in monitoring and alerts.
• No-code interface designed for both technical and non-technical users.
• Automated schema detection and adjustment.
• Compatible with popular data warehouse environments.

What to consider:

Hevo supports marketing data but is not tailored to it. It lacks prebuilt logic or features specific to campaign-level marketing analytics, which may limit its usefulness for marketing teams with complex data needs.

Takeaway:

Hevo Data is a solid no-code integration tool for general cloud data consolidation, though teams focused on marketing-specific workflows may find more value in tools purpose-built for that use case.

8. Fivetran

What it is:

An enterprise-grade data integration platform that automates data pipelines from a wide range of sources into cloud data warehouses.

Why it works:

Fivetran is known for its reliability, scale and robust connector ecosystem. It automates schema management, handles large data volumes and is well-suited for organizations building centralized analytics infrastructure across departments.

Key features:

• Extensive catalog of connectors across finance, ops, CRM and some marketing tools.
• Automated schema updates and high-volume syncs.
• Enterprise-grade security and compliance features.
• Designed to support complex data environments at scale.
• Optimized for use with cloud data warehouses like Snowflake, BigQuery and Redshift.

What to consider:

Fivetran is a powerful general-purpose tool, but it isn’t purpose-built for marketing. It may require additional tools or engineering resources to support marketing-specific logic, campaign data normalization or blended channel reporting.

Takeaway:

Fivetran is a strong option for enterprise data teams managing company-wide analytics, but marketing teams may need more specialized tooling to get fast, unified insights without custom development.

9. Panoply

What it is:

A cloud-based data platform that combines data warehousing with built-in ETL and prebuilt connectors, including some marketing sources, in a single package.

Why it works:

Panoply simplifies the modern data stack by bundling storage, integration and querying capabilities. It allows teams to ingest, store and analyze data without managing multiple tools or writing complex code.

Key features:

• All-in-one platform with warehousing and ETL combined.
• Prebuilt connectors for business and some marketing platforms.
• Built-in SQL editor and visualization support.
• Designed for quick deployment and minimal maintenance.
• Ideal for small teams or early-stage data operations.

What to consider:

While Panoply covers the basics well, it lacks advanced marketing-specific logic, historical data enrichment or deep customization features. It’s more suitable for simple reporting workflows than complex marketing analytics.

Takeaway:

Panoply is a convenient entry point for teams that want to simplify data infrastructure, though marketing teams with advanced needs may outgrow its capabilities over time.

10. Airbyte

What it is:

An open-source data integration platform that centralizes data from a wide range of sources, including many marketing, advertising and analytics tools, into cloud warehouses or other destinations.

Why it works:

Airbyte is developer-friendly and highly flexible, with a fast-growing connector library and options for self-hosting or cloud deployment. It appeals to engineering teams that want full control over data pipelines and infrastructure.

Key features:

• Open-source with a growing catalog of connectors.
• Supports marketing, analytics, CRM and custom sources.
• Can be self-hosted or deployed in the cloud.
• Developers can build or customize connectors as needed.
• Ideal for teams with strong in-house technical expertise.

What to consider:

Airbyte offers flexibility but requires technical setup and maintenance. It is best suited to teams with engineering resources and may not be practical for marketers who need a no-code or fully managed solution.

Takeaway:

Airbyte is a powerful choice for teams that need customization, control and hands-on data quality management, but it demands more technical effort than tools built for marketing teams.

Quick data integration solutions comparison

Compare tools side-by-side in this comparison chart:

This comparison highlights key differences, but features alone don't tell the full story. 

How to choose the right data integration tool

So what are the criteria that separate a good fit from a costly mismatch? Use the following criteria to choose the right fit for your needs.

1. Security and compliance

Your solution should support GDPR, CCPA and other data privacy frameworks, with secure data transfer baked in, not bolted on.

Key questions to ask:

• Does it meet GDPR and CCPA compliance standards?
• Is data encrypted in transit and at rest?
• Can you control access and audit usage across teams?

2. Scalability

Marketing data volumes grow fast. Your platform should scale without breaking pipelines or your budget.

Key questions to ask:

• Can it handle large and growing datasets without lag?
• Are usage-based costs predictable as data volumes rise?

3. Ease of use vs. customizability

The right solution supports both technical and non-technical users without forcing trade-offs.

Key questions to ask:

• Can business users create reports without engineering support?
• Is it flexible enough for custom logic or transformations if needed?

4. Fit for marketing use cases

Generic tools often miss the nuances of marketing data, like blending spend and performance data across multiple platforms.

Key questions to ask: 

• Does it connect natively to ad, analytics and CRM platforms?
• Can it blend data across channels without complex workarounds?
• Does it support campaign-level granularity?

Even the best data integration platform won't deliver value if it’s solving the wrong problem. Before you decide, it’s critical to understand how these tools differ from data warehouses and how both solutions work together.

Data integration platforms vs. data warehouses

Data integration platforms and data warehouses are both essential to a modern data stack, but they solve different problems.

Integration platforms collect, clean and move data from sources like ad platforms, CRMs or web analytics tools into a central destination. They handle extraction, standardization and formatting. The best tools automate this with prebuilt connectors, scheduling and transformation features that require little engineering effort.

Data warehouses are where that data lands. Tools like BigQuery, Snowflake or Redshift store large volumes of structured data and make it easy to query. They don't pull data from sources or clean it. They simply store it and make it fast to query.

In short:

• Data integration platforms get your data in shape.
• Data warehouses store it and let you analyze it.

You need both. One prepares the data, the other puts it to work. For marketing teams dealing with fragmented and fast-changing data, a strong integration layer is just as important as the warehouse itself.

Where does API management fit into the stack?

APIs are how modern platforms share data, but managing them is rarely simple. Each tool, like Google Ads, Meta, HubSpot or LinkedIn, has its own limits, schemas and quirks. Connecting to one might be easy. Managing 10 or more is a full-time job.

Data integration platforms solve this by handling API complexity. They offer prebuilt connectors that manage authentication, schema changes, rate limits and version updates automatically. Instead of building and maintaining scripts, teams can set up integrations in minutes.

This is especially important for marketing data. APIs change often and data structures vary across platforms. Integration tools adapt in the background, keeping data flowing without manual fixes.

For junior data engineers, it means fewer fire drills. For marketers, it means quick access to clean, current data without waiting on developers.

The benefit is speed and stability. Teams move faster, avoid downtime and focus on insights instead of infrastructure.

API management is no longer a separate task. In modern stacks, it’s built into the integration platform, removing one of the biggest friction points in working with marketing data.

Data integration with marketing intelligence built in

Moving data is easy. Making it useful is the hard part.

Most ETL and ELT tools weren’t built for marketers. They’re powerful but slow to set up, hard to manage and often miss the nuances of campaign data. That’s where automated platforms like Funnel stand apart.

They don’t just pull data. They make it work, fast.

Why it works:

• Connects to any marketing source, from ad platforms to CRMs.
• Cleans and unifies data automatically, no code required.
• Syncs in near real time, so your reports are always up to date.
• Scales effortlessly across dozens or hundreds of channels.
• Easy for non-technical users to manage and adjust.
• No infrastructure to maintain, no surprise costs.
• Purpose-built for marketing teams and analysts.

Instead of filing tickets or rewriting brittle scripts, you get trusted, ready-to-use data in hours, not weeks.

The result? Faster insights, fewer delays and more time spent acting on data instead of fixing it.

You trade some custom engineering for speed, clarity and control. For most marketing teams, that’s not a compromise. It’s a win.