What is ETL? Everything marketers need to know

Before you can build a report, spot trends or answer questions about performance, you need to prepare your data. It’s often scattered across platforms, uses different naming conventions and isn't ready for analysis.

ETL (Extract, Transform, Load) is a data integration process that consolidates your data. It extracts data from source systems, transforms it into a consistent format and loads it into a destination such as a data warehouse or lake.

ETL has become a core part of modern marketing reporting, enabling teams to spend less time preparing data and more time making data-driven decisions.

But before you invest in an ETL solution, it’s important to understand how the process works. In this guide, we'll break down everything marketers need to know about ETL, including how it works, its pros and cons and how it compares to other data integration approaches.

What is ETL?

ETL is a process for combining data from multiple systems into a single database. Reliable ETL tools can extract data from multiple sources, transform data into a unified format and load the transformed data into a target system, such as a cloud data warehouse or data lake.

Data scientists and other data professionals might use complex approaches to data extraction, data transformation and data loading. Many of them might even learn how to build manual data pipelines.

Marketers and other business users rarely have the time or desire to learn about building data pipelines. You just want a simple way to prepare data, so you can use data analytics apps to spot trends and measure success.

ETL tools can help because you can take structured and unstructured data from practically anywhere, reformat it, and send it to your data processing app.

The ETL process explained

⬇️ Extract:

Extract means gathering data from its source, like a database or an application.

🔀 Transform:

Transform means cleaning, de-duplication and standardization.

⤵️ Load:

Loading is sending the transformed data to a data warehouse or similar place where it can be used in BI tools, for example.

What are the three stages of ETL?

Let’s take a closer look at how each stage of the ETL process performs data integration.

1. Extract raw data

An ETL platform can extract data from multiple sources simultaneously. For example, you might use ETL tools to collect:

• Survey results
• Email response rates and other email data
• Performance marketing data
• Organic website traffic
• Sales on e-commerce platforms

An ETL platform acts like a series of pipes that connect these and other data sources to a single destination.

2. Transform data to clean it and make it ready for analysis

Before data can reach its destination, an ETL must put all information in the same format and check for data quality.

For some professionals, data transformation might be a hard-to-grasp concept. When you drill into the details, it can get very confusing.

The good news is that you don’t need to know how the data integration process works. As long as your ETL tool does the job well, you can combine data from multiple sources. The data quality check means any incomplete or corrupted data values will get removed so they don’t skew your results.

3. Load the data into a data warehouse or another data destination

Once your extracted data has been reformatted and cleansed, the ETL software can load it to a data warehouse or other destination.

Where you put your data depends on how you want to use it. If you just want to store information so you can review it later, you can use pretty much any data store your business has.

Technically, you will need to differentiate between structured and unstructured data.

Structured data has a standardized format and doesn’t usually include any text. It includes information like your bounce rate and conversion rate. You can put structured data into a database.

Unstructured data can include live chat messages, survey results and social media exchanges. It’s often text-based and hard to quantify. This type of information can go into a data lake.

A quick guide: data lakes and data warehouses

What’s the difference between data lakes and data warehouses, and how does that affect your ETL tools and processes?

Data warehouses

A data warehouse stores and manages structured data. It’s a good target system for an ETL process involving organized data sets. It can:

• Index and optimize query performance for efficient data extraction and transformation.
• Centralize data, making it consistent and minimizing errors.
• Make strategic decisions easier with historical patterns and trends.
• Scale with you as your data volume grows, which makes it perfect for enterprise environments.
• Integrate with business intelligence (BI) tools and reporting platforms for seamless insights.
• Comply with regulatory standards in data governance and security.

Data lakes

A data lake stores structured, semi-structured and unstructured raw data, which means you have more flexibility to accommodate diverse types of data. If you’re storing large volumes of data, it’s more cost-effective than a data warehouse. It can:

• Be more agile than a data warehouse.
• Allow for experimentation in data analytics or transformations without the need for an upfront data schema or data modeling.
• Integrate seamlessly with big data tech, letting businesses perform complex data processes at greater scale than a data warehouse.
• Give access to raw data that can be explored and analyzed by data scientists.
• Support real-time data processing for faster insights and decision-making.

You can also load data to business intelligence and data analytics applications. For the most part, though, it makes sense to store the information in one or multiple databases. Otherwise, you might lose access to source data you need later.

Want to dive deeper? Try this: An introduction to marketing data warehouses, or watch our YouTube video below.

Why marketing data challenges the generic ETL flow

ETL can move and transform data. But marketing teams need more than data movement. They need data they can depend on as platforms, metrics and business requirements change.

Without a data integration platform designed to solve marketing data’s specific challenges, you’re always going to have to worry about historical data loss, broken reports, errors, unreliable AI workflows and other issues, along with the engineering time spent fixing broken pipelines and manually querying data.

Fragmented data sources

Marketing data lives in advertising platforms, analytics tools, CRMs, ecommerce platforms and other systems. Bringing all that information together and transforming it so it’s contextualized and normalized is one of the biggest challenges marketers face.

An ETL doesn’t give you a single source of truth for disconnected marketing data. A marketing intelligence platform like Funnel does. With a generic ETL, data can be piped into a warehouse, but someone still has to write the logic to keep it organized and to query it for analysis and reporting before moving it to other destinations. Funnel ingests data from hundreds of sources, normalizes it and applies business rules so it’s ready for measurement, reporting and other marketing activities.

API instability

Marketing platforms regularly update their APIs, which can affect how data is collected and reported. ETL tools don’t manage those connections, so someone on your team has to handle API changes. Funnel offers managed integrations, so data engineers aren’t spending time fixing a broken pipeline with every update, and your team doesn’t have to worry about losing data.

Changing metrics and naming conventions

Fields, metrics and naming conventions change over time. A marketing intelligence platform like Funnel lets you apply your own business logic, so data is standardized, and reports remain consistent even when source platforms evolve.

Data retention limits

Many marketing platforms only store detailed historical data for a limited period. ETL can help preserve that data before it becomes unavailable by moving it to a warehouse and applying logic. However, you can lose your raw data in the process. Funnel stores all your historical data, including in its raw form.

How a marketing ETL solution helps solve the challenges generic ETL isn’t designed for

A marketing-specific platform solves a different set of problems than a generic ETL. Funnel, for example, is a marketing intelligence platform built around the complexity of marketing data. It manages the collection, storage and preparation of marketing data so teams can use dependable data across reporting, analysis, measurement and their existing data stack.

Marketing ETL use case: business intelligence

Some large companies have dedicated BI teams that rely on ETL tools to reformat and cleanse data before analyzing it.

Power Digital shows how business intelligence teams can use marketing-focused ETL to save time and improve insights. The marketing company collects data from diverse sources, including Shopify, Google Ads, Google Analytics and Facebook Ads. Some of its data destinations include Google Data Studio, Google Cloud Storage, Google Sheets and Amazon S3.

When Power Digital adopted Funnel as a tool capable of backing up historical data, the company’s BI team:

• Reduced its data collection process time to about one hour.
• Saved each team member three to four hours of work per month.
• Benefited from custom connectors that save hundreds of hours per month and avoid high engineering costs.

With Funnel, marketing has access to reliable data, avoids unnecessary data refreshes and enjoys considerably higher efficiency that leads to deeper insights without long wait times.

Most creative marketers aren’t marketing data engineers. With a no-code marketing intelligence platform that doesn't require a lot of technical knowledge, data is more accessible. There’s a trustworthy data foundation that’s ready-to-use by marketing and data analytics teams, which means no more dependence on engineers to analyze performance, measure outcomes, make budgeting decisions and optimize campaigns.

ETL vs. ELT: What’s the difference?

ETL isn't the only data integration approach you'll come across. As cloud data warehouses have become more powerful, many organizations have shifted toward ELT (Extract, Load, Transform).

Like ETL, ELT is designed to bring data from multiple systems into a central location for reporting and analysis. The difference is when the transformation happens.

With ETL, data is cleaned, standardized and transformed before it’s loaded into the destination.

With ELT, raw data is loaded first and transformed later inside the warehouse.

ELT has become popular as cloud data warehouses such as Snowflake and BigQuery have made it easier to store and process large volumes of data. Rather than deciding upfront exactly how data should be transformed, teams can load raw data first and apply transformations later.

This approach is often more scalable because it uses the processing power of the data warehouse itself. As data volumes grow, organizations can scale warehouse resources without redesigning their entire data pipeline.

What is reverse ETL?

Traditional ETL and ELT focus on moving data into a central location for reporting and analysis. Reverse ETL does the opposite. It takes cleaned, unified data from a data warehouse and sends it back into the tools teams use every day.

For example, a marketing team might use reverse ETL to push customer segments from a data warehouse into a CRM, advertising platform or marketing automation tool. This allows teams to act on insights instead of keeping them locked inside reports.

Reverse ETL has become increasingly important as more organizations centralize their data in cloud warehouses. While ETL and ELT help create a trusted source of data, reverse ETL helps ensure that data can be used across sales, marketing, customer success and other business functions.

Together, ETL, ELT and reverse ETL form a complete data lifecycle: collecting data, preparing it for analysis and then activating it across the business.

How do you build an ETL strategy?

So how do businesses set up an ETL process? First, it’s important to understand your data and goals. Before diving into the technical aspects, it’s essential to have a clear understanding of your data and business objectives.

• Identify data sources: Determine where your data resides (databases, files, APIs, etc.).
• Define data requirements: Understand the specific data elements needed for analysis and reporting.
• Set clear goals: Establish what you want to achieve with the ETL process (data warehousing, reporting, machine learning, etc.).

7 steps to build an ETL strategy

1. Data profiling and assessment

The first stage is all about understanding the data, the amount to process, and ensuring that any issues are cleaned. That means:

• Analyzing data quality, consistency, and completeness.
• Identifying potential data issues and cleaning requirements.
• Understanding data volumes and velocity to determine ETL tools and infrastructure.

2. Data modeling

This stage involves designing the structure of your data to ensure it effectively supports your business needs. It includes:

• Designing the target data structure (data warehouse, data mart, or other).
• Creating data models that align with business requirements.
• Defining relationships between data elements.

3. ETL process design

Here, you outline the specific steps involved in moving and transforming your data.

• Outline the ETL pipeline, including extraction, transformation, and loading steps.
• Determine data transformation logic (cleaning, formatting, calculations, etc.).
• Define error handling and recovery procedures.

4. Tool selection

Choosing the right tools is crucial for efficient ETL.

• Evaluating ETL tools based on data volume, complexity, and budget.
• Considering open-source options or commercial tools.

5. Data quality and validation

Ensuring data accuracy is paramount. This step focuses on:

• Implementing data validation checks at each stage of the ETL process.
• Defining data quality metrics and monitoring processes.
• Establishing data governance policies to ensure data accuracy and consistency.

6. Testing and deployment

Before going live, thorough testing is essential. Businesses will need to:

• Develop comprehensive test cases to verify data accuracy and transformation logic.
• Conduct performance testing to identify bottlenecks and optimize the ETL process.
• Deploy the ETL pipeline into a production environment.

7. Monitoring and maintenance

The ETL process is an ongoing journey, so it’s important to observe and revisit your strategy on an ongoing basis.

• Continuously monitor ETL job performance and data quality.
• Implement alerts for data anomalies or errors.
• Schedule regular maintenance and updates to the ETL process.

Which should marketers look for in an ETL?

Most ETL tools are built for stable, structured business data. Marketing data isn’t that. Ad platforms rename fields, restate numbers days or weeks after a campaign ends and report in different currencies and time zones. Keep these questions in mind when evaluating ETL tools:

• Is it built to handle changing marketing APIs?
• Can it preserve historical marketing data?
• Can marketers standardize metrics and dimensions without relying on engineers?
• Can definitions change without rebuilding historical data?
• Can the same marketing data be used across BI, warehouses, spreadsheets, measurement and other workflows?
• Who maintains the integrations when platforms change?

Funnel is a marketing intelligence platform with a built-in data hub, managed connectors, no-code transformations and pricing based on the number of data resources, not data volume. It is built to answer all of these issues:

• Funnel handles API changes with fully managed connectors.
• Marketing data is preserved with no retention limits.
• Non-technical teams can standardize metrics and dimensions without relying on engineers.
• You don’t need to rebuild historical data if definitions change.
• The same marketing data can be used across all your tools and workflows.
• Funnel connects with over 600 data sources, and as part of our Data Guarantee, we’ll create a custom connection if one doesn’t exist.

ETL moves marketing data, but a marketing-specific ETL solves marketing data challenges

ETL solves an important part of the marketing data problem: getting data from different systems into a usable format.

But as your marketing operation becomes more complex, the challenge isn't simply moving data. It's keeping that data dependable as platforms, APIs, metrics and reporting requirements change.

That's the problem Funnel is built around. Funnel is a marketing intelligence platform that gives marketing and data teams a dependable foundation for their marketing data, so the same trusted data can support reporting, analysis, warehouses, measurement and other workflows.

FAQs about ETL

What is the difference between ETL and ELT?

ETL transforms data before it’s loaded into a destination, while ELT loads the data first and transforms it later. Both help prepare data for reporting and analysis.

What is ETL used for in marketing?

ETL helps marketers bring data from multiple platforms into one place. This makes it easier to build reports, track performance and spot trends. However, marketing teams need a marketing-specific ETL solution to avoid issues like data loss and broken reports.

Do I need ETL for marketing data?

If you're working with data from multiple platforms, ETL can save time and reduce manual reporting. It becomes more valuable as your reporting needs grow. However, generic ETL doesn’t maintain consistent definitions, preserve historical data or manage integrations. Funnel does.

What is the best ETL tool for marketing?

The best ETL tool for marketing depends on your goals, data sources and budget. If you need a general data integration platform, tools like Fivetran and Airbyte are popular options. For marketing teams, Funnel goes beyond traditional ETL by helping collect, standardize, store and activate marketing data for reporting, analytics and decision-making.

What is reverse ETL?

Reverse ETL takes data from a warehouse and sends it back into tools like CRMs, ad platforms and marketing automation software so teams can act on insights.