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Marketing data is often fragmented, inconsistent and constantly changing. Your marketing team pulls data from multiple platforms, each with different formats, update frequencies and quota restrictions. Keeping up with these demands might be your job, but does it have to be so frustrating?
Every time an API changes, your team has to scramble to update scripts. Every failed data transfer means lost insights and a desperate email to your data team. Every manual fix adds risk and delays decision-making. Meanwhile, campaigns are moving fast, marketing is hounding you for insights and, because of poor data quality, opportunities are slipping away.
It doesn’t have to be like this.
The hidden costs of manual marketing data pipelines
You and your team have been bandaging your pipelines for years. You know how to plug issues as needed and can mostly keep things under control with a few patches here and there. You know how to put a spreadsheet together when the marketing team needs it, but all this additional time plugging reporting processes that should just work is costing you.
And these aren’t the only hidden costs. Consider:
Continuous development
Manual marketing data pipelines often require constant attention. What may have started as a simple solution has evolved into a web of customized code, complex integrations and ongoing upkeep.
Every new marketing campaign or product launch introduces new data requirements, often leading to another round of patching and troubleshooting. This cycle can become a massive drain on your engineering resources, leaving little room for strategic projects or innovation.
Maintenance
Managing marketing data pipelines requires a lot of maintenance work, pulling your team away from strategic projects. APIs frequently change, sometimes without warning, and platforms like Facebook Ads impose strict rate limits, adding complexity.
Manual monitoring is inefficient, as failed data transfers require immediate troubleshooting, diverting resources. Error resolution and normalizing fields are time-consuming tasks that increase operational risk, leading to inaccurate data and delayed insights. In addition to the actual labor costs, these ongoing maintenance burdens drain your team's time and energy, leading to even more costly issues like employee dissatisfaction.
Engineering overhead
When your data processes add more complexity than they take away, your resource allocation is skewed.
Instead of working on strategic initiatives, your engineers are spending an average of 44% of their time maintaining manual data pipelines.
While storage and compute costs remain constant whether you build or buy, the real financial burden lies in the hidden costs of maintaining manual pipelines — ongoing API updates, troubleshooting and debugging. Not to mention the costs of making business decisions based on inaccurate insights muddled together under pressure.
Automated ETL (extract transform load) removes this drain on engineering resources, allowing teams to focus on growth-driving projects while giving your analysts and marketing team the timely insights they actually need.
What is ETL as a Service?
ETL as a Service is managed ETL automation. It takes over the tedious parts of data management — extracting, transforming and loading data from multiple data sources. It also takes the burden of pipeline maintenance off of your engineers.
Instead of worrying about API changes or manual troubleshooting, your team can focus on driving innovation.
Using managed ETL reduces operational risk by improving data accuracy and consistency, all while cutting down on the time spent maintaining pipelines. It means faster time to reliable insights without the constant firefighting.
Here’s how:
Streamlined pre-aggregation and redundant data processing
Data pre-aggregation is all about summarizing your data into key figures before you even start querying it. Instead of having to calculate things like totals or averages every time you run a query, the heavy lifting is done upfront during data processing. This makes your queries run faster and reduces the strain on your system.
For example, let’s say you’re aggregating data for a retail company, and you have daily sales data. Instead of recalculating daily sales every time you need to pull a report, you could pre-aggregate that data into monthly or yearly totals as you load it into your system. So, when you need to pull up sales for the past year, the system can simply pull the pre-calculated total instead of adding up all the daily numbers again.
It’s a great way to save time, reduce computation and keep things running smoothly, especially as your data grows.
The good news is that this no longer has to be a painstaking process. Tools like Funnel automatically optimize the data, ensuring only the most relevant and accurate data is passed through for reporting and analysis, saving both time and computational power.
Dealing with schema drift and duplication
A major headache for data teams relying on manual pipelines is schema drift — the sudden changes in data structure that can cause breaks in your pipelines. Managed ETL services detect schema changes, like new columns or data type adjustments, and update the pipeline logic on their own without your intervention.
This means you don’t have to manually rework everything each time your data evolves. Version control also means you can retain the raw data and revert to previous data versions if you need to roll back.
Duplicate data is another frequent issue in marketing data. With multiple platforms and systems involved, it's easy to end up with duplicated or overlapping data entries. Managed ETL services resolve this by deduplicating data during their automated ETL processes. This ensures your reports and dashboards are built on clean, accurate data without the need for manual intervention.
Out-of-the-box optimization and faster insights
One of the best parts about ETL as a service is that engineers no longer need to manually tweak partitions or perform complex optimizations to improve query performance. By eliminating these processes, managed ETL ensures that data is ready to be analyzed quickly, giving marketing teams faster, more reliable insights.
Empowering marketing teams with self-service access
With managed ETL, marketing teams no longer have to rely on engineers to manually pull data whenever they need it. Self-service access to clean, pre-aggregated data means they can pull the information they need to analyze performance, test new campaigns or adjust strategies, all without waiting on development resources.
The best part? No-code interfaces enable your marketing teams to analyze and manipulate the data like data engineers without needing to have technical expertise. This significantly improves agility, allowing your teams to make data-driven decisions faster and independently.
Why fully managed ETL beats DIY pipelines
Fully managed ETL removes the need for in-house pipeline maintenance, API management and troubleshooting so your team can focus on using data, not fixing it.
Built-in adaptability to platform changes
APIs, which are like bridges connecting different software systems, often change. For example, if an API updates how it sends data or changes its structure, your existing system might break because it doesn’t know how to handle the new format. In traditional ETL systems, you’d need to manually update your pipeline to handle these changes. But with ETL as a Service, this is all done automatically behind the scenes. The service monitors API changes and adjusts your data flow to ensure everything keeps working smoothly.
For example, if a payment processor updates its API, the service detects that change and adapts without you needing to lift a finger. This means your data pipelines keep running without downtime or delays, and you won’t have to worry about fixing broken integrations.
Faster implementation without the technical debt
Building custom ETL systems in-house can take months, as you have to create connectors for each data source and build transformations to clean and format the data. For example, if you want to pull data from Google Analytics and Salesforce, you’d have to build custom connectors for both and write logic to combine and clean that data in a way that’s useful for analysis.
This takes time and creates long-term technical debt. You’ll need to continually update and maintain your custom code as your data sources change.
But with ETL as a Service, this heavy lifting is already done. Services like Funnel automation offer pre-built connectors for a variety of platforms, so you can integrate with Google Analytics, Salesforce and others in just a few clicks. This dramatically speeds up the process of getting your data into a usable format, and your team avoids the ongoing technical debt of managing custom-built solutions. Essentially, you get a fully functioning pipeline ready to go in hours rather than waiting months to build one from scratch.
Top ETL automation tools
With so many ETL automation tools out there, it’s crucial to know which one delivers the right features for your needs. Below, we’ve broken down the top options, highlighting what sets them apart.
ETL feature comparison
Which key features set these tools apart?
- Funnel: Specializes in marketing data integration with a vast array of connectors and a user-friendly interface, offering a managed service that handles data extraction, transformation and loading seamlessly.
- Fivetran: Provides a fully managed data pipeline with a significant number of connectors, though slightly fewer than Funnel. It automates data extraction and loading but offers limited transformation capabilities, often requiring additional tools for complex transformations.
- Supermetrics: Focuses on marketing data but is primarily designed for data extraction into platforms like Excel and Google Sheets. While it supports integration with data warehouses, its transformation capabilities are limited, and it is not a fully managed service.
- AWS Glue: A robust ETL service within the AWS ecosystem, offering extensive transformation capabilities. However, it requires technical expertise to set up and manage, as it is not a fully managed service and primarily supports AWS-native data sources.
Which ETL automation tool is right for your needs?
- For non-technical users or teams seeking ease of use: Funnel stands out due to its fully managed service option, extensive marketing data connectors and user-friendly interface, minimizing the need for technical intervention.
- For teams with technical expertise seeking customization: AWS Glue offers extensive transformation capabilities but requires coding and management, making it suitable for teams comfortable with AWS services and custom ETL development.
- For businesses focused on cost and data volume: Fivetran operates on a volume-based pricing model, which may be cost-effective depending on your data needs, but consider the potential need for additional tools for complex transformations.
- For simple data extraction needs: Supermetrics is suitable for straightforward data extraction into tools like Excel or Google Sheets but may fall short for comprehensive ETL requirements.
When selecting an ETL tool, consider factors such as the specific data sources you need to integrate, the complexity of required transformations, your team’s technical expertise and budget constraints.
Does ETL automation eliminate the need for data engineers?
ETL automation doesn’t eliminate the need for data engineers, but it lets them focus on more exciting and impactful work. Instead of spending hours on routine API and pipeline fixes or manually transforming raw data, engineers can now hand off those responsibilities to the managed ETL service provider.
An automated service like Funnel manages the entire data flow — pulling data from sources, transforming it into the right format and loading it into centralized dashboards for analysis. This includes handling all of the monitoring, error detection and adjustments needed to ensure that data flows smoothly and without disruption, so engineers don’t have to worry about manual fixes or downtime.
With this automation in place, engineers can shift their focus to higher-level tasks that drive business value, like designing more efficient data models that improve analytics, developing advanced machine learning algorithms to predict customer behavior or setting up complex business rules that help guide decisions across teams. By automating the mundane, managed ETL services give engineers the time and flexibility to work on projects that really move the needle for your business.
Can ETL automation scale with my marketing team's growing data needs?
As your marketing team's data needs grow, ETL automation can scale effortlessly to handle the increasing volume and complexity of data. For example, as you add more data sources, like new marketing platforms, customer behavior data from your website or social media analytics, a managed ETL service like Funnel will integrate these new data sources without requiring additional manual setup or rework from your team.
The service will detect and adapt to changes in data structure, ensuring that your marketing data pipeline keeps running smoothly and that your dashboards continue to reflect accurate insights.
From a technical perspective, ETL automation platforms can handle a variety of tasks swiftly, such as scaling up processing power when the data load increases or adjusting the data transformation logic based on new business rules or marketing metrics.
Let’s say your team starts running more advanced A/B testing or collecting granular user data from multiple campaigns. Managed ETL as a Service will scale its performance to make sure that complex data transformations, such as aggregating click-through rates across channels or calculating customer lifetime value holistically, are done quickly and accurately, even as data volume grows.
Additionally, automation handles the monitoring and error detection, so when there’s an issue, such as a data feed going offline or a malformed record, the provider can correct it without your team having to intervene. This means your marketing data pipeline is more reliable, more efficient and able to scale without hitting the performance bottlenecks that your developers would usually have to sort out.
Best practices for using ETL automation
Managed ETL means less manual intervention for your development team, but it isn’t completely hands-off. For robust data governance, here are some best practices to implement:
1. Modularize your ETL pipelines
Instead of creating one monolithic ETL process, break your pipelines into smaller, more manageable modules. Each module should focus on a specific task, such as extracting data from a source, applying a transformation or loading it into a centralized marketing intelligence solution.
This modular approach makes debugging easier. If something breaks, you can narrow down the issue to a specific module.
It also enables reusability, so you can repurpose modules when adding new data sources or modifying transformations. For example, if you need to add a new marketing platform's data, you can reuse the extraction and transformation modules, only adjusting the specifics for the new platform.
2. Optimize for performance
As your data grows, your ETL pipelines need to handle larger datasets more efficiently. Regularly optimizing the performance of your ETL processes can reduce processing time and system load. Techniques like parallel processing, partitioning large datasets and indexing data can help speed up transformation and loading tasks.
For instance, if you're running analysis on time-series data like customer activity over months or years, partitioning the data by time periods (e.g., by month or year) can drastically reduce the time spent loading and querying the data.
3. Document and version control your ETL workflows
Managed ETL forms a large part of your wider data governance, so with a growing ETL pipeline, proper documentation becomes essential for collaboration, troubleshooting and scalability.
Clearly document your data flows, transformations and integration logic so that both technical and non-technical team members can understand the process. Version control is also crucial for managing changes to ETL workflows. It allows you to track updates, roll back to previous versions if needed and ensure consistency across your team.
For example, if your marketing team requests changes to how certain customer segments are defined in your pipeline, version control lets you manage updates efficiently without disrupting ongoing processes.
4. Implement robust data quality checks
Data quality is crucial for reliable analysis, so it’s essential to automate checks at each stage of the ETL process. Before data is transformed, you should verify that it meets predefined criteria, such as valid formats, no missing values and correct data types.
After transformation, additional checks can ensure that the data’s integrity remains intact, preventing issues like data corruption or loss during the loading phase. For example, if you're working with marketing campaign data, ensuring that fields like "campaign ID" or "ad spend" follow consistent formats can help you avoid errors that could skew analysis.
Empower marketing with insights they can use — without draining engineer resources
To stay competitive, your team needs more than just functional data pipelines — you need a solution that automates, scales and evolves with your business. Managed ETL streamlines your data processes and ensures faster, more reliable insights.
By automating tasks like pre-aggregation, schema drift handling and deduplication, you empower your team to focus on high-value projects. With self-service access and no-code interfaces, marketing teams can make data-driven decisions independently.
Using managed ETL removes operational bottlenecks, reduces risk and accelerates growth, positioning your business for long-term success. That means data in and direct insights out: just the way it should be.