Data pipeline vs. ETL pipeline: What’s the difference?

It’s a great big digital world out there, and it’s chock-full of customer data.

Of course, if you’re a business owner, you probably know that this can be both a blessing and a curse. The task of data management is enough of a challenge by itself, but it’s exponentially more difficult if your customer data becomes siloed across multiple systems.

Fortunately, pairing your customer data platform (CDP) or customer relationship management system (CRM) with a reverse ETL solution like Cloud Connect can streamline your data management strategy.

Data pipelines and ETL pipelines both play important roles in your approach to customer data management. Let’s consider each of these pipelines individually, and then explore how they differ.

What is a data pipeline?

Put simply, a data pipeline is a set of operations designed to automatically move data from one or more sources to a target destination. Transformation of data may occur along the way, but that’s not a necessary characteristic of a data pipeline.

Web analytics, social listening tools, CSV sheets, PPC campaigns, event trackers, and deterministic data are all examples of data sources. Essentially, any point where your customers interact with you is an opportunity for sourcing data.

Of course, collecting data is one thing. It serves no purpose just sitting there. You need to store it, organize it, analyze it for ways to optimize your marketing campaigns, and use it to personalize customer experiences. You need to get it from the point of collection to the point of taking action—and that’s what a data pipeline does for you.

What is ETL in data?

Now, regarding ETL pipelines.

ETL in data stands for extract, transform, and load. An ETL data pipeline:

1) extracts data from various sources;

2) transforms it in some way, and then;

3) loads it into a destination such as a cloud database or a data warehouse.

For a deeper explanation on ETL, check out this blog post that breaks down the term. Learn more about the differences between ETL and ELT in this blog post.

Data pipeline vs. ETL pipeline: How are they different?

All data pipelines play important parts in your overall data management strategy, but ETL pipelines are particularly powerful because of what they’re built to accomplish.

You can think of ETL pipelines as a subcategory of data pipelines. That’s really all they are: a specific type of pipeline.

With that in mind, here are the three main differences between data pipelines and ETL data pipelines.

1. ETL pipelines transform data

Whereas a regular data pipeline simply moves data from point A to point B, an ETL data pipeline additionally performs some type of transformation upon that data during its journey. This could entail aggregating, cleaning, validating, resolving identities, or manipulating the data in whatever way is necessary to align with your business intelligence needs.

2. ETL pipelines run in batches

ETL pipelines are intended to handle large volumes of data. As such, they typically run in batches on a set schedule—for example, once or twice daily when network traffic slows down. Regular data pipelines are usually always up and running, processing data in real time.

3. ETL pipelines terminate after loading

Finally, ETL pipelines stop once they’ve finished loading, and don’t restart until the next scheduled batch. Other data pipelines don’t necessarily stop after load; in fact, they might trigger additional processes afterward.

ETL pipelines are important for business intelligence and analytics because they’re able to pull data from disparate sources and deliver it all to a single point of access. This centralization of customer data streamlines your analytics, reporting, and decision making, and allows for vastly improved CRM. It also frees up your dev team to focus on high-priority tasks instead of having to solve for a fragmented customer data situation.

Put the right pipelines in place for your customer data

Customer data can be both a blessing and a curse. On the one hand, it’s fairly easy to collect, and there’s a whole lot of it out there waiting to be collected.

On the other hand, without the proper processes in place, it doesn’t take long for your customer data to become siloed, disorganized, and underutilized.

That’s why it’s essential to have a reverse ETL solution in place to facilitate data streaming from your data warehouse to your downstream tools. For those who don’t know what a reverse ETL solution is, we explain it in detail in a recent blog post. But essentially it’s the opposite of ETL. It brings data from your cloud warehouse into your existing tech stack.

We know just how important a reverse ETL solution is to your data management. That’s why we invented Cloud Connect.

Cloud Connect is a powerful reverse ETL solution that gives you easy access to all of your data, enabling you to provide your customers with a more personalized experience and create unique segments in your marketing tech stack. At Lytics, we’ve helped many companies turn their fragmented data structures into streamlined, centralized data powerhouses that deliver—and we can help yours, too.

Ready to get started? Take a deeper look into Cloud Connect™ by watching our explainer video below or read more in our introductory blog. Or see it for yourself. Try Cloud Connect free and test your first segments today.