What Is An ETL Pipeline? A Complete Guide For Beginners

An ETL pipeline is a series of processes that helps a business collect data from different sources and move it to destination systems — usually a data storage system such as a data warehouse — while transforming it along the way.

These pipelines are essential for data scientists and analysts, as well as many other professionals who count on business intelligence and data-driven insight.

If you’re unfamiliar with ETL pipelines, this article is for you. We will be discussing all that you need to know about ETL pipelines, the processes involved, how they differ from data pipelines, and their advantages and disadvantages. 

By the end of this article, you will have a deeper understanding of what ETL pipelines are and why they’re important for businesses across industries. 

And as data technologies rapidly evolve, you’ll be positioned to choose the right type of pipeline that meets your data and business needs. 

Let’s start by getting a well-rounded idea of what an ETL pipeline is. 

What Are ETL Pipelines?

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ETL stands for extract, transform, and load. Together, this set of processes moves data from a source to a destination system.

Moving data around a company is extremely important. A modern enterprise typically has many disparate sources of data, as well as various goals that require data to be consumed in different ways and by different systems. 

For example, you might have security measures, marketing and sales goals, financial optimization needs, and organization-level decisions to make. All of these things require data — in many places and in many forms. 

How do you make sure data is in the right place in the right format?

That’s where data pipelines, including ETL pipelines, come in. 

They allow businesses to move data between systems in an automated fashion, with data transformations included along the way. 

Now, let’s take a look at the three steps of ETL in depth.

I. Extract

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The pipeline must first extract, or ingest, information and data from a source or multiple sources.

Data source systems often include:

• APIs
• Websites
• Data lakes
• SaaS applications
• Relational databases, or transactional databases

How does data extraction actually work? 

The technical details depend on the exact pipeline architecture. Because the pipeline is a separate type of data system from the source, it requires some sort of interface to communicate: an integration. 

A company might purchase an ETL tool from a vendor. In this case, integrations with different source systems are usually included with the tool. In other cases, data engineers employed at the company might build the ETL pipeline in-house, including the necessary integrations with sources. 

Pipelines may extract source data in real-time, meaning as soon as it appears in the source. They can also use a batch processing workflow, meaning that new changes are picked up at a set time interval. 

II. Transform

Transformation is the second step of the ETL process. In this step, raw data extracted from the source is transformed into data that’s compatible with the specific destination and use case.

For example, your business intelligence tool might require JSON data that conforms to a certain schema, but your source data is in CSV format. The transformation step of your ETL pipeline could change the data from one format to another as it passes through.

That’s just one example, and use cases vary widely. But one thing is almost always true: you must transform data at some point during its lifecycle. In an ETL pipeline, that transformation happens almost immediately after the data is extracted.

What types of data processing happen during the transformation step?

Most often, they include simple processes like:

• Filtering
• Aggregation
• Re-shaping data to conform to a schema 

These relatively simple transformations have a critical purpose. They ensure that when the dataset arrives in storage or in another application, it’s clean, consistent, and won’t cause errors. It’s ready to be used to meet business goals.

The fact that they are simple also makes them easy to automate. This saves data team members time from a task that would otherwise be quite tedious.

By contrast, more complex, exploratory data transformations and deep analysis can occur later in the data life cycle. This work is more complex and usually requires human attention. But automating the easy stuff up-front allows you to arrive here faster.

III. Load

The last step of the ETL process is loading the data into the target system, or destination. It’s the inverse of extraction.

In a typical, popular use case, the destination is a data warehouse or some other type of data store designed to power data analysis. Once in the warehouse, all sorts of analytic workflows can take place: data science, machine learning, exploratory analytics, and more. 

But use cases vary widely, and there are many other possible destinations. Generally speaking, destination systems can include:

• Data warehouses
• SaaS applications
• Operational business systems
• Visualizations and dashboards

Just as with the source, the pipeline needs to integrate with the destination system to load data. The technical details of the loading process depend on whether you use an ETL tool, what tool that is, or whether your ETL pipeline was built in-house. 

Once data is loaded, you can start to notice the latency of the data flow; that is, the amount of time it takes the ETL data to move from source to destination. 

Depending on the pipeline, latency can be in the hours, minutes, or, if real-time data streaming is used, down to the millisecond. We’ll discuss whether real-time ETL is possible toward the end of this article.

ETL Pipeline Vs Data Pipeline

ETL pipelines fall under the category of data integration: they are data infrastructure components that integrate disparate data systems.

More specifically, ETL pipelines are a subset of data pipelines. Simply put, a data pipeline is any system that takes data from its various sources and funnels it to its destination.

But it’s not really that simple. 

Every company’s data infrastructure and strategy are complex. Companies have different sources, destinations, and goals for their data. As a result, when you look closer, you’ll find that every data pipeline is unique. 

ETL pipelines are a sub-category that was named in the effort to start to break down or categorize this complex reality.

If you’re familiar with the world of data, you may have heard “data pipeline” and “ETL pipeline” used interchangeably, although they are different. This usage pattern has to do with the history of data pipelines. 

ETL was among the first data pipeline architectures to gain popularity in the enterprise. This is because it was fairly simple for a data engineer to build a basic on-premises ETL pipeline specifically for every one of their employer’s use cases.

In those days, you couldn’t purchase an ETL tool; you had to build your own. 

Over the years, data infrastructure has moved into the cloud, compute has grown more powerful, and service providers started to sell different types of tools. As a result, the possibilities for data pipelines have grown complex. 

Despite all this, it’s very possible to hone in on a few things that set ETL pipelines apart from other types of data pipelines.

A. Transformation Isn’t Necessarily Done In Data Pipelines

Data pipelines don’t, by definition, have a transformation step. They almost always should, for reasons we discussed previously: Most source data is messy and needs to be cleaned up.

Data that isn’t minimally aggregated or shaped to a schema will quickly become unmanageable and useless. 

But let’s imagine a perfect world for a moment. Let’s say you had a perfect set of JSON data that you wanted to move from a cloud storage provider to an operational system that happened to honor that exact format. In theory, you wouldn’t need to transform it. The “T” in “ETL” would be unnecessary. 

B. ETL Pipelines End With Loading 

In the same vein, ETL pipelines explicitly require that transformation occur between extraction and loading. Loading is the last step. This isn’t always the case for data pipelines.

For example, the transformation could occur after loading (an ELT pipeline), or the pipeline could be more complex, with even more steps. 

ETL vs ELT

Now, we’d be remiss if we didn’t compare ETL to ELT pipelines. 

ELT pipelines are another well-known category of data pipelines. When you know what ETL stands for, it’s pretty easy to figure out what ELT means. 

“E” still stands for extract. “L” still stands for load. And “T” still stands for transform. 

An ELT pipeline is simply a data pipeline that loads data into its destination before applying any transformations. 

In theory, the main advantage of ELT over ETL is time. With most ETL tools, the transformation step adds latency. 

On the flip side, ELT comes with risks. 

With ELT, you’re essentially sending data straight to the destination and putting off the transformations until later. By dumping disorganized, unclean data into your data store, you can risk bogging your systems down and even storing corrupt data that you’ll never be able to recover.

But as we’ve discussed previously on the blog, the line between ETL and ELT isn’t exactly as clear as it seems. There are hybrid architectures, systems that say they’re one thing and are really another, and other variations to take into account.

6 Benefits Of ETL Pipelines

Ultimately, ETL pipelines are vital to companies because they collect and prepare data for analytics and operational workflows. By doing so, they ensure businesses of all industries can effectively use their data to function in the modern world.

ETL pipelines share all the benefits of data pipelines in general, as well as having some special benefits of their own. Let’s break that down.

1. Collecting Data From Various Sources

As businesses scale, they inevitably start to generate all different types of data, which comes from an equally wide array of sources. 

Databases, APIs, websites, CRMs and other apps… the list goes on. 

ETL pipelines provide a reliable way of centralizing data from all these different sources.

2. Reduced Time For Data Analysis

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Data arrives at the target system transformed and ready to use. 

Sure, transforming the data along the way can add a bit of time to the pipeline. But once it’s loaded, it’s ready to go. 

You can transform your data however you need — so that it’s not just compatible with the target system, but also easy to use for human analysts and data scientists. If your destination is a data warehouse, as is often the case, this reduces the time to actionable insights.

3. Deeper Analytics And Business Intelligence

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By automating the more tedious transformations — aggregation, filtering, re-shaping — ETL pipelines don’t just save time. They also allow the humans that consume data to take their analysis even deeper. 

ETL eliminates manual work and human error from the early transforms, allowing analysts to innovate. 

4. Easy Operationalization

The ETL pipeline’s destination isn’t always a warehouse. In some cases, you’ll pipe data straight from the source to applications like business intelligence software or an automated marketing platform. 

This is known as data operationalization: putting data to work in the business’s day-to-day operations. Once you know what transformations are required to operationalize, you can create a direct path for data with an ETL pipeline.

5. Ample Support And Tooling

ETL isn’t the only kind of data pipeline, but it’s one of the easiest to set up. This is because it’s been around for a long time. 

Many data engineers have years of experience building ETL pipelines… but you might not even need to hire an engineer. Companies that sell ETL tools have proliferated in recent years. Most of these tools are extremely easy to use.

6. Data Quality Assurance

Transforming before loading ensures that only high-quality data arrives in the target systems. This prevents nasty surprises like corrupt data or incorrect insights. 

You can also add automated testing to your ETL pipeline for an extra level of insurance that your transformations are succeeding.

Disadvantages of ETL Pipelines

The main disadvantage of ETL pipelines is that — at least traditionally — they are not real-time data pipelines. 

Rather, ETL pipelines rely on batch processing. This type of processing is relatively easy to implement, but it introduces some amount of delay. 

As we progress through the 2020s, real-time data is quickly becoming the norm. Especially for operational data workflows, we expect the systems we use to reflect new updates immediately. 

A few examples of when a delay in the data pipeline is not acceptable include:

• When you’re detecting financial fraud.
• When you’re managing inventory in a fast-paced warehouse.
• When you need to capture a customer for an online sale before they leave your site.

What About Real-time ETL?

Real-time data pipelines are possible, and some of them include transformations. 

These pipelines rely on a data streaming infrastructure and apply transformations on the fly. There are no data batches, and as a result, the time from source to destination is a matter of milliseconds

You won’t hear these kinds of pipelines called “ETL,” because, on a technical level, they are very different processes. But they ultimately achieve the same goal: getting data from source to destination, while transforming it along the way. 

Conclusion

ETL pipelines started as a special piece of technology built by engineers. Today, easy ETL tools put them in the reach of many more stakeholders. And that group of stakeholders has grown huge. 

Business leaders, IT professionals, salespeople, and many, many more types of workers are impacted by ETL pipelines and other types of data pipelines. 

In this article, you learned what  ETL pipelines are and how they differ from data pipelines in general. We’ve examined each component of the process, and the advantages they bring. 

Most importantly, we discussed the importance of transformation in data integration. We live in the age of “big data.” Organizations need to keep their massive amounts of data in order if they want to derive useful insights from it.

Finally, we took a critical look at data latency and saw how it can be the main disadvantage ETL. 

After reading about ETL pipelines, where do you think they fit in your business process? Do you use ETL pipelines, or a different architecture? Let us know in the comment section below.

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Today, you can achieve the same benefits of traditional ETL, but in real-time. 

Estuary Flow is a tool that helps you create real-time data pipelines. With Flow, you can add transformations before loading data into your destination systems. But it’s not traditional ETL — it’s a data streaming solution with its own, unique process.

You can learn more about how Flow works here, or try it free.