Data Operations

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

Cole Lehman
Cole Lehman

It’s easy to confuse data pipelines and extract, transform, and load (ETL) pipelines. Many people use the terms interchangeably, but—while related—they are actually two different things. The short answer is that ETL pipelines transform data as they move it to a central data warehouse and data pipelines can be simple workflows that simply move raw data from place to place.

Once you know the difference between data pipelines and ETL pipelines, you can feel confident in choosing the right data pipeline tools and building out your data warehouse strategy in more detail. In this blog post, we‘ll cover the different types of data pipelines, what makes ETL pipelines valuable, and what tools you can use to streamline your data management operations.

What is a data pipeline?

In its most basic form, a data pipeline is a series of processes that moves data from place to place. Sometimes data pipelines clean and transform data along the way, and sometimes they just transfer raw data from source to destination.

A modern data pipeline replaces repeatable manual or human-assisted tasks. For example, instead of having a person manually download a CSV from Salesforce and upload it to your data warehouse every week, you can build an automated data pipeline to do this on a regular schedule.

You can think about data pipelines like water pipes that transport water from point A to point B. Point A is where raw data is collected, and after being processed, point B is where cleansed data ends up.

Data pipelines vary in complexity. They can be a single data ingestion pipeline that moves data from Salesforce to a cloud data warehouse. Or, they can be whole data science pipelines (actually made of many individual pipelines) that cover everything from ingestion to analytics.

Here’s a closer look at the different kinds of data pipelines.

The different types of data pipelines

Some data pipelines go from source to cloud data warehouse. Others, like reverse ETL pipelines, go backward from cloud data warehouse out to other locations. There are data pipelines that connect one SaaS tool to another in real-time and data pipelines that run jobs at night while your employees sleep.

  • Data ingestion pipelines: A data ingestion pipeline moves raw data from a wide variety of sources to destinations like cloud data warehouses. Data ingestion pipelines help data teams keep their data flowing from place to place so that data scientists, data analysts, and others can make a higher percentage of the data useful to the business.
  • Data science pipelines: A data science pipeline is a collection of processes and actions that turn raw data into actionable business intelligence. It automates the flow of data wherever possible, handling data transformation, data ingestion, and any other important data processes. (Data science pipelines are not to be confused with singular data pipelines, which are built between data sources and tools like CRMs.)
  • Batch data pipelines: Batch data pipelines work best for complex datasets and large volumes of data. They typically run outside normal user hours to avoid impacting other workflows using the data source. Batch data pipelines typically handle low-frequency data jobs that relate to things like payroll and billing where there is a large interval between batch processing of datasets.
  • Streaming data pipelines: When you need data in real-time, a streaming data pipeline is required. These move data from source to destination as soon as any new data is generated. Due to the always-on nature of these data streaming pipelines, they need close data monitoring to avoid cascading errors. They’re used for time-sensitive datasets like fraud detection, critical operating reports, and customer behavior monitoring.
  • Extract, Load, Transform (ELT) pipelines: An extract, load, and transform (ELT) tool accomplishes exactly what it sounds like—it loads your extracted data into a data warehouse and then transforms it using a data transformation tool like dbt. The main difference between ETL and ELT is the location where data transformation happens. In ELT tools, it happens inside the data warehouse instead of in a staging area in the ETL process.
  • Reverse ETL pipelines: Reverse ETL is the process of moving data from your central data warehouse out to your operational systems, cloud apps, and other software solutions. Usually, ETL goes the other way—extracting data from periphery sources, transforming that data into the proper schema and format, and then loading that data into your data warehouse.

Now that you know the different kinds of data pipelines and how they process data, let’s talk about ETL pipelines and what they do for your business.

What is an ETL data pipeline?

An ETL data pipeline is a process that moves data from periphery and external data sources into a central data warehouse or data lake. But it doesn’t just move data to a new data storage location. It transforms data into usable formats and schema before it loads the extracted datasets into your warehouse, data lake, or database.

All types of data pipelines are used in conjunction with ETL pipelines. They provide a way of getting your raw input data into an ETL pipeline and out of it again at the end of its journey.

A typical data pipeline might consist of many different steps, such as loading data, cleansing it (e.g., removing outliers), transforming it (e.g., splitting it by month), and finally loading transformed results back into an ETL pipeline which will take care of storing them on the final stage of the process.

Many companies have teams of dedicated data scientists, data engineers, data analysts, and machine learning specialists to make all their big data useful to the business. Running DataOps at scale requires a modern data stack and automated processes like ETL to make sure data analytics teams have constant streams of accurate and up-to-date data.

Why do you need ETL pipelines?

You need ETL data pipelines whenever you have multiple data sources—like Salesforce, Marketo, Hubspot, Google Ads, Facebook Ads, social media, website analytics, APIs, or cloud apps—and you want to combine different types of data from all those sources into valuable insights.

Start by setting up automated ETL pipelines from each data source to your data warehouse. Once the data is extracted, it’s transformed into compatible formats to match your cloud data warehouse, and finally loaded into the destination.

Now that all your data is in a central data warehouse, your data analytics team can model it for strategic insights. That means you can measure advanced business intelligence metrics like lifetime customer value, brand loyalty metrics, and advanced customer journey behaviors.

Here’s a quick list of the high-level benefits your business can get from using ETL:

  • Increased decision-making speed
  • Easier data migration
  • Improved data quality
  • Faster and more efficient data integration
  • More accurate data analysis
  • Better data visualizations
  • New datasets for machine learning analysis
  • Central source of truth for company data

ETL data pipelines lead to clean, accurate, and real-time datasets that make it possible to build new software products and features faster. When your product data is cleansed, structured, and accessible, you can quickly iterate on new searches and filter ecommerce features.

Every time you use ETL data pipelines to move data from a new source to your data warehouse, you add to the list of possible opportunities for your business.

What's the difference between a data pipeline and an ETL pipeline?

The main difference is that ETL pipelines not only move the data from source systems to destinations, but they also transform it along the way. Data pipelines don’t always perform transformation—sometimes they just move raw data from location to location. That doesn’t mean you don’t need data pipelines, they’re crucial for many processes. And they even feed ETL pipelines.

So, in this case, it’s a pretty simple answer. You need data pipelines when you just need to move data from place to place. You need ETL pipelines when you need to transform data before you load it into a destination. Here’s a list of our favorite data pipeline tools—ETL or otherwise.

What data pipeline tools can I use?

There’s a wide field of options when it comes to data pipeline tools. You can choose between enterprise-ready ETL pipeline tools and fully customizable open-source pipeline tools. There are also cloud-based data pipeline tools and custom-built ETL pipeline tools.

When evaluating, it’s essential to define your use cases, budget, desired capabilities, and data sources to ensure you choose the right data pipeline tool for your business.

Here are some of our favorite data pipeline tools:

Fivetran data pipelines

When you collect data from many sources to feed your data science pipeline, Fivetran helps you securely access and send all data to one location. This tool allows data engineers to centralize data effortlessly so that machine learning algorithms can cleanse, transform, and model the data.

Stitch ETL

Stitch delivers simple, extensible ETL built specifically for data teams. It delivers analysis-ready data into your data science pipeline. With Stitch, extract data from the sources that matter, load it into leading data platforms, and analyze it with effective data analysis tools. From there, your machine learning algorithms take over and find the patterns you need to solve business problems.

Shipyard data orchestration

Shipyard integrates with dbt, Snowflake, Fivetran, Stitch, and many more ETL tools to build error-proof data workflows in minutes without relying on DevOps. It allows your data engineers to quickly launch, monitor, and share resilient data workflows and drive value from your data at record speeds. This makes it easy to build a web of data workflows to feed your data science pipeline with many data sets.

Get started with any kind of data pipeline

Whether you need an ETL data pipeline or a data pipeline to simply connect two sources in real-time, it’s the technology that makes this possible. We built Shipyard’s data automation tools and integrations to work with your existing data stack or modernize your legacy systems.

If you want to see for yourself, sign up to demo the Shipyard app with our free Developer plan—no credit card required. Start to build data workflows in 10 minutes or less, automate them, and see if Shipyard fits your business needs.