What Are Data Pipelines And Why Do They Exist

The demand for data has grown substantially in this AI-driven world.

Meaning, there are more and more data pipelines being created.

The funny thing is, when I first started in the data world, no one around me used the term data pipeline.

I am sure plenty of data teams used the term data pipeline.

But personally, I had mostly heard terms like integrations, automations and ETL.

In fact, I am not even sure when I first came across the term. But if you’re a data engineer in this modern era, then much of your time is spent, building, maintaining and keeping data pipelines running smooth.

Even with AI, you’re probably still finding yourself opening up 3,640 line queries, and the occasional custom data pipeline system.

What Do Data Pipelines Actually Do?

Before we dive into the why for data pipelines, let’s talk about the what.

When you look at data pipelines, here is likely what people might say they do.

1. Move data from a source to a destination
2. Sometimes they transform that data
3. And they do all of this repeatedly and reliably without human intervention

That’s the technical function of a data pipeline.

How it happens can vary.

This could be automated SQL, Python scripts, Airflow, Estuary, SSIS, Glue, and so many other tools.

But you do need to think beyond just this when it comes to data pipelines.

Pulling in a recent post from Zach Wilson.

It’s important to think beyond just moving data from A to B. And start thinking in outcomes and ownership.

What is the data pipeline actually doing?

The Real Reason Data Pipelines Exist – Trust

We alluded to this above, but let’s talk about why data pipelines exist. Because, hey we could just manually load data into databases.

Just use:

COPY INTO analytics.raw_orders

FROM @raw_stage/orders/

FILE_FORMAT = (TYPE = ‘CSV’ SKIP_HEADER = 1)

ON_ERROR = ‘CONTINUE’;

Done!

No need to automate anything, right?

After all, we are just moving data from point A to B.

Well, there are many reasons we automate data workflows and turn them into data pipelines. Here are the key benefits we get.

• Timeliness
• Accuracy
• Consistency
• Recoverability
• Scalability

But it goes beyond just recoverability and consistency. At a certain point, the goal isn’t just to move data reliably, it’s to make that data meaningfully valuable to the business.

To do that, we need to think in terms of a few core pillars:

• Integration – Data should flow seamlessly across systems, not live in isolated silos.
• Availability – The right data needs to be accessible at the right time, whether that’s real-time or batch.
• Outcomes –-Data should ultimately drive decisions, actions, or automation—not just exist in a table somewhere.

Now, it’s worth calling out that not every data pipeline you build needs to optimize for all of these at once. In fact, many don’t.

Some data pipelines are purely operational. For example, moving data from a CRM into another internal system to support workflows—not analytics. Others might extract data, apply a few transformations or calculations, and push it right back into the source system to enrich it.

And that’s the point – not all data pipelines are built for dashboards, reporting, or even data warehouses.

So when thinking about pipelines, it’s less about forcing everything into a “data warehouse-first” mindset, and more about understanding the job the pipeline is meant to do.

Because ultimately, a good pipeline isn’t defined by where the data ends up – it’s defined by the value it creates.

Why You Need To Care About These

Integration

Many data teams aren’t building data warehouses; they are just replicating their databases and CRMs into Snowflake or Databricks. Just isolated siloed data that was once in separate systems, now in their own schemas and un-integrated data sets.

Part of what the data pipeline is supposed to handle in terms of logic(and as determined by the data modeling process) is the integrations. The parsing, cleaning, and adding of keys that allow you to join data across systems. This also means you’ll likely need to consider what data sets will need to join with each other in the source systems themselves.

Availability And Usability

Many data workflows require data analysts go to the source systems and extract the data in an Excel, then from there they will need to manually process, set-up VLOOKUPS and build out a “database” in Excel.

Part of what data pipelines do is move data into the data warehouse making said data more easier to access.

And this is not just for end-users like analysts, but also automations and LLMs. Having data centralized means it’s easier to work with said data, especially when it’s well modeled.

As data becomes easier to access by the right users, the more they can actually use it for.

Scalability

At a certain point, having half automated scripts run by cron might be too chaotic. Sure, if you only need 2-3 simple data workflows managed. This might be fine.

But as your data use cases grow.

As your data team grows.

As the end-users of said data grows.

You’ll want data pipeline systems that make it easy to automate.

Think about having to rerun 200 data pipelines. That’s logistically difficult if you can’t easily kick all the jobs off and track their successes or failures.

Outcomes

Data pipelines can be easily built without pipelines. But I think it’s important to think about the “so what”. Why are you building your data pipeline?

Is it to automate a process, and if so, does it need to ingest data into the data warehouse?

What business goal are you hoping to drive with the building of your pipeline? Every new data pipeline you build without a clear purpose just becomes a technical liability over time. It increases cost, maintenance time, etc.

So what is your team hoping to do with the data pipeline?

Here are a few examples, you could say your data pipeline:

• Reduces unnecessary discounting by analyzing win/loss data and discounts to show where deals close without price concessions.
• Improves onboarding success by identifying which onboarding steps and early product behaviors correlate with long-term retention.
• Reduces support costs by linking support tickets to product events to eliminate the root causes driving repeat issues.
• Increases retention through proactive customer success by alerting CS teams when usage drops or support volume spikes.

Timeliness

One of the great things about data pipelines is that they are easy to track and can run whenever you need them to.

Meaning, if you need them to prepare a data set prior to 8 AM, they can do that. You know how long it’ll take(assuming nothing goes wrong, and even then, likely you can set up some level of recoverability).

An analyst doesn’t have to wake-up early to make sure the data gets processed in an Excel file. Instead, it can land in a table and be picked up as needed.

Accuracy

We in the data world love talking about data quality. Well, the data pipeline is one of the many places where data can be transformed improperly. Data can be duplicated, removed, and or altered in such a way that it is no longer accurate.

Data pipelines are a great place to check for data issues.

This can occur before even processing the data to check that the source contains the expected fields and ranges of data. From there, as you transform the data throughout your pipeline, you’ll likely need to implement other checks.

Consistency

The problem with “Excel data pipelines” is they offer room for errors. You copy and paste the wrong data set or forget to update a formula.

A programmed data pipeline, is repeatable and consistent.

You can create logic to check for errors like the wrong data being inserted or if dimensional data is missing. So even if you do have some issue, it can flag it early. It also helps avoid a fat-finger issue where someone accidentally thumbs in a number.

Recoverability

Sometimes, the wrong data enters a data workflow. Meaning, you’re going to want to be able to detect that and then be able to rerun our data processes easily without having to worry about what else could go wrong.

We don’t want to worry about duplicate data.

We don’t want to worry about a small step being missed.

So having the process codified ensures we know exactly what will happen in terms of data tables being populated.

Final Thoughts And What Is Coming

Data pipelines are everywhere in companies. They take many different shapes and forms, but overall, their goal is to do more than just move data from point A to B.

A good data pipeline creates trust. It gives teams confidence that the data they rely on will be there when they need it, in a form they can actually use, and in a way that supports real decisions and real workflows. That might mean powering a dashboard, feeding a model, triggering an alert, enriching a CRM, or simply saving an analyst from spending three hours every Monday stitching together CSVs.

That is also why the conversation around data pipelines needs to mature a bit. Too often, people talk about pipelines as if they are just technical plumbing. But the best data engineers know that pipelines are really delivery systems for business value. They are how raw operational activity becomes something usable, repeatable, and scalable.

And in this AI-heavy era, that matters even more.

If you have any questions about your AI or data strategy do reach out!

In case you missed my last article, we’ve already covered some of the various data pipelines that exist(I even labeled one the Excel Data Pipeline in the past).

If you’d like to read more about data engineering and data science, check out the articles below!

Why Is Data Modeling So Challenging – How To Data Model For Analytics

What Is A Data Platform And Why You Should Build One

Throughput vs Latency: Understanding the Key Difference in Data Engineering

How to build a data pipeline using Delta Lake

Intro To Databricks – What Is Databricks

Data Engineering Vs Machine Learning Pipelines