What Is Data Pipeline Orchestration and Why Does It Matter for Developers?

Is your data scattered all over the place, coming from different sources and getting lost in various silos across your organization? 

If yes, then you might better understand how tough managing data is without pipeline orchestration, and if not, you should definitely know what this process is.

But if you feel like you’re the only one struggling with this, you’re definitely not. A lot of businesses from all kinds of industries are experiencing exactly the same challenges and missing out on great opportunities from their data, just like you. 

Stick around, because that’s exactly what we’re here to explore.

What is data pipeline orchestration?

Data pipeline orchestration is pretty much what it sounds like: a process that automates, manages, and schedules the flow of data between multiple systems and tools through pipelines to make sure it’s delivered in the right order, at the right time, without manual steps or errors. 

But data pipeline orchestration isn’t just about securing data movement; it also coordinates the execution of interdependent tasks to guarantee that each task runs only after the previous ones are successfully completed, so that nothing breaks along the way. 

So we can say that the whole point of this process is to make it easy-peasy for organizations to move all the data that matters accurately (and seamlessly) between their various sources and platforms.

The logic behind data pipeline orchestration

Keep in mind that data pipeline orchestration operates quite differently from data pipelines because, as we mentioned earlier, its main role is to coordinate and automate all the tasks that move and process data without any manual work or coding involved.

So, in this section, we will break down the logic and modus operandi of data pipeline orchestration.

1. Defining the workflow

To kick off our list, we’ve got to talk about workflow definition, a step where you have to break down the pipeline’s business, what tasks need to run, in what order (dependencies) and under what conditions (triggers), a setup that you might also hear referred to as directed acyclic graph or DAG, (a topic for another day). 

2. Scheduling tasks

The orchestration system schedules tasks to run automatically based on a predefined schedule or certain triggers: pulling data every 6 hours, when a specific database gets updated, when a new lead fills out a form, we could go on, but you get the idea here.

3. Dependencies

This function is, as the name suggests, responsible for tracking which tasks rely on others, so things like data transformation don’t happen before extraction is fully done. The point of this is to avoid sending any mistaken or incomplete data through the pipeline.

4. Processing tasks

This step runs automatically, which means the system takes care of everything, from extracting data from a source and cleaning it up to adding it into the database and everything in between. 

5. Instant alerts

Throughout the data pipeline, the orchestration system will monitor any potential errors or delays, and this is where the alerts function helps out. Whenever an error occurs, the team in charge gets notified right away so they can figure out what’s wrong and fix it quickly. 

6. Managing task failures

If a task fails to execute, it’s not the end of the road, not even close. Orchestration tools can automatically retry the task, bypass it based on preset rules, and if it still fails, the system will pause the rest of the workflow altogether to prevent things from going off track.

7. Logging and reporting

Does the data pipeline orchestration get any better than that? And the answer is, yes, it does! Here’s how: all the tasks’ performance that the system does can be logged, and so your team can get full visibility into how the pipeline is performing. 

Eventually, this will allow you to instantly fix bugs, meet audit requirements, and regularly improve your workflows.

Why does data pipeline orchestration matter for developers?

Now that you know what goes into the orchestration system, let’s hop ahead to see how it directly (and indirectly) helps developers. 

• Scalable solution

Orchestration tools are built to scale; it doesn’t matter if you’re processing 100 records or 10 million, you don’t have to do any of the heavy lifting, like redesigning everything, just because your product or user base is growing. 

Now, you’re probably thinking things like: “What does this mean for developers?” Well, with data pipeline orchestration, they can easily add new services or processes without breaking existing setups like code, APIs, or workflows. 

Even better! When their data volumes grow, they don’t have to rebuild their pipelines from scratch because the system scales with them, and all of this eliminates headaches related to downtime, errors, and regular maintenance. Pretty much a lifesaver, right?

• Simplifies complex workflows 

As software development projects grow, so does the complexity of the workflows behind the scenes. Different data sources, APIs, and internal tools that must interact in the right sequence.

Not only does the orchestration system establish dependencies between tasks and prevent race conditions, but it also makes sure these tasks run in a logical flow, with built-in rules to fix any disruptions along the way.

We know, we know, you definitely need an illustration here! 

Let’s take the example of AI-powered demand forecasting, a model that extracts data from several sources: sales platforms, inventory systems, customer behavior tools, and maybe even third-party sources like market trends. 

Needless to say, each of these data sources should be processed sequentially and cleaned up before the model starts generating accurate forecasts. Now imagine doing all that without orchestration? Total chaos! But with orchestration in place, everything runs on schedule: the system manages the timing, establishes task dependencies, detects errors, and connects the dots between systems, so instead of worrying about how the data gets there, your dev team will prioritize identifying demand patterns and optimizing stock levels, and honestly, that’s a much better challenge to have!

• Better teamwork 

When your orchestration system is in place, your workflows become a fully trackable setup where you and your team can visualize everything in real time: the tasks, dependencies, timings, sequences, and even alerts. 

What are the takeaways from all of this, you ask? Better monitoring, faster troubleshooting, and more efficient team collaboration, all thanks to a clear overview of your entire data pipeline. 

And this is what it looks like for your team:

• Your data engineers can see exactly where their ETL fits in the process.
• DevOps can track resource usage and adjust scaling policies. 
• Product teams can understand the total turnaround time it takes for every process

• Minimizes human errors

Without orchestration, developers often rely on manual scripts to move data, which will always leave room for human errors, because no matter how careful your team is, when they are overwhelmed or distracted, they might forget a step, run something twice, or mistime a trigger. 

But orchestration tools can run your data pipelines over and over, automatically and before a real, breathing human ever does. And even if one task fails (which can happen sometimes), you don’t have to re-run the entire pipeline; you only have to set automatic retry rules, and you’re good to go! 

Just configure your workflow once, and the orchestration system will do all the legwork every time, and save you from “oops, I forgot to upload the data” moments.

• Easier data integration 

It wouldn’t be a discovery to hear that developers rarely work with just one tool or platform; they use a combination of databases, cloud services, analytics tools, or messaging systems, simply because most projects rely on a mix of different software. 

Data pipeline orchestration to the rescue! 

So instead of writing tons of custom code to make each tool talk to the others, orchestration platforms will do all the heavy lifting for you. They will facilitate the communication between different systems, manage data transfer, and, of course, control when each step should happen.

If your team is, for instance, pulling data from multiple platforms, an ETL/ELT pipeline tool like Windsor.ai will be your one-stop solution. 

Windsor.ai helps developers and marketing teams integrate all the data that matters, from 325+ sources, including Facebook, Google Ads, HubSpot (or any other data stream) into a single platform. From there, you can plug that data into their orchestration workflows and send it to a data warehouse, database, BI tool, or any other system in less than 5 minutes with absolutely no code and zero manual effort.

Common challenges in data pipeline orchestration

At this point, we can agree that data pipeline orchestration is a game changer for managing the data tasks of your organization, BUT, and here’s the big but, it’s not all sunshine and rainbows. 

It often comes with challenges that your team (especially the data engineers) will likely face when implementing data orchestration in your systems. 

Let’s explore together some of the most common ones.

• Security measures 

Security concerns definitely deserve a spot on our list of challenges, and it’s not hard to see why. Your data moves throughout the orchestration process, and a lot of it is often sensitive. 

Implementing them in a way that complies with security measures is usually a time-consuming mission, because this step requires adding regulatory standards such as data encryption, access controls, multi-factor authentication, and audit trails. 

• Data integrity 

How can you tell if your orchestration system is doing its job? By looking at the quality of data in the pipeline, of course! The best way to do this is to implement checks and alerts to spot any issues, protect your data integrity, and prevent any loss.

But it is all easier said than done, especially when you have to manage large datasets that require real-time processing.

That’s what Windsor.ai helps you with, by continuously monitoring your data pipelines and immediately notifying you about any unexpected errors.

• Data silos

It happens more often than you’d think that different teams in an organization have their own separate data sets. You might find sales data inaccessible to the customer support team, and the legal team can’t view product data.

This isn’t just about data pipelines not talking to each other; it goes much further, it slows down decision-making, wastes time, and, in every sense of the word, lowers the value you get from your data. 

So breaking down these silos is challenging and at the same time inevitable to create a well-orchestrated data workflow across your organization.

• Find the right talents 

Data pipeline orchestration requires a wide range of skillsets, such as data engineering, software development, and data analysis, all of which need top data professionals, and the truth is, finding talents who check all those boxes is usually not that simple. 

You’ll need to build a team that knows how to set up and manage orchestration tools properly. 

Found the right talents? Congrats. But you’re not done yet. Since the technical and operational aspects of orchestration keep evolving, you’ll also need to invest in ongoing training to keep everyone in the loop. 

Sounds tough? It can be. But it’s certainly not impossible. And this brings us to our next challenge.

• Stay ahead of changes

With data pipelines, you will always need to face change, because they’re not static, and neither are your organization’s needs; that means you’ll constantly be updating logic, transformations, and integrations to keep the orchestration system running.

The real challenge here is to handle these changes without disrupting your orchestration setup or data integrity, but don’t worry! With proper planning, know-how, and consistency, it’s totally manageable. 

Conclusion

There you have it! 

We have told you as much about data pipeline orchestration as you should know before you start implementing it. 

Depending on your industry, you can automate your data movement. And it’s not as tough as you think; there are plenty of orchestration tools that will help you move your data between endless systems with minimal involvement on your part.

Get started with Windsor.ai today and activate your 30-day trial to benefit from optimized pipelines and seamless data automation!