What is a Data Pipeline? The Business Essentials

What is a data pipeline? On a basic level, it’s pretty self-explanatory. Just like oil or water, data is rarely stagnant. It must travel between disparate systems, and often must be cleaned or processed along the way. You don’t want your data to lag, get stuck, or arrive at its destination in an unusable condition. 

Ok, let’s translate that cute analogy into a definition:

A data pipeline is a technological pathway that captures data from a source and delivers it to a destination in a desired state. 

But if you’re here, you probably want to go deeper than a definition — you want to know what a data pipeline looks like in detail, how it works, and how it impacts your business day-to-day. 

In this post, we’ll cover…

• Data pipeline components
  
• Use cases for data pipelines
  
• How data pipelines are designed
  
• Tools and technologies you can use
  
• Data pipeline challenges and best practices

Well-managed data makes every business process more efficient, from marketing to manufacturing. And pipelines are the connective tissue of your data infrastructure. 

Let’s take a closer look.

Components of a Data Pipeline

There are many ways to approach data pipeline architecture. We’ll define each data pipeline component below, but keep in mind that these components can look different or come in a different order than the way they’re laid out here.

But in general, this framework is a great starting point to understand how data pipelines work. Based on this, you’ll start to see what variations are possible. 

Data sources and ingestion

Data pipelines need to first ingest (or capture, or collect) data from a source system. The source might be a SaaS API, database, cloud storage, website, event stream from IoT devices, or something else entirely. 

There are lots of ways to capture data. Methods include:

• A push-based HTTP mechanism. 
  
• An event-based, real-time streaming mechanism. 
  
• A batch-loading mechanism that relies on polling or repeated queries.

Data cleaning and pre-processing

Data taken directly from the source is rarely ready for consumption. Before the pipeline can proceed, it needs to make sure the data is tidy and in the correct format. 

Either as part of the ingestion step or immediately after, the pipeline applies basic transformations to the data. What these look like will depend on the pipeline, but often, they’ll change the data type and validate the data against a schema. 

When this is done, the pipeline can move on to the next step with little risk of error. 

Data storage and management

Although pipelines are data in motion, the data must also be stored as it moves through. 

This storage can be ephemeral (for instance, a Kafka topic that flushes periodically) or long-lived (a cloud storage bucket that you can re-purpose or use as a backup). You can tweak this management strategy based on your needs

Data transformation

Now, we can add another layer of processing: data transformation. This goes beyond the bare-minimum cleaning and schematization we did earlier. 

Depending on your data needs downstream, it might benefit you to:

• Filter data
  
• Aggregate data
  
• Join multiple datasets

These kinds of transformations are often cheaper and more efficient to do here than in the destination system. 

Data delivery and consumption

Finally, we’re ready to move data to the destination system. This is often a data warehouse, from which your team can complete data analysis and visualization tasks. But it can also be an operational system (a CRM, an alerting system… anything that takes business-oriented action based on the data).

Designing an Effective Data Pipeline

You might design your data pipeline with the help of a data pipeline tool or platform, or you can build it from scratch if you have the engineering chops. 

Either way: don’t get ahead of yourself! It’s easy to get busy building and forget the big picture. Keep these things in mind. 

Understand business requirements and goals

If you’re in management, marketing, sales, or logistics, you’re probably nodding vigorously. If you’re an engineer, this topic might make your eyes glaze over. 

Listen, this one is a cliche for a reason. There’s a reason every data technology article brings this topic up. Because the disconnect between stakeholders in a company can manifest itself as a data pipeline that doesn’t actually do what you need it to do.

Before designing a pipeline, have a meeting with all the stakeholders to make sure you’re all on the same page about the business goal. 

Select appropriate technologies

The first question to ask yourself here is: Will we be building this from scratch, or using a data pipeline tool? 

If it’s the former, you’ll need to pick all the components — the storage, connection mechanism, processing engine, etc. 

If it’s the latter, you’ll need to pick a pipeline tool. As you evaluate your options, take a peek under the hood to make sure they’re built in a way that will meet your needs. For instance, plenty of pipeline vendors claim to support change data capture, but ultimately use a batch processing framework, which negates CDC’s real-time data benefit.

Build a scalable and flexible architecture

Data volumes worldwide are growing at an alarming rate. Here, I’ll cite a source, but you know what it looks like, I think (it’s an exponential growth curve). 

So, even if you think you know how much data your pipeline will need to handle in the future, be open to the possibility that you might be surprised. And build accordingly. 

If you’re building your own pipeline, use a distributed architecture that can scale automatically. For storage, choose a cost-effective cloud-based solution. For processing, use a distributed processing framework.

If you’re using a pipeline platform, make sure their pricing structure will work in your favor as your data grows. 

Ensure data quality and security

Implement a strong data governance strategy to make sure your data is consistent, compliant, and secure. Or, make sure your platform of choice incorporates these features.

• Define user personas, their permitted actions, and the corresponding access grants. 
  
• Research possible security vulnerabilities of each component of the pipeline and take action to prevent them. 
  
• Ensure data is encrypted and complies with any applicable regulation, like HIPAA or GDPR. 
  
• Deploy your pipeline within a secure network. 

Test and optimize the pipeline

Before you deploy your data pipeline to production, test it thoroughly on a development server. And after you deploy the pipeline, be ready to optimize it further — you can never be 100% certain how the pipeline will perform under production workloads. 

Tools and Technologies for Data Pipelines

Now that you’ve taken mental note of some best practices, it’s time to actually start assembling your data pipelines. 

Because this is a blog post and not a book, we won’t list every piece of tech that can fit into a data pipeline. But we will introduce some major categories. 

Extract, Transform, Load (ETL) tools  

If you’ve decided you’d rather not build your pipeline infrastructure from the ground up, you need to get your hands on a data pipeline tool. ETL tools are a popular subset of data pipeline tools in which transformation happens before loading to the destination. 

Typically, these are SaaS tools with intuitive user interfaces. They offer plug-and-play components to assemble data pipelines, while handling the infrastructure on your behalf. Read about popular ETL tools here.

Cloud-based data processing and storage services

Unless your organization’s security policies require on-premise infrastructure, your best bet is to use cloud-based tools for your data pipelines. 

This is especially true for your storage components and data processing engines, which do a lot of heavy lifting and are really hard to scale on-premise. If you’re using an ETL tool, it’ll usually come with a managed option, meaning the vendor hosts components in the cloud on your behalf. 

Big Data technologies 

To make sure your pipelines scale, use processing technologies designed for Big Data. This includes Hadoop, Spark, or real-time processing techniques based on modernized Map Reduce. 

These processing technologies are distributed in nature, which means they’re easy to scale up and down cheaply, especially when they’re cloud-hosted. If you’re using an ETL tool, this component should be included.

Data visualization and BI tools 

At the end of the data pipeline, it’s up to you to put your data to good use. You can hook up data visualization and BI tools to the destination system to translate the data into actionable, digestible information in the form of dashboards, reports, or alerts.

Machine learning and AI platforms 

Similarly, take analysis even deeper with machine learning models and AI solutions based on data in the destination system. Just don’t build anything that’s smart enough to take our jobs, okay?

Example Data Pipeline Use Cases

By now, your mental model of a data pipeline is starting to come together. But we’re also getting far removed from the actual goal of data pipelines: to help businesses run better! 

Where do you need data pipelines for business? Simple: any time two systems need to work together with the same data to accomplish a goal, you need a data pipeline. 

Let’s ground that down with a few common business use cases. 

Customer analytics and insights

To gain meaningful information about customers, say, in e-commerce, you need a pipeline between your customer database and your online store or application. You can craft timely, customized marketing and in-store experiences by combining their behavior data with information you already know about them.

For example, if a customer views a jacket and then leaves your site, you can send them a personalized email that addresses them by name, includes a coupon, and promotes other jackets that you sell.

Fraud detection and prevention

You can detect and prevent fraud with a data pipeline that connects a database of known information with current activity from IOT devices or applications. 

For example, a financial institution might have information in a database about a certain customer, including their hometown and where they usually use their card. If an ATM withdrawal is suddenly detected thousands of miles away, a real-time data pipeline can help the institution flag the transaction as potential fraud.

Supply chain optimization

For a supply chain to work well, merchants need to balance routine stocking and logistics with wider market trends. To do so, they rely on a variety of operational databases and analytical systems, which must all be united around the same information. Data pipelines make this possible. 

Predictive maintenance

Using data pipelines to combine current data with historical models helps all sorts of industries stay ahead of the maintenance curve.

For example, machines on a factory floor might be equipped with IOT sensors that measure their usage, performance, and other data points. By piping this information into a database or data warehouse, factory managers can analyze patterns of when each component tends to break, and start to replace and upgrade things proactively. 

Personalization and recommendation engines

Whenever you play music or stream TV on your favorite platform, a data pipeline connects your in-app activity to a database. That’s how it makes the intelligent recommendations that help you find your new favorite artists and shows.

Challenges and Best Practices for Data Pipelines

Before you race off to build the data pipelines that’ll uplevel your business (or nicely ask the engineering team to build them), there are a couple more challenges to keep in mind. 

As we’ve seen, pipeline building is high-stakes. Some of these issues might come up for you — that’s totally fine and normal, as long as you’re prepared.

Dealing with data silos and heterogeneous sources and destinations

Earlier in this article, we casually told you to “connect to the source or destination system” as if that was easy. In reality, odds are you’re run into at least one of these roadblocks:

• Having a bunch of different source and destination systems that all require separate integration mechanisms.
   
• Having one particular source or destination system that’s a pain to connect to because it’s a complicated system or has special security measures.

Managing data privacy and compliance

Another thing I breezed past earlier. “Oh, just comply with HIPAA and GDPR!” I wasn’t being naive, I was just trying to keep morale high. 

As a rule, the more separate components you’re self-managing, the harder this gets. And of course, the privacy and compliance of the data pipeline are impacted by the privacy and compliance of the source and destination systems. 

Handling large and complex datasets

The scalability of your pipeline’s storage and processing will be tested when it comes time to move a large and complex dataset, or when a previously small stream of data balloons unexpectedly. This is why it’s important to be prepared to optimize and iterate on your production pipelines.

Ensuring data consistency and reliability

Cleaning data and validating it against a schema can be very easy or very hard. This depends entirely on the data you’re ingesting and what system it comes from. This becomes more complex when you’re dealing with a variety of sources.

Monitoring and maintaining the pipeline

Just keeping all these challenges in mind isn’t enough: you must actually keep track of how your pipeline is handling these challenges and be ready to spring into action at a moments notice. You should incorporate alerting, data visualization, and testing to monitor your pipeline — don’t skip this important step, or your pipeline could fail silently!

Solution: Let someone else handle it for you

Maybe all those challenges are obvious to you, and you have the engineering resources lined up to knock them down and keep your pipeline running smoothly. 

But maybe they make you want to stick your head in the sand. If that’s the case, working with a managed data pipeline tool might be the right path for you. That’s because a good data pipeline tool manages most of these challenges on your behalf by:

• Providing pre-built connectors for a variety of systems. 
  
• Building compliance and privacy into their tools. 
  
• Managing scaleable cloud resources that can handle your biggest datasets. 
  
• Including robust validation and schema management capabilities. 
  
• Keeping you in the know with reporting tools, dashboards, and alerts. 

Plus, with managed solutions, you can always throw in the towel and call in expert support when you’re swamped or lost. 

Conclusion

That was a lot of information. Here’s all the ground we covered: 

• We got to know the components of data pipelines, and the different ways they can fit together. 
  
• We explored best practices for building data pipelines… as well as common challenges you should prepare for. 
  
• We discussed the broad categories of technologies that go into data pipelines. 
  
• And we grounded everything down with real-world examples in different industries. 

Still, we only just scratched the surface. 

If you’re ready to learn more about data pipelines, ETL, and related technologies, check out the rest of the Estuary blog. 

And if you’re looking for a fully managed data pipeline solution, you can sign up to use Estuary Flow for free. Flow integrates with dozens of systems, is scalable, user-friendly, and processes all your data in real time.