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An Introduction to Data Pipelines

Data is at the heart of all modern enterprise applications, so facilitating its seamless flow from one location to another is a vital part of the software development process.

A data pipeline is a set of processes or actions that enable the efficient flow of data from one place to another. Think of it as a public transport network, with each route being part of the pipeline and each station representing a particular source, table, or data storage point.

For example, if the data in a SaaS application needs to be sent to a data warehouse, it passes through a data pipeline. If specific values need to be substituted with alternatives and transferred to another table or API, that process is another part of the pipeline.

For organizations, it's important to understand how the benefits of a well-configured, efficient data pipeline can support the company's business objectives. However, the construction of a proper data pipeline can be a slow process, especially if starting from scratch. To ease that process, we've compiled some guidelines based on our own first-hand experience with data pipelines. 

Data Pipelines in Support of Business Objectives 

The sheer amount of data businesses are collecting these days has made it more important than ever to be able to make the flow of data efficient. Especially since competing in today's industries means having the ability to collect more diverse and segmented data than ever before. For a lot of companies, the problem becomes creating a system that can collect, process, and display that data efficiently, allowing them to use it to support various business objectives.

It's an important piece of the puzzle and one that's necessary if companies hope to be around tomorrow. The McKinsey Global Survey on data and analytics reported that a company´s ability to monetize data seems to be correlated with industry-leading performance, and the industries that are making it happen are financial services, tech and energy.

Data pipelines can mean stronger data security and compliance practices, streamlined data management, improved BI, better sales and operational decisions, faster and relevant innovation efforts and much more. However, they need to be well thought out and crafted with those goals in mind.  

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Defining the Data Pipeline Process

There are a multitude of ways to put together a comprehensive data pipeline, but overall, it performs data extraction and collection, data processing and data visualization.

So, how do you structure a data pipeline that supports business objectives? There are a multitude of ways to put together a comprehensive data pipeline, but overall, it performs data extraction and collection, data processing and data visualization.

The first and most important step is deciding what data to work with, where it needs to be collected or transformed, and how it should move between sources. This process should link directly to the company's business objectives. For instance, maybe the purchasing team needs data on customer buying habits in multiple locations, with the goal of moving stock to the most profitable region.

If starting out with nothing, this means manually selecting every field, table, data source, transformation, or whatever is needed, and defining how they link across the pipeline. It is far more beneficial in the long term to spend time doing this when first building the pipeline, rather than risk losing time and money when it's too late. This process can be laborious, but it only needs to be done once. After that, automation can be brought into play.

The goal here is to ensure that the most important data is easy to locate and in the same format. Without this initial clarity on what needs to be achieved, both from a technical and business standpoint, costly delays are inevitable. Which is why it's imperative to have a technically capable team that also has a good understanding of the business.

Working with the data without understanding how it's used can lead to issues and if you don't understand that mistake you have to wait for the right people to look at that info and correct it. Most businesses can point to one important database, but for larger businesses, they may have a variety of tools or databases that will require a well thought out plan.

From an engineering perspective, it would be great for businesses to think both about how they function now and their vision for the future. If they can put energy into envisioning how they want to be using or visualizing data down the line, it's easier for engineers to create a system that could make that possible from the beginning. 

Working with Automation 

When thinking about automation, it's important to determine if your data processing and visualization will be in the cloud, and if so, which cloud provider. This will change the tools and frameworks available to you. Usually small business are already using the cloud and it's easy to stick with what they are already using. (If you need more info on cloud agnostic approach, we've got you covered, just check out this article.) For larger, businesses that may have an agnostic-cloud approach they may have access to a greater number of tools and resources.

With the addition of automated tools, the manual process of moving, adjusting, or analyzing data becomes far simpler and reproducing those processes is a breeze. Apart from being a huge time saver, automation allows engineers to reproduce almost any process in the data pipeline and quickly debug it. With a well-structured automated data pipeline, it's easy to resolve issues with transformations as there's no need to adjust the code for every individual process.

Depending on the data pipeline, companies need someone who knows the relevant frameworks and technologies and is very knowledgeable about cloud architecture in order to implement the right automation tools. For existing pipelines, these experts can look at how they are structured and what tools they are using, then work on either optimizing them or changing them completely.

Our team at PSL has spent a considerable amount of time training in this space, creating proofs-of-concept for each tool in Google Cloud Platform (GCP), both on client projects and to build internal capacity. We've also been working on how to determine efficiently and cost effectively what to automate.

Aiming for Visualization in Data Pipelines 

When it comes to well-designed data pipelines, the principle is always the same: how can we make better use of the data that we have? Data dashboards and visualizations are very important to this principle, so it helps to think of the data pipeline process as a means to boost business intelligence.

To get the most out of visualization, companies should start by being honest about what they want to do and where they want to be in the future. Most businesses can work with simple metrics to start with, all of which are usually tweaked or adjusted several times, so it's good practice to leave the more complicated analysis for later. With the right approach, interesting results will start to become apparent in the visualizations, giving new insights into what to monitor next and what new processes to add to the pipeline.

[RELATED CONTENT | How to Get Started with Data Lakes]

Moving data through a pipeline to another database allows companies to look at certain indicators, such as sales per region, users per month, or anything that relates to the business goals. At PSL, we work with a client that has 40 indicators of how it is performing, which has been easier for us to achieve after moving data from a transactional to a dimensional database through the pipeline. 

Planning for Growth

For companies that deal with large amounts of data, perhaps maintaining several data silos or warehouses in the cloud, efficient data pipelines are an important aspect of data-driven business. For others, they should be seen as a strategic implementation for the future.

As companies grow, robust and sophisticated data pipelines become much more important. They can produce certain indicators in real-time or pave the way for machine learning algorithms. This practice of planning for scalability and innovation is why investing time, energy, and money into data pipelines can be a strategic move for the future of your company.

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