Why data engineering is crucial to business development

There now exists a significant opportunity for organisations to radically improve the way they run their businesses and interact with their users. From real-time analytics to machine learning and AI, whether it's making predictions about the future or automating work that previously required costly human resources, modern data technologies allow companies to make intelligent use of their data.

Realising this potential, however, is not without its pitfalls. Many organisations have begun their data journeys only to see their efforts faltering and struggling to realise real business impact. Promising proofs of concept and prototypes often fail to develop into real solutions that integrate with business systems. So, what can we do to maximise our chances of success in this area? One of the critical tools in building solid foundations for data solutions is the area of data engineering. This helps us turn our data ideas into real-world solutions.

What is data engineering?

Data systems, fundamentally, are software systems. This means that, in order to build solid, reliable and extensible data solutions, we should draw on well-established software engineering practices to provide a solid foundation for our data systems. This is something that is too often missing in data projects. Software engineering practices are, quite reasonably, not the main focus of prototypes and proofs of concept (such projects are often staffed with business analysts and data scientists whose area of expertise has a different focus). However, when we want to promote a successful prototype to production, we need engineering skills to turn these into production-worthy systems.

There are many important software engineering practices out there. These practices are becoming commonplace in software projects but are often missed out in data projects. This may be because some participants in data projects have a background in data warehousing and analytics, where such practices are less common. Our aim is to ensure that best practices are shared and adopted within our data projects.

Software Engineering practices: Testing

These days, software developers are used to writing unit tests for their code. These tests ensure that every part of a codebase works as intended and provides a safety net that makes it easier to change code without accidentally introducing bugs. Higher-level tests are performed at the component and system level, while integration tests ensure the code works correctly when interacting with external entities. This allows developers to move quickly with confidence.

For all the same reasons that having a suite of automated tests is hugely valuable for any software application, we need to do the same for all artefacts of a data project. This means having tests for data schemas, data transforms, SQL queries and data algorithms. In fact, we should view every configuration artefact that’s part of a project as code, and test and deploy it with the same care as any other code.

Automating the system lifecycle

A data system consists of many parts, ranging from cloud infrastructures, such as storage buckets and databases, to code that runs and processes data. To build these systems, we don’t just write code, we also need to deploy it and provision the infrastructure on which it runs.

Too many systems rely on manual steps in order to build and deploy parts of it, or to perform maintenance like data updates and bug fixes. The result can be brittle systems where there’s a lot of scope for human error. It’s also slow and costly to maintain such systems – and this cost can grow as the system scales up to get bigger and more complex. To avoid such problems, we should aim to automate every aspect of the system lifecycle: building, deploying, provisioning, and maintaining all parts of the system.

Cloud platforms make this goal significantly more achievable. Every piece of infrastructure can be configured via code so that no manual steps are necessary. This means that, ideally, we should be able to recreate a whole system from scratch just by running code.

Continuous deployment

Agile practices have become commonplace in software projects, where the value of working in small iterations and shipping incremental code changes continuously has been demonstrated time and time again. Releasing code to production frequently, sometimes several times a day, means you get rapid feedback on your work. This means you catch problems sooner, users can review changes quicker, and you avoid the integration problems you often get when you have different strands of work happening in parallel over a long period of time (this applies just as much to data systems). It’s also worth noting that, to be able to deploy continuously, you need to build on the practices of having automated tests and deployment – it’s just not feasible to do this in a manual way.

Data engineering challenges

So, is data engineering just software engineering under a different name? Well, not quite! There are a number of challenges specific to data engineering that are important to address, which is why having specialist data engineers on your team is so valuable. We’ll highlight a couple of examples here…

Data architectures and technologies

The world of big data, machine learning and AI has moved at an incredible pace since these areas started becoming popular over the last couple of decades. It seems new technologies appear daily and it can sometimes feel like a full-time job trying to keep on top of them all! Data engineers can help you to navigate this vast ecosystem and pick the most relevant technologies for any given project.

Furthermore, data engineers can define an architecture that puts all the pieces together as part of a coherent whole. A well-designed architecture provides a solid foundation for your data project while allowing you to evolve over time to use new technologies that help you deliver to your requirements. Architecture should be designed, from the start, to take into account security and privacy, data regulations, scalability and performance, while not being over-engineered for your current requirements. In short, good architecture should be as simple as possible, while helping your organisation to handle more complexity over time.

Working with large amounts of data

A crucial part of building production-ready data systems is to ensure that solutions work reliably with very large amounts of data. While data analysts and data scientists often have a deep understanding of the business problem at hand, performance and reliability may not be their focus when coming up with algorithms and queries. Data engineers know how to store huge amounts of data so that it can be accessed quickly and efficiently. They also know how to run computations that don’t fit on a single machine, using clusters of tens or even hundreds of computers.

An important part of data engineering is building systems that detect and recover gracefully from a failure without data loss (failure is an inevitable part of running large systems – the more data you have and the more processing you do, the more likely it is that something will fail). This includes implementing the infrastructure for monitoring the status of the data system and the tools and processes that allow errors to be safely and quickly resolved.

Huge potential

Intelligent use of data can offer a huge boost to businesses and carries with it huge potential. But, in order to deliver on this potential, it’s essential to build well-engineered systems that reliably produce correct data, comply with regulations, and that can evolve and grow as business requirements change. At eSynergy, we believe data engineering is a crucial part of this, and that – by applying both well-established software engineering practices and using specialist skills and techniques specific to data systems – we can build systems that deliver on this promise in ways that are both cost-effective and that provide solid foundations for future data needs.

Do you wan to innovate with confidence by taking control of your data and technology?