It has been approximately 20 years since humanity’s output of digital data overtook analog data. Since then, the field of data engineering has changed so dramatically that it’s hard to believe we’re only on the cusp of a truly data-driven future.
As firmly entrenched as we are in the Information Age, we’re still in the early days of figuring out what to do with all the data we’re producing. Data engineers are indispensable to that process.
We’ll start with a brief history of data, followed by a quick rundown of what data engineering is, how it fits into the data ecosystem, and – most importantly – whether data engineering is right for you.
As such, this is a good article for anyone interested in data, junior data engineers, or data professionals curious about data engineering.
Let’s dive right in!
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You might think of data as a relatively modern phenomenon, but it’s actually been around for a long time. Data, and the need to understand it, is as old as human civilization itself. No matter how advanced we believe ourselves to be, much of the data we generate leads back to genuine human concerns, like what food we decide to eat, clothes we wear, or news to share. In other words, data isn’t just a bunch of numbers— it’s vital information used to make decisions, tell stories, and drive change.
In today’s world, data engineers are responsible for making it all work.
Even in ancient societies, data was essential to the functioning of society— they needed ways to keep track of trade goods, tax rates, and crop yields.
Some fantastic early examples of recorded data dating back to at least 3,100 BCE are Sumerian cuneiform clay tablets used to record and store economic information. Clay tablets contained valuable data, documenting information such as the distribution and deliveries of grains like barley or wheat.
Another comes from Ancient Babylon. The complaint tablet to Ea-Nasir dates back to around 1750 BCE and is thought to be the oldest known customer complaint. The customer, in this case, was unhappy with the quality of copper ingots they had received and took their grievance directly to the source.
If you compare how long analog data has been around to digital data, you’ll see that it’s still in its infancy. Big data is ubiquitous and will only become more so as we move further into the 21st century.
This is where data engineering comes in.
Bill Inmon defined data engineering as “the construction of a system that converts data into information” in his 1993 textbook, “Building the Data Warehouse.” Inmon’s definition of data engineering is still pretty accurate today. However, the field has evolved drastically since then.
Data engineering really only started coming into its own in the late 20th century, with the rise of big data and distributed data architectures.
Big data is a term that refers to the massive, ever-growing volume of data that organizations are generating.
This data comes from a variety of sources, including:
Organizations need to be able to store, process, and analyze this data to extract valuable insights that are used to make better decisions, improve operations, and drive growth.
In the early days of data engineering, the focus was on building data warehouses — large, centralized repositories for storing data that could be used for reporting and analysis. This represented a big shift from the traditional way of storing data in isolated silos and opened up new possibilities for data analysis.
However, the centralized data warehouse model had its limitations. For one, data warehouses were expensive to build and maintain. They were also difficult to scale, and they often became data silos in their own right. The centralized data warehouse was simply not designed to handle the sheer amount of data people were generating.
Another limitation of data warehouses was that they were designed to support reporting and analysis but not real-time decision-making, which would give businesses a significant edge over their competition.
To address these limitations, a new approach to data engineering was needed to enable companies to process and analyze big data in real-time. The centralized data warehouse model eventually gave way to the distributed data architecture of today, where data is stored in multiple, distributed locations.
Note: Another major advancement for data architecture was the introduction of the cloud.
A distributed data architecture has many advantages over the centralized data warehouse model. For one, it’s more scalable and easier to maintain. It’s also more flexible, as data can be stored in multiple formats and accessed by different users simultaneously. In addition, a distributed data architecture is more resilient to failure, as data can be stored and accessed from multiple locations.
While the benefits of a distributed data architecture are many, it does come with its own set of challenges.
For example, data can be lost if a server goes down or there is a network outage. In addition, data can be corrupted if it’s not properly managed. Finally, data can be misinterpreted if it’s not properly processed and analyzed.
The rise of big data only exacerbated these challenges, as businesses began to generate and collect more data than they could process and store. This created a new set of challenges for data engineers, who now had to design and build systems that could handle the volume, velocity, and variety of big data.
Modern data engineering teams are turning to the cloud to overcome these challenges.
Cloud-based software architectures are even more scalable, reliable, and secure than traditional on-premise data architectures. And because the cloud is designed for distributed computing, it’s the perfect platform for modern data engineering. To manage this new, distributed data architecture, a new variety of data engineers was needed— one with the skills to design, build, and maintain increasingly complex data systems.
Fortunately, many cloud-based data management platforms now make it easy to collect, process, and analyze data at scale. These platforms are designed to handle big data, and they’re becoming increasingly popular with data engineering teams.
Furthermore, data engineering has evolved to encompass a broader range of activities, from data cleansing and modeling to data mining and visualization. And as data engineering teams continue to grow, they will only become more essential to the success of modern businesses.
The future of data engineering is cloud-based, real-time, and automated. Contrary to the popular association of automation with job cuts, data engineering is not going away anytime soon. The technologies and tools that data engineers use may change, but as long as new types of data are generated, we will always need people to interpret and manage it.
Data engineering will continue to be essential as our data architectures become more complex. Remember, we’re still in the infancy of the digital age, and there is still so much untapped potential for data engineering to grow and evolve.
So, if you’re interested in a career in data engineering, there’s never been a better time to get started. Data engineering skills are in high demand thanks to major FAANG companies like Google and Amazon that have invested heavily in providing services like Google Cloud and AWS.
But before you get started, it’s important to understand what data engineering is and whether or not it’s the right field for you.
Data engineering is a funky hybrid field that sits at the intersection of data science and software engineering. It’s a field concerned with the end-to-end management of data, from its initial collection to its eventual analysis and decision-making.
The data engineer’s role is to ensure that the data is in the right format, cleansed of any errors or inconsistencies, and in a format that is easy to use, readily available, and secure. A data engineer is also responsible for designing and building the systems that house this data and maintaining these systems as they grow and change over time.
On any given day, a data engineer might be responsible for any number of tasks, including:
As you can see, data engineers have a wide range of responsibilities. They need to have a strong technical background and be able to write code, but they also need to communicate effectively with non-technical stakeholders.
Being a data engineer can be rewarding and challenging, even if it’s not as glamorous as data science. If you’re interested in working with data but are unsure if data engineering is the right fit for you, here are a few questions to ask yourself:
If you answered “yes” to all of these questions, then data engineering might be the right field for you!
Now that you know a little bit more about what data engineering is and whether or not it might be the right field for you, let’s take a look at what data engineers actually do.
To understand data engineering, it’s important first to understand the ecosystem in which it operates. Data engineering exists within the broader field of data science, which is concerned with extracting insights and knowledge from data to create predictive models and decision-making tools.
Data Engineers collect data from different multiple data sources, clean it, process it, and store it in data repositories for end-users.
Data analysts, data scientists, and business intelligence analysts can then use this data to build predictive models, machine learning models, run analyses, and generate reports. These models and reports can be used to decide everything from marketing campaigns to product development or to get insight into how satisfied your customers are.
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So, what comes next? Now that you’ve learned a little about data engineering and what it takes to be a successful data engineer, you can begin planning your career in this area. Data engineering is a promising field with many opportunities, but it’s not easy to break into - make sure you do your homework before applying for jobs in this field!
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