Future of Data Engineering & How It Is Being Reshaped By Modern Data

Trends Which are Affecting the Future of Data Engineers 

In this article, I'll discuss the significant trends in today's data environment and how they effect the data engineering profession. It's important to note that the purpose here is to go broad and shallow, covering a lot of trends at a high level before delving deeper into each trend in separate blog entries.

The following are the trends I'd want to cover:

• Data infrastructure as a service
• Data integration services
• Mountains of Templated SQL and YAML
• ELT > ETL
• The rise of the analytics engineer
• Data literacy and specialization on the rise
• Computation Frameworks
• Accessibility
• Democratization of the analytics process
• Erosion of the semantic layer in BI
• Decentralized governance
• Every product is becoming a data product

Data Discussion at a Higher Level

There will undoubtedly be changes in data engineering solutions "on the ground," but one development that will have a huge impact on enterprises over the next ten years is the growing importance and duty of data executives.

Many data-specific professions were previously divided into departments, such as Head of Analytics, Head of Data Science, Head of Data Engineering, and so on. However, data-specific roles are increasingly making their way into the C-suite and into the boardroom. The phrase Chief Data Officer has gained popularity in the recent five years. There are almost 10,000 hits when you search for the phrase on LinkedIn (with many more if we include variations).

Organizations are shaped by leadership and the decisions made by leadership. As we see more data roles in the boardroom, the data function (and data itself) will become a first-class citizen and a vital consideration in every decision-making; whereas it was once considered important but not critical, it is now considered a required business function for modern enterprises.

This will shape organizations around data if it comes to fruition. With the purpose of exploiting data as a key competitive advantage, data engineering services and related functions will be strategically positioned to speed everything that happens at the organization (rather than fulfilling requests from internal customers).

Data/ML engineering roles will supplant data science roles as the most sought-after employees as the distinctions between data science and data infrastructure blur.

"We Don't Need Data Scientists, We Need Data Engineers," a recent blog post and Hacker News conversation, reaffirms that data engineering is popular again. No one can deny the power of data science when used properly, but corporations are learning that solving more fundamental concerns around data collection, cleansing, storage, and analysis — before they can do anything with the data — is a higher priority.

Data science-heavy machine learning (ML) and a tight alignment between data science and engineering may be required by advanced companies. However, when you take a step back, the reality is that many firms and business applications just require basic machine learning, not complex neural networks. Basic machine learning skills can be picked up by developers and engineers, and we're already seeing this shift with the rise of "ML Engineering" roles, which require individuals to know how to design ML algorithms, train them on real data, and deploy them in production.

The blurring of the barriers between data infrastructure and data science is the underlying technological force driving this growth in Data Engineering / Data Science professions. Because of the hygiene requirements outlined above, data scientists have traditionally worked in data engineering, but we are increasingly seeing process and technology on the infrastructure side produce data science and machine learning products.

Every team has dedicated Data Engineering Assistance

Data and data functions will become first-class citizens, as previously stated. We've already seen the beginnings of centralized data engineering solutions in the organizational and day-to-day operations of enterprises. They offer data goods and services to other departments inside the company.

The most advanced firms, on the other hand, go beyond the concept of a shared service center and create dedicated resources for individual teams ahead of time. This elevates the term "data-driven" to new heights. Rather than leveraging data to speed up and impact existing tasks, teams at firms like Matter most are collaborating with data engineers to develop and rebuild initiatives, tactics, systems, and processes.

Teams are collaborating with data engineering to question, "How can our data and data systems affect the way we think about addressing this problem?" instead of "How can we use data to make this better."

This strategic collaboration will become the norm in business operations and organizational structure during the next ten years.

An Increase in the number of "Unicorns" working on Data issues

The software industry will rise to the occasion as the megatrend gains traction (as it always has). Companies like Databricks and Snowflake, which have developed multibillion-dollar businesses solving hard challenges in data processing and storage, come to mind when we think about unicorns.

We've seen the first crop of unicorns who were early movers in the sector in the last five years, as the fledgling data engineering services has started to become mainstream. Segment (bought by Twilio for $3.2 billion) and FiveTran (valued at $1 billion and increasing) are two companies that have established massive businesses around data collection. DBT and Looker (through LookML) are two more startups that have created major businesses around data processing.

In response to the enormous demand generated by all of the previous arguments in this essay, the number of companies in the data area will only increase over the next ten years.

Moving Data Technology will become commoditized

Moving data is still a non-trivial problem for many firms today, which is why the companies described above were able to develop multi-billion-dollar businesses. Despite this, they only had a ten percent market share!

Many firms nowadays must make some kind of sacrifice in order to create pipelines, whether it's eating expenses internally if they build themselves or navigating the process of vendor selection, new technologies, and so on. Standard playbooks, tools, and architectures for developing and linking data pipelines within a corporation will exist in ten years, if not sooner.

Cost and competition, in addition to mass adoption, will foster commoditization. To begin with, the hard cost of transporting data is falling. Second, more businesses are transitioning to a "owned data" infrastructure in order to reduce the number of data silos and lower the expense of storing copies of their data among various vendors.

Because of the competition, corporations will be able to charge a lower premium simply for solving a difficult problem. Because transporting data from X to Y was difficult and there were few options before, firms were willing to pay more to alleviate the problem. However, when more options become available, those solutions will become more cost-effective.

Businesses will have the luxury of fixing acute pain points as a routine part of architecting their data pipelines and data stack, rather than purchasing point solutions.

Infrastructure that is real-time (or near real-time) will become the norm

Even though there are a lot of suppliers in the Customer Data Platform and Customer Data Pipeline area, only a few of them provide real-time use cases out of the box.

Customer Data Platforms thrive at things like customer profiles and customer journey activity, but not so much at the data pipelines that bring the data into the system. Customer Data Pipelines, on the other hand, are a difficult to develop and sell at scale technology (remember the billion-dollar enterprises mentioned above that have only achieved 10 percent market penetration).

Because real-time pipelines are still in their infancy, many businesses develop and manage their own systems, which takes a lot of time and work.

Real-time pipelines will be used by most firms in the next ten years, as more companies enter the consumer data infrastructure market and develop their products on current cloud technology. Difficult difficulties like real-time personalization will become turnkey.

Data Engineers and businesses that value data have a bright future ahead of them

Both data engineers and the firms that employ them will benefit from these trends. Data engineers will be able to spend more time contributing strategic value rather than attempting to make the data and infrastructure function as data becomes more valuable inside firms and technology makes what were formerly difficult challenges simple to address.

The same is true for businesses: resources that were previously utilized to establish and maintain consumer data infrastructure will be redirected to the development of better products and services.