The evolution of data engineering tools

Data engineering, a discipline vital for managing and processing data, has undergone remarkable transformations over the years. Its roots can be traced back to the early days of computing when organizations began to face the growing volumes of data generated by their operations. However, “data engineering” is a relatively new term and was not used before 2000.

Today, we will delve into the fascinating world of data engineering and its tools, focusing on their historical evolution.

A brief history of data engineering?

Let’s start by going back to the beginnings: the emergence of electronic computers in the mid-20th century. While the term "data engineering" may not have been used at the time, the foundational principles and practices that underpin the discipline began to take shape as organizations sought ways to manage and process the increasing volumes of data they produced.

Initially, data engineering was primarily characterized by manual processes and rudimentary tools, often requiring specialized technical expertise. However, as technology advanced, so did the tools and methodologies employed in the field.

The emergence of relational databases in the 1970s marked a significant milestone, enabling more structured data storage and retrieval. This paved the way for the development of Extract, Transform, Load (ETL) processes, which became fundamental in integrating data from disparate sources.

The advent of the internet and the exponential growth of digital data in the late 20th century presented new challenges and opportunities for data engineering.

By the turn of the millennium, a large enterprise's data landscape typically consisted of several relational databases for storing operational data alongside something we call a "data warehouse." Usually, this is also a relational database optimized explicitly for historical queries and business reporting.

However, these traditional relational databases struggled to cope with the scale and variety of data generated by web applications and online transactions. So, some engineers at Google began developing alternative tools. Their work led to the emergence of two notable systems: a distributed file system called "GFS" and a large-scale data processing system called "MapReduce." Other companies could access the main concepts of these systems in published papers, and they adopted them to construct their internal Big Data platforms.

A little while later, something happened that eventually led to significant changes: Yahoo open sourced the first components that ultimately became the Hadoop ecosystem. This proved to be a real game changer as it was the first system that made Big Data processing available to the masses.

Hadoop became ready for its prime time around 2010. In addition, numerous other internet giants and research institutions decided to open-source new tools, such as HBase, Storm, Cassandra, Spark, Presto, Druid, etc. This was also when the first cloud IaaS (Infrastructure as a Service) offerings started to appear, making it relatively convenient to deploy and operate these technologies at scale.

During this time, data-driven tech companies like Facebook and Airbnb officially started using the term "data engineer."

Over the following five years, data engineers enthusiastically embraced the new tools and started using them for their work. Traditional relational databases were soon deemed inadequate and disregarded, with NoSQL databases becoming the new standard.

The age of “Big Data”

You can think of this era as the “Wild West” of data engineering. Scalability was more important than everything else: companies focused on storing as much data as possible, even if they didn’t have an immediate use for it. Remember, this was a time when they did not yet have the abundance of tools we do today, so the data engineers themselves would often have to create the solutions necessary to deal with a problem.

As more and more tools became available, it became easier to collect data. So, after 2015, an increasing number of companies found themselves confronted with massive volumes of it. They needed help managing all the data, which boosted the demand for data engineers. Soon, the discipline became commonplace in large organizations.

Unfortunately, the individuals assigned to perform data engineering tasks were not always well-versed in the intricacies of Big Data tools, resulting in frequent overspending and numerous unsuccessful projects. This led to the creation of even more data engineering tools and managed services that promised simpler, more efficient, or more cost-effective handling of Big Data.

The rise of the instant gratification culture

Let’s pause for a moment and think about our lives today. How much do we use our phones, and how much do we depend on them? And most importantly, how much patience do we have for an app that doesn’t work, a product we ordered that’s delayed, our Internet cutting off, and us not being able to check messages immediately?

Instant availability of services is the norm today. We are used to having everything at our fingertips. This phenomenon was caused by the rise of social media, which introduced us to instant messaging. It fueled our desire for near real-time updates across various domains. For companies that wanted to keep up with this demand, it became increasingly important to enable the processing of real-time - often called streaming - data. The need for streaming data tools surged, and new frameworks were created.

During this period, there was also a critical reevaluation of the design of NoSQL databases. It became apparent that some notions regarding traditional data warehouses were overly rigid. Consequently, the concept of "NewSQL" databases emerged, incorporating SQL as the query language while employing NoSQL-like storage underneath. Additionally, hybrid data systems like "lakehouses" were introduced, combining features of data lakes (such as Hadoop) and warehouses (relational databases).

In summary

Today, data engineering boasts a diverse ecosystem of tools and technologies, from data integration platforms with intuitive graphical interfaces to serverless computing environments that abstract away infrastructure management.

The evolution of data engineering reflects not only technological progress but also the increasing importance of data-driven decision-making in organizations across industries. There is a growing demand for real-time data, and new streaming frameworks keep appearing. By understanding its history and embracing newly emerging tools, aspiring data engineers can adapt to changing requirements, drive innovation, and deliver more efficient data solutions.

By combining historical knowledge with a willingness to embrace the future, data engineers can navigate the complexities of modern data ecosystems with confidence and success.

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