As businesses become more data-driven and consume a variety of technological services to optimize their operations, the amount of data they generate as a result also keeps getting bigger. New data sources that vary in size, type, and complexity get added to the infrastructure. Getting value out of such massive and diverse data requires a robust framework in place - one that facilitates fast, cutting-edge data science and analytics. That’s where data engineering comes into play.
While the data scientists get all the accolades for unlocking value from the mountains of data, it's the data engineers that lay the important groundwork by setting up the infrastructure conducive to their analysis.
In this post, we take a look at what data engineering entails and tackle the million-dollar question - how exactly do you become a data engineer?
Simply put, data engineering facilitates better flow and access to the data within the teams in your organization. It gives you the ability to collect, clean, store, and manipulate your data and make it readily available for analysis.
As mentioned earlier, most companies have multiple data sources and collect their data in various formats, such as text files, database logs, multimedia files, etc. Data engineers build and maintain the data infrastructure that allows for the collection and storage of this data. They are also responsible for building a system that cleans and transforms this data into a format that data scientists can then use to generate valuable insights. This involves creating optimal databases, defining and implementing schema changes, handling the metadata, and integrating new data management tools and systems.
Data engineering also entails some critical tasks that ensure the smooth and efficient functioning of your data pipeline. Some of these critical tasks include workflow scheduling, autoscaling to handle traffic spikes, and, most importantly, building a robust infrastructure that operates seamlessly for months or even years - with minimal upgrades and tweaking.
While the trend of data engineering is relatively new, the underlying concepts of data engineering have been in place for quite a long time, although the mindset was very different. In the classical world of DataOps, a single engineer would configure integrations and perhaps filters between data sources and let the developers do the heavy lifting.
Over the last two decades, there has been a progressive change in the overall data design philosophy, the resultant changes in tooling, and the evolution of the data engineer role in organizations. Modern data engineering is advanced operations - ensuring automated data cleanup, reconciliation, and integrating disparate sources is just where it starts.
Needless to say, to become a data engineer, you must be familiar with all the underlying concepts - from architecting systems and setting up pipelines that facilitate data collection, storage, and processing, to building data stores within the data warehouse and managing the data infrastructure.
While many data engineers are experts in computer science, the good news is that you don’t need a background or a degree in programming. Here are, however, a few things to keep in mind before you get started with your journey in data engineering:
The saying "Jack (or Jill) of all trades” applies to most cross-functional roles, and data engineering is no exception. From web application coding to Regex to network topology to data science, there is a vast range of skills that are all useful for a successful career in data engineering.
While the similarly named but quite a distinct field of Data Science has several related university degrees and dozens of Bootcamp programs available, data engineering is mostly an on-the-job, hands-on experience. While some cloud certification programs for data engineers have cropped up of late, nothing beats having practical experience of building your data pipeline from scratch or managing a data infrastructure and troubleshooting the problems.
From IT to frontend coding, most data engineers started doing something else in tech. This is not to discourage anyone from pursuing data engineering as a role, but you may want to start in something that has more resources available for an absolute beginner. There are several courses in Machine Learning or Data Science that are readily available, with more entry-level job roles to take that first step.
The best place to start out in data engineering is doing tech support for your customers. This support allows you to interact with most customer data sources as well as understand the way the product is used in the real world. This is a great way to get started as a Data Engineer.
Now that we’ve gained some clarity about what data engineering entails, here are some essential technical skills you need to master to become a data engineer:
Data Engineering is considered to be the intersection of data science and software engineering. To be a data engineer, you need to be proficient in both, and being an expert in programming is the first step. A thorough understanding of data structures and algorithms, followed by their application, the fastest-growing, goes a long way in mastering any programming language.
The world of data engineers revolves around databases and data warehouses, and SQL is their best friend. You need to have a firm understanding of SQL to do everything remotely related to databases - from simple database queries and defining schemas to implementing data models and performing database normalization. Understanding the traditional database administration skills, such as database design, data backup, recovery, etc., comes in quite handy.
As companies these days work with unstructured and semi-structured data, knowledge of NoSQL databases like MongoDB, Redis, etc., are also important.
ETL (Extract, Transform, Load) is a process mainly used in the context of data warehousing, which allows you to gather data from multiple, disparate sources and consolidate it into a single, centralized repository - your data warehouse.
With ETL, you can transform vast amounts of data into actionable business insights by giving a unified view of your data while providing relevant historical context to it. You can leverage modern data processing tools like Apache Spark to load terabytes and even petabytes of data - in batches or streams - and process it effortlessly in no time.
Building a successful, efficient ETL and data processing strategy goes a long way in determining the value you can get out of your data, so mastering these skills is a significant stepping stone in becoming a successful data engineer.
As a data engineer, you will work with many repetitive and often tedious tasks. A full database table cleanup once - every three hours - doesn’t sound too exciting. Automating such tasks can save you a lot of time and effort.
When it comes to data processing, you will sometimes have to build and schedule jobs that run in a given time interval. As your data processing jobs start adding up within your workflow and a dependency begins to build on other jobs, workflow scheduling and job orchestration tools such as Apache Airflow comes in very handy. These tools allow you to perform parallel processing using the popular tools within the Big Data ecosystem.
Automation and workflow scheduling plays a crucial role in almost all modern data infrastructures, so having a working knowledge of them is very important.
Almost all companies today have their data infrastructure set up on a cloud. Data engineers connect their data systems to various cloud-based sources or deal with several data points stored in a cloud-native data warehouse. Cloud storage and processing are relatively cheaper than the on-premise alternatives, and you don’t have to worry about the aspects related to infrastructure maintenance and availability.
Therefore, having a working knowledge of building and managing your data pipelines that ensure high-quality data processes and workflows is essential.
While the skills required to become a data engineer can be quite overwhelming, it’s scarce to find a data engineer whose day-to-day job consists of utilizing the entire spectrum of data engineering skills. Their responsibilities vary largely, depending on the size and nature of the company. The one-size-fits-all concept no longer exists in modern organizations. Each company has its own specific requirements and use-cases and thus requires its data engineers to master specific skills related to those requirements.
Thanks to the recent data boom, there is a huge demand for data engineers, and it is one of the fastest-growing occupations in the tech industry. Needless to say, the data engineer role is quite a rewarding career choice if you’re willing to pursue it.