Data Engineering – From Zero to Hero

My colleague An previously published a chart documenting his journey from Data Science to Data Engineering at statworx. His post showed which skills data engineers require for their daily work. If you are unfamiliar with data engineering, it is a field that revolves around storing, processing, and transferring data in a secure and efficient way.

In this post, I will discuss these skill requirements more in depth. Since there are quite a few topics to learn about, I propose the following order:

1. A programming language
2. The basics of git & version control
3. The UNIX command line
4. REST APIs & networking basics
5. Database systems
6. Containerization
7. The cloud

While this may differ from your personal learning experience, I have found this order to be more manageable for beginners. If you’re interested in a brief rundown of key data engineering technologies, you may also appreciate this post by my colleague Andre on this topic.

Learning how to program – which languages do I need?

As in other data related roles, coding is a mandatory skill for data engineers. Besides SQL, data engineers use other programming languages to solve their problems. There are many programming languages that can be used in data engineering, but Python is certainly one of the best options. It has become the lingua franca in data driven jobs, and it’s perfect for executing ETL jobs and writing data pipelines. Not only is the language relatively easy to learn and syntactically elegant, but it also provides integration with tools and frameworks that are critical in data engineering, such as Apache Airflow, Apache Spark, REST APIs and relational database systems like PostgresSQL.

Alongside the programming language, you will probably end up choosing an IDE (Integrated Development Environment). Popular choices for Python are PyCharm and VSCode. Regardless of your choice, your IDE probably will introduce you to the basics of version control, as most IDEs have a graphical interface to use git & version control. Once you are comfortable with the basics, you can learn more about git and version control.

git & version control tools – tracking source code

In an agile team several data engineers typically work on a project. It is therefore important to ensure that all changes to data pipelines and other parts of the code base can be tracked, reviewed, and integrated. This usually means versioning source code in a remote source code management system such as GitHub and ensuring all changes are fully tested prior to production deployments.

I strongly recommend that you learn git on the command line to utilize its full power. Although most IDEs provide interfaces to git, certain features may not be fully available. Furthermore, learning git on the command line provides a good entry point to learn more about shell commands.

The UNIX command line – a fundamental skill

Many of the jobs that run in the cloud or on-premises servers and other frameworks are executed using shell commands and scripts. In these situations, there are no graphical user interfaces, which is why data engineers must be familiar with the command line to edit files, run commands, and navigate the system. Whether its bash, zsh or another shell, being able to write scripts to automate tasks without needing to use a programming language such as Python can be unavoidable, especially on deployment servers. Since command line programs are used in so many different scenarios, they also apply to REST APIs and database systems.

REST APIs & Networks – how services talk to each other

Modern applications are usually not designed as monoliths. Instead, functionalities are often contained in separate modules that run as microservices. This makes the overall architecture more flexible, and the design may evolve more easily, without requiring developers to pull out code from a large application.

How, then, are such modules able to talk to each other? The answer lies in representational State transfer (REST) over a network. The most common protocol, HTTP, is used by services to send and receive data. Learning the basics of how HTTP requests are structured, which HTTP verbs are typically used to accomplish tasks and how to practically implement such functionalities in the programming language of your choice is crucial. Python offers frameworks such as fastAPI and Flask. Check out this article for a concrete example of how to build a REST API with Flask.

Networks also play a key role here since they enable isolation of important systems like databases and REST APIs. Configuring networks can sometimes be necessary, which is why you should know the basics. Once you are familiar with REST APIs, it makes sense to move on to database systems because REST APIs often don’t store data themselves, but instead function as standardized interfaces to access data from a database.

Database systems – organizing data

As a data engineer, you will spend a considerable amount of time operating databases, either to collect, store, transfer, clean, or just consult data. Hence, data engineers must have a good knowledge of database management. This entails being fluent with SQL (Structured Query Language), the basic language to interact with databases, and having expertise with some of the most popular SQL dialects, including MySQL, SQL Server, and PostgreSQL. In addition to relational databases, data engineers need to be familiar with NoSQL (“Not only SQL”) databases, which are rapidly becoming the go-to systems for Big Data and real-time applications. Therefore, although the number of NoSQL engines is on the rise, data engineers should at least understand the difference between NoSQL database types and the use cases for each of them. With databases and REST APIs under your belt, you will need to deploy them somehow. Enter containers.

Containerization – packaging your software

Containerization is the packaging of software code with the operating system (OS) libraries and dependencies required to run the code to create a single lightweight executable—called a container—that runs consistently on any infrastructure. More portable and resource-efficient than virtual machines (VMs), containers have become the de facto compute units of modern cloud-native applications. To get better understanding of how containers make AI solutions scalable, read our whitepaper on containers.

In order to containerize applications, most engineers use docker, an open-source tool for building images and running containers. Packaging code almost always involves command line tools, such as the docker command line interface. But not only applications or REST APIs may be containerized. Data Engineers frequently run data processing tasks in containers to stabilize the runtime environment. Such tasks must be ordered and scheduled, which is where orchestration tools come in.

Orchestration – automating data processing

One of the main roles of data engineers is to create data pipelines with ETL technologies and orchestration frameworks. In this section, we could list many technologies, since the number of frameworks is ever increasing.

Data engineers should know or be comfortable with some of the best known – such as Apache Airflow, a popular orchestration framework for planning, generating, and tracking data pipelines.

Maintaining an instance of such an orchestration framework yourself can be quite cumbersome. As the technology stack grows, maintenance often becomes a full-time job. To alleviate this burden, cloud providers offer readymade solutions.

The cloud – into production without too much maintenance

Among the many cloud providers, it makes sense to pick one of the big three: Amazon Web Services (AWS), Google Cloud Platform (GCP) and Microsoft Azure. All of them offer different services to solve standard data engineering tasks such as ingesting data, scheduling and orchestrating data processing jobs, securely storing data and making it available to business users and data scientists. Due to the plethora of offerings, it makes sense for data engineers to familiarize themselves with the pricing when choosing a solution.

If you have a good understanding of, say, database systems then grasping database systems in the cloud should not be too difficult. However, certain technologies such as Apache Spark on Databricks may be difficult to explore without access to the cloud. In this case, I would recommend setting up an account on the cloud platform of your choice and start experimenting.

High effort, high reward

Let us recap: to become a data engineer, you will need to learn:

1. A programming language
2. The basics of git & version control
3. The UNIX command line
4. REST APIs & networking basics
5. Database systems
6. Containerization
7. The cloud

While this seems like a lot to learn, I would urge you not to be discouraged. Virtually all of the skills listed above are transferrable to other roles, so learning them will help you almost regardless of your exact career trajectory. If you have a data science background like me, some of these topics will already be familiar to you. Personally, I find the networking to be the most challenging to grasp, as it is often handled by IT professionals on the client side.

You are probably wondering how to get started in practice. Working on individual projects will help you learn the basics of most of these steps. Common data engineering projects include setting up database systems and orchestrating jobs to regularly update the database. There are many publicly available datasets on kaggle and APIs, such as the coinbase API, from which to pull data for your personal project. You may complete the first steps locally and eventually migrate your project to the cloud.