Why You Should Learn Data Engineering
Exciting news: we just launched a totally revamped Data Engineering path that offers from-scratch training for anyone who wants to become a data engineer or learn some data engineering skills.
Looks cool, right? But it begs the question: why learn data engineering in the first place?
Typically, data science teams are comprised of data analysts, data scientists, and data engineers. In a previous post, we’ve talked about the differences between these roles, but here let’s dive deeper into some of the advantages of being a data engineer.
Data engineers are the people who connect all the pieces of the data ecosystem within a company or institution. They accomplish this by doing things like:
- Accessing, collecting, auditing, and cleaning data from applications and systems into a usable state
- Creating and maintaining efficient databases
- Building data pipelines
- Monitoring and managing all the data systems (scalability, security, etc)
- Implementing data scientists’ output in a scalable manner
Doing everything listed above primarily requires one particular skill: programming. Data engineers are software engineers who specialize in data and data technologies.
That makes them quite different from data scientists, who certainly have programming skills, but who typically aren’t engineers. It’s not uncommon for data scientists to hand over their work (e.g., a recommendation system) to data engineers for actual implementation.
And while it’s data analysts and data scientists who are doing the analysis, it’s typically data engineers who are building the data pipelines and other systems necessary to make sure that everyone has easy access to the data they need (and that no one has access to the data who shouldn’t).
A strong foundation in software engineering and programming equips data engineers to build the tools data teams and their companies need to succeed. Or, as Jeff Magnusson put it: “I like to think of it in terms of Lego blocks. Engineers design new Lego blocks that data scientists assemble in creative ways to create new data science.”
This brings us to the first reason why you might want to become a data engineer:
1. Why Learn Data Engineering? It’s the Backbone of Data Science
Data engineers are on the front lines of data strategy so that others don’t need to be. They are the first people to tackle the influx of structured and unstructured data that enters a company’s systems. They are the foundation of any data strategy. Without Lego blocks, after all, you can’t build a Lego castle.
In the above Data Science Hierarchy of Needs (proposed by Monica Rogati), data engineers are completely responsible for the two bottom rows, and share responsibility with data analysts and data scientists for the third row from the bottom.
To gain a better understanding of how critical data engineering is, imagine the pyramid pictured above is used as a funnel and flipped upside down. Data is poured into the top of that funnel, and the first people to touch it are data engineers. The more efficient they are at filtering, cleaning, and directing that data, the more efficient everything else can be as the data flows further down the funnel and towards other team members.
Conversely, if the data engineers are not efficient, they can serve as a block in the funnel that harms the work of everyone downstream. If, for example, a poorly-built data pipeline ends up feeding the data science team incomplete data, any analysis they perform on that data may be useless.
In this way, data engineers act as multipliers of the outcomes of a data strategy. They are the giants on whose shoulders data analysts and data scientists stand.
“A common starting point is 2-3 data engineers for every data scientist. For some organizations with more complex data engineering requirements, this can be 4-5 data engineers per data scientist.”
2. It’s Technically Challenging
One of the Python functions data analysts and scientists use the most is
read_csv — from the pandas library. This function reads tabular data stored in a text file into Python, so that it can be explored and manipulated.
If you’ve worked with data in Python before, you’re probably very used to typing something like this:
import pandas as pd df = pd.read_csv("a_text_file.csv")
Easy and convenient, right? The
read_csv function is a great example of the essence of software engineering: creating abstract, broad, efficient, and scalable solutions.
What does that mean and how does it relate to learning data engineering? Let’s take a deeper look.
- Abstract. When reading a file in a computer, a very complex process occurs under the hood. However, our use of the function is very simple, what goes on in the background is abstracted away from the usage. You don’t need to understand what
read_csvis doing “under the hood” to use it effectively.
- Broad. This function also allows us to explicitly choose what delimiter is being used in the text’s file tabular data (e.g. commas, semicolons, tabs, and so on). This makes it easy to use with a variety of CSV styles, and that’s music to data scientists’ ears. And there are many other options that allow data practitioners to focus on their goals instead of having to worry about programming details.
read_csvworks quickly and efficiently, and it’s also efficient to read in code.
- Scalable. Another option included with this function allows us to read files by chunks, so that if a file is too large to read into the computer’s RAM, it can be read chunk by chunk, allowing users to process files as large as they come.
Robert A. Heinlein is said to have said that “One man’s magic is another man’s engineering”. It is data engineers who work that magic, building tools like the
read_csv function that are abstract, broad, efficient, and scalable so that the rest of the team can focus on the data itself and their analysis rather than having to struggle with programming puzzles.
(At the same time, data engineering probably requires less math than data science, so if you prefer programming to mathematics, data engineering could be an ideal option!)
3. It’s Rewarding
Making data scientists’ lives easier isn’t the only thing that motivates data engineers. There’s no denying that data engineers are making a significant and growing impact on the world at large.
“Every day, we create 2.5 quintillion bytes of data,” IBM states in its article 10 Key Marketing Trends for 2017 and Ideas for Exceeding Customer Expectations.
“To put that into perspective, 90 percent of the data in the world today has been created in the last two years alone – and with new devices, sensors and technologies emerging, the data growth rate will likely accelerate even more.” [Emphasis added.]
The immensity of today’s data has made data engineers more important than ever. Business Insider reports that there will be more than 64 billion IoT devices by 2025, up from about 10 billion in 2018, and 9 billion in 2017″. With this growth comes a lot more data from a lot more sources, and thus a lot more need for engineers who can effectively process and channel it.
This means that data engineers have a huge variety of ways they can pursue their interests and deepen their skill sets. To give you an idea of how vast this world is, here’s a list of popular data tools and technologies: Amazon Redshift, Amazon S3, Apache Cassandra, Apache HBase, Apache Kafka, Apache Spark, Apache Zookeeper, Azure, ElephantDB, Hadoop Distributed File System, IBM DB2, MapReduce, Memcached, Microsoft SQL Server, Mongo DB, Oracle Database, PostgreSQL, Redis, SQLite, Storm, SAP IQ, Teradata and Vertica.
Of course, a data engineer doesn’t have to know all of these, but this list illustrates just how much there is to do in the world of data engineering. Once you’ve got the skills to get jobs, you’ve got a lot of freedom to choose what you’re working on and what tools you’re working with.
Since data engineers have both data and software engineering skills, they’re also capable of building a variety of products. Want to contribute to an early-stage startup, or become an entrepreneur and found your own someday? Data engineering skills give you the tools you’ll need to both build great products and analyze how those products are performing. You’ll be able to implement and measure the success of pretty much anything you can think of.
Want to work remotely? According to 2019’s Future Workforce Report, “2 out of 5 full time employees will be working remotely in the next three years”. So if working outside of the office is something that suits you, data engineering could help you achieve that goal. Because there’s a high demand for data engineers, and because most of the work can be done remotely, it’s definitely possible to find remote data engineering jobs, or work for yourself as a freelance contractor on shorter-term data engineering projects.
Finally, data engineers also have plenty of opportunities to give back the community. According to 2019’s Stack Overflow Developer’s Survey, “About 65% of professional developers on Stack Overflow contribute to open source projects once a year or more”. And since you’ll have data and engineering skills, you’ll be able to make a real difference developing cool new tools for the data science community.
4. It Pays Well
You should never take a job based only on the salary, but there’s no denying that salary is important!
According to IBM’s The Quant Crunch: How the Demand for Data Science Skills is Disrupting the Job Market, “Jobs specifying machine learning skills pay an average of $114,000. Advertised data scientist jobs pay an average of \$105,000 and advertised data engineering jobs pay an average of $117,000.” [Emphasis added.]
It’s no surprise as to why. Data engineering skills like Python, SQL, and the shell regularly rank among the highest-paying skills in StackOverflow’s developer surveys. And at the time of this writing, there are around 70,000 results for the search term Data Scientist on LinkedIn, and around 112,500 results for the search term Data Engineer. On GlassDoor the difference is even more pronounced: around 22,500 for data scientists versus around 77,100 for data engineers (filtered for jobs posted in the last month).
Not only there is a large demand for data engineers, that demand keeps increasing! As of June of 2019, the demand for data engineers had increased 88% year over year (source).
And that’s not all! According to Statista, “The global big data market is forecasted to grow to 103 billion U.S. dollars by 2027, more than double its expected market size in” 2019.
5. It’s Valuable Even If You Don’t Want to Be a Data Engineer
Even if you don’t want to pursue a career as a data engineer, if you want to work in data science, it can be very useful to have some knowledge of data engineering. The benefits are multifold:
- As a data practitioner, there’s a good chance you’ll periodically be asked to complete tasks that have some overlap with other job roles, including data engineering.
- Learning a different way of looking at things can be beneficial to your understanding, and it gives you an opportunity to brush up on skills you might not have used in a while.
- Having engineering skills will make you more self-sufficient. This can help your career tremendously as you need not be blocked anymore, waiting for someone to do something for you.
- Learning data engineering skills will allow you to empathize with data engineers and better communicate with them. This will also help your team, as you can become the bridge that connects yours to the data engineering team.
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Bruno is currently a content author and Data Scientist at Dataquest teaching data science to thousands of students.