5 Mistakes New Data Engineers Make – Data Engineering Consulting
Photo by CHUTTERSNAP on Unsplash
When it comes to best practices and business alignment, most new data engineers learn as they go on the job. From building overly complex and unsustainable systems to putting too much faith in existing data structures, here are five of the most common mistakes and traps that even the most skilled and talented new data engineers can fall into, and what you can do to avoid the same pitfalls.
5 Common Mistakes that Tend to Trip Up New Data Engineers
Massive data sets by their nature are imprecise, and it’s really easy for data engineers to lose sight of the forest for the trees. A common theme among new data engineers is highly technical systems that are difficult to maintain in the long run and that don’t keep the end-user and the overall business objectives in mind.
Building Unmaintainable systems
Many new data engineers build programs that may work just fine and deliver a specific end result in the short term, but fall apart or are too complicated to maintain in the long run. ETL systems and data warehouses that are too reliant on complex code and can’t be managed without the original data engineer’s input are unsustainable and ultimately inefficient. New data engineers need to think beyond the immediate task at hand and approach development projects with a clear path for future development that will continue working and running well into the future.
Assuming Data Is Accurate
In a perfect world, data would be accurate and up to date, ready to plug in and work its magic. Unfortunately, that’s usually never the case. New data engineers can be overly reliant on the accuracy and “cleanliness” of their data sets. As a rule of thumb, it’s probably always a good idea to assume that even “clean” data is slightly dirty and at least partially inaccurate and outdated.
It’s imperative to incorporate data hygiene practices on an ongoing basis to make sure that you’re working with the most accurate information. Here’s a list of a few simple data cleaning best practices to keep in mind for every project:
- Create a data quality plan
- Standardize contact data at point of entry
- Validate data for accuracy
- Locate/identify duplicates
- Append data
Building Overly Complex Logic All In One
This ties into the first mistake and relates to data warehouses and systems that are too complicated and overwrought to survive without the ongoing input of their developer. A prime example is setting up too many steps in a single query. Think about every step and whether it’s really necessary to each query, and whether they’re making the system easier to use or just bogging it down unnecessarily and making it too difficult to maintain. Think ahead as you build and consider whether the system will be intuitive enough for someone else to understand and maintain.
Not asking why they are building something
This is the step where data engineers need to align and be in sync with the business and organizational goals of the project. The data itself may be set in stone, but without a clear vision of how it should come together and what the overall purpose is, new data engineers can lose sight of the intended purpose of what they’re trying to build. Knowing the intended business impact of each project makes it easier to prioritize how to scrape and structure the data. What is the end-user going to do with it? Data engineers should understand the business cases in order to understand what they should build.
Not thinking about their end-user
Even if a new engineer manages to sidestep every other mistake and pitfall, ignoring the end-user is a critical mistake that can sink the entire project. Even the most technically advanced system is only as good as its usefulness to the end-user, so their needs should always be front and center throughout the development pipeline.
- Are your data structures user-friendly?
- Is the end-user SQL savvy?
- What tools and programs do they have at their disposal?
- What are their overall capabilities?
- Do they understand data models?
These are just some of the basic questions for new data engineers to consider when working on a project.
Best Practices for New Data Engineers
Making mistakes (and learning from them) comes with the territory in any profession, but implementing a few best practices early on can save new data engineers a lot of time and effort in the short and long run.
Keep Your Functions Simple
Designing simple functions focused on a single task makes it easier to identify and quickly go back and course correct when you make a mistake. According to data engineer Anna Anisienia:
“To make functions reusable, it’s a good practice to write them in such a way that they do one thing. You can always have your main function that can tie together different pieces. Overall, I found out that by making functions small (i.e. focusing on doing one thing well), I tend to develop code faster, as a failure of a single element can be easier identified and fixed. Smaller functions make it also easier to exchange single components and use them as Lego bricks that can be combined together for different use cases.”
Less is More
Writing less code and keeping it as simple and straightforward as possible is the best way to make it work and easy for others to manage once you’re no longer involved. Think about how you read code. Is it concise and easy to follow? Does it have proper naming and the right structure? Say it in as few lines of code as possible because the less code you write, the less there will be to maintain.
Use Proper Naming Conventions
The easiest and most efficient way to make code easy to read and maintain by a new person is to be as clear and detailed as possible when you’re naming your code functions. Strive to make your code “self-documenting” so that its function is painfully obvious, which will make everyone’s life (and job) that much easier.
Remember that in data as in life, simple is usually better than complex.
How Do I Become a Data Engineer?
Data engineering careers are in high demand, so there’s never been a better time to become a data engineer. According to PayScale, the average yearly base salary for a data engineer in the U.S. can exceed $100k. There are several paths you can take to become a data engineer, and you don’t necessarily need to go back to college or invest in an expensive advanced degree to pursue a career in data engineering.
To become a data engineer, you’ll need to be a proficient programmer (Python is a standard entry point). Data engineers also need to learn automation and scripting, and database modeling (SQL is the best place to start).
You can learn everything you need to become a data engineer in a traditional academic program, through a boot camp, or you can even take the self-learning route to get started with the fundamentals.
If you liked this article, then check out these videos and articles!
5 SQL Concepts You Need To Know Before Your Next Data Science Or Data Engineering Interview
The Dos and Don’ts for Passing Your Technical Interview
7 Real-Time Data Streaming Databases — Which One Is Right For You?
Passing The System Design Interview For Software Engineers
4 SQL Tips For Data Scientists