As the data industry evolves with new technology, so do data engineering challenges. What can new engineers expect?
As data engineers, you play vital roles in your field by collecting and analyzing data. But necessary data engineer skills today aren't the same as they were in years past, and the role is seeing some serious growing pains. Let's focus on some challenges for data engineers.
One of the biggest challenges, and the root of many others, is that data engineering is a relatively new and dynamic discipline. While it has its origins in database maintenance and business intelligence, it's taken on a life of its own in recent years. You won't find many university courses on the subject, nor will you expect to find a "data engineering boot camp" any time soon. That means engineers learn the bulk of their best practices on the job.
Further complicating things is that the data engineering field deviated from its original path. Engineers of the past focused more on creating data pipelines and collecting data into warehouses. Now the work is far more complicated, with added responsibilities in data analytics and building algorithms. And the data that engineers work with is astronomically larger than in the past (but more on that later.)
Data engineering is a true hybrid role born from an explosion of data and technological advancement. These advancements are industry-changing, and that change is still ongoing. We can expect the data engineering role to keep changing with it, and where it ultimately ends up remains to be seen.
The header is a bit of hyperbole, but the term "Big Data" is not. Data engineers today must work with more data than ever before, and there's no sign of a plateau. While the massive amounts of data are a boon to the industry, data grows at a rate faster than most can expect to wrangle it, which leads to a couple of problems.
All that information is a strain on the most advanced machines. Reports and models slow to a crawl as they struggle to process the wealth of data running through them. If you're not careful, your data needs can outgrow the capabilities of your machines.
As a data engineer, your time is valuable. You can't afford to spend hours on a few reports. There are ways to work around this, though. If you haven't already, moving to the cloud can be a viable option. Cloud data warehouses have several perks, such as being more scalable and elastic than the traditional warehouse. Additionally, not having your servers on the premises means you'll save time and resources on database management.
All this data can overload engineers, who struggle to pull in data sets fast enough. What isn't helping is older ETL technology, which can be code-heavy and bog down your process further. A potential solution can be to switch to an ELT system --- that's extract, load, and *then *transform --- working with the data on an as-needed basis. It can conflict with your data governance strategy (more on that below), but it can be useful in developing a bigger picture of the data and guiding you toward better data sets for your core models.
With the demand for more data pipelines and the rising tide of Big Data looking more like a tsunami, one of the greatest data engineering challenges is keeping existing pipelines in working order.
Fortunately, there's also a shift at the code level. Imperative programming makes way for declarative programming, and a growing emphasis on low-code or even no-code systems takes a huge burden off of the data engineer's shoulders and reduces the maintenance burden.
While other industries fear automation, in this case, it's a data engineer's friend.
Data governance isn't fun. It adds a level of bureaucracy to data engineering that you may want to do without. But the alternative can lead to inconsistencies in key data values and definitions. It means the potential for bad data floating around in various integrations and reports.
Consider how many integrated systems exist in your business. If certain fields aren't synced between programs, it could lead to inaccurate data if reports get pulled from the wrong place at the wrong time. This is especially true if fields aren't updated in real time.
One solution to this is to impose some sort of data governance plan. This could range from a page in a handbook to a larger committee, depending on the size of your business. What's important is that you have a plan to keep data input and output consistent. The good news is you likely have at least some data governance strategy in place already.
Unfortunately, this presents new challenges for data engineers alongside the previous point of having too much data to work with. You now need to strike a balance between getting data quickly and "good enough," and keeping the data accurate enough to make sound business decisions.
Sometimes your data engineering challenges aren't going to be with data but other people. Clients or employers can put up obstacles intentionally or otherwise. Sometimes we just can't get out of our way.
A clear business strategy will be the foundation for any company. But some people find a new toy in the industry and want to implement it without considering how it affects their business or strategy. Machine learning could be that new toy a business wants to adopt, but have they considered how they can make it work for them?
Change doesn't have to be as radical as a new AI. It could be a new integration you want to implement. But without considering how this addition will fit into your business plan, you may end up spending more time than you want to try to shove that puzzle piece into place.
The one way around this obstacle is to put your business goals first, every time. Consider where you are in your data strategy, where you want to be, and finally, how you will get there. For more guidance on this aspect, here's another article on developing a data analytics strategy.
Some legacy programs and systems persist almost out of comfort. They take the role of a rock in the middle of a rushing river. But in the face of an ever-changing industry, sometimes these systems can pose problems that would be solved with a little software upgrade.
One example is the use of Excel. It's been a mainstay in offices for decades, and for good reason --- it's simple and effective at what it does. But it's not without its faults, and these faults can be costly even to the biggest companies. Consider Barclays in 2008 buying much more than it bargained for due to a reformatting error in a single Excel spreadsheet. These errors are uncommon, but not unheard of, and they could happen to you.
If you're still working with Excel and want to avoid similar errors, consider treating it like its own coding language. That means implementing reviews and test cases. Much like your data governance strategy, it may seem like a costly and tedious addition to your workload. But you know the risks of going without.
The alternative would be to research and consider other software. Just make sure it aligns with your business strategy.
There are a lot of challenges you will need to overcome if you want to be a data engineer.
But this is also probably why companies are hiring more data engineers as compared to data scientists.
For anyone looking to improve their data strategy, it all starts with data engineering.
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