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Cover image for How to Overcome Common Challenges in Data Engineering to Make Data Work for You With Minimum Effort
Sergey Laptick
Sergey Laptick

Posted on • Originally published at xbsoftware.com

How to Overcome Common Challenges in Data Engineering to Make Data Work for You With Minimum Effort

Information is the cornerstone of every business. Whether you treat animals in a small rural town or sell complex electronics systems to clients all around the world, there’s a base of records you can’t live without. The way you use the information you have may vary depending on the current needs of your organization. For some, a web app allowing users to check the dates of appointments to the veterinarian will do the trick. Some more demanding business owners may need to use Machine Learning and Artificial Intelligence algorithms to predict customer behavior and adopt their strategy accordingly.

Even if you work in a small company, at some point, you may find that a bunch of excel tables can’t cover your needs anymore.The growing number of customers constantly increases the overall number of data records and relations between them. Using different apps that work with different data formats makes everything even more complicated. Without a significant development background, it may be hard even to determine how many databases you need to remain efficient. One? Three? A hundred? In such a case, you can rely on data engineers. Their job is to take care of your data infrastructure and the challenges they face along the way will be considered today.

What Makes Data Engineering so Important

Data engineering is an important part of software development in every case where vast amounts of data are used. When a development company creates a complex solution for a customer whose business model involves analyzing tons of heterogeneous information and extracting helpful insights from it, it is crucial to take care of various fundamental aspects of storing the raw data. It’s essential to ensure that data storages are accessible for any kind of software such as BI or Deep Learning apps. Also, to take the most out of this data, it must be accurate and reliable.

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To stay afloat and grow as quickly as possible, companies can use various apps that must access multiple different data sources. Warehouse management systems, ERP solutions, and CRM apps not only process data but also generate new portions of it. Data engineering helps all these parts to work together as a single mechanism. The development of a pipeline that can collect information from different sources and deliver it to a destination point in a timely manner can benefit every business organization, but challenges that the engineering team may face don’t allow treating the process carelessly.

Overcoming Obstacles on the Way to State-of-the-Art Data Infrastructure

The first aspect of data engineering is related to software development as a whole. We’re talking about the intention of some development companies not to consider a real person who will use the app while designing it. Data architecture is a purely technical process that requires many ears to master. However, the person who will use the application may be even more important than the data engineer, despite the lack of tech expertise. Software is created to be used and not to just exist, and meeting users’ expectations must remain one of the major priorities.

Despite that data engineering is a part of the development process that is usually hidden from the user’s eyes, it’s the user who determines how the process must evolve. As a rule, if a person who uses the app works with easy-to-understand data, the underlying functionality requires significant effort. On the contrary, if software provides access to raw data, it’ll be easier for the data engineering team to make their job done. For example, data analysts can use such programming languages as R or Python to extract useful insights from raw data, while the company managers will feel comfortable with tables and graphs that BI solutions can provide.

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Each new client leads to the generation of new data records. The better you satisfy the client’s needs, the higher will be the probability that more people will get to know about you and will replenish your clientele. On each step of this ladder to ultimate success, you’ll generate more and more data. Moreover, it’ll come from different types of devices. It’s hard to surprise a developer with the need to process data coming from smartphones, but there may be more β€œexotic” data sources if you, for example, use IoT devices to boost your business. In this scenario, eventually, the question of efficient data management strategy may appear.

The problem is that when you grow too big, this flow of data will become never-ending, while most records may have pretty low business value. Using inaccurate information that will appear in the overall data flow can harm your organization or nullify the efforts of your team. Therefore, among other things, it’s important to ensure that the development process includes the creation of a long-term data management strategy. It implies that there are strict rules determining how the integrity of data must be maintained and someone responsible can ensure that everybody follows them.

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A proper approach to data engineering can also help the development team to create practices and policies that will help to eliminate the possibility of human error. There’s no product 100% secure from these kinds of issues, but the good news is that an experienced data engineering team can more or less predict scenarios where something may go wrong and develop a guide helping to avoid any unpleasant consequences.

System integration is an important part of the development process and its main purpose is to make different databases work together. Sometimes companies continue to use legacy databases for many reasons. Database migration may cost a lot of money and require much effort for which some may not be ready. The lack of possibility to connect such databases with modern ones is a source of an additional headache for a development team, and data engineering specialists in particular. Even if all components of a software system are up-to-date, it doesn’t mean that developers will deal with a homogeneous set of data tools. Some of them better work with multi-terabyte datasets while the others can find specific records in a blink of an eye which are required for websites to work fast and ensure an enjoyable user experience.

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If you want system integration to run smoothly, the first thing that you must do is to modernize the legacy software if you use it to avoid all the hidden costs it brings. Doing this at the first stages of development can make the life of a data engineering team much easier. However, as it’s been said, not everybody wants to modernize, and it’s their right. Here, the software development team must do their best to enable a new data pipeline ensuring barrier-free flow of information between different subsystems using such solutions as Azure Data Factory, for instance.

Conclusions

Data in complex software solutions behave like some strange fluid. You collect it from different sources. You store it in multiple β€œcontainers” each with its specific features. You must deliver it to multiple people and keep in mind that it can change its state under different circumstances. The fluid nature of data, however, for some people is nothing more than just a feature of the work that needs to be done every day. Data engineering is a set of practices that allows to subdue the fickle nature of the data and makes it as predictable as possible to ensure the best possible user experience.

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