Every organization and data engineer should know these challenges for a better understanding.

Digital information is now one of the most important pillars of the business world. Organizations from all industries are looking for the best ways to use it for long-term growth. After all, there is a lot of volatility in today's world, and being prepared to deal with it is essential for business leaders.

Companies are increasingly turning to the field of data engineering for assistance in becoming more data-driven. However, because digital information technologies are constantly evolving, your company may face several data engineering challenges along the way. In this article, we have explained the major challenges faced by data engineers. Read on to learn about data engineer challenges in detail.

What is Data Engineering?

Data engineering is the complex task of making raw data usable by data scientists and organizational groups. Data engineering encompasses a wide range of data science specialties.

Data engineers create raw data analyses to provide predictive models and show trends in the short and long term, in addition to making data accessible. It would be impossible to make sense of the massive amounts of data available to businesses without data engineering.

Data Integration from Several Sources

As the number of data sources grows, especially if there are some similarities between them, it becomes increasingly difficult to integrate them in a granular and consistent manner.

Even the Big Data Platform May Struggle to Handle the Volume of Data

Organizations and data engineers are now working with more data than ever before, and there is no sign of saturation. For sure, more data is better for organizations, but if it goes above and beyond expectations, it can cause major issues.

Data Engineers Must Be Constantly Learning

In recent years, we believe it has become one of the most difficult challenges we face as data engineers. As data grows, so do its storage and processing requirements; new platforms, processing engines, frameworks, tools, and so on are being developed, forcing data engineers to stay on their toes at all times.

Support and Maintenance of the Data Pipelines

As the number of data pipelines grows, so does the number of data sources and types of data. This includes pipeline support and maintenance. There, it is critical to have consistent design patterns and automation in place to ease debugging and maintenance if something goes wrong.

Performance and Scalability Issues

As data volumes grow, so will the demand for analytics, modeling, dashboards, and reports. If the proper platform and tools are not used, this will result in performance and scalability issues. Scaling storage and processing needs is a difficult and time-consuming task for infrastructure teams. To avoid such scenarios, the data engineering team should be responsible for making the right decisions from the start.

Data Quality

The accuracy of the data-driven reports, dashboards, and models is entirely dependent on their quality. There are various aspects of how data quality is defined and measured. Completeness, consistency, conformity, accuracy, integrity, and timeliness are among them. These can be addressed either during the ingesting/ETL job or by scheduling jobs that can check these aspects on the data loaded overtime regularly.

Data Governance

This is one of the most important processes in any data engineering project. The responsible team will ensure that data engineers adhere to policies, strategies, and compliance regulators. However, if data grows at a faster rate than expected, these data governance requirements may become an impediment for engineers. Maintaining this delicate balance is undoubtedly one of the most difficult challenges.

Data Accessibility Issues

This is an unusual situation in which, despite having data loaded from numerous sources, there may be issues with it not being available when needed. This could be due to an issue with the ETL/ELT process or incorrect access controls.

Lack of Proper Understanding of Massive Data

Companies fail in their Big Data initiatives when they lack understanding. Employees may be unaware of what data is, how it is stored, and processed, how important it is, and where it comes from. While data professionals may be aware of what is going on, others may not have a clear picture. This can result in a large amount of data sitting in data stores, either completely unused or overused.