Exploring the future of Data Engineering: A brief Guide

We live in a world full of data. Everything we do online and offline, right from browsing the internet to transactions in stores, or social media posts to shows we watch, generates huge amounts of data every single moment. This data is the greatest resource for any organization looking to incorporate data-driven decision-making. A recent study from IDC predicted a staggering 175 zettabytes of data to be generated by 2025. So, we can imagine how powerful its impact is going to be in today’s modern world. And, not to mention, the evolution of the data engineering domain it will bring with it to handle such a huge amount of data.

Today, data engineers can be termed as some of the most important professionals in data science careers as they are responsible for building and maintaining data pipelines to ensure data is accessible and is of top-most quality for analysis. But, as technology continues to thrive in our world, the future might look a bit different.

In this article, we will explore what the future of data engineering looks like.

Data Engineering in today’s world

Before we dive deeper into this topic, let us first understand the current role of data engineering in today’s world. The field of data engineering is full of challenges. Data silos, where different data types in an organization and departments are stored in fragments and not collectively in one place, have always been a problem that prevents proper data analysis.

Moreover, scalability is also an important issue because the amount of data generated is exploding, and managing complex data infrastructure including on-premise servers is very difficult. Scaling it is not only expensive but consumes a lot of time and resources.

But with the rise of cloud solutions, that offer more scalable and cost-effective solutions, things have started to change.

Future of Data Engineering

• Adoption of Cloud and Managed Services

  There are several key players including AWS, Microsoft Azure, Google Cloud Platform (GCP), etc. that are offering unmatched data storage, processing, and analytical capabilities. Gartner has predicted that by 2025, 70% of all organization's data warehouses will be deployed in the cloud.

  These online platforms offer powerful and scalable data engineering features helping data engineers to focus on productive activities like data modelling, ensuring proper data accessibility for machine learning models, pipeline optimization, etc.

  Not only that, managed services also eliminate the burden of managing local infrastructure, and freeing up data engineers' time for other productive works.

• Automation and Automated Analytics

  Data engineering involves a number of repetitive tasks such as data collection, cleaning, processing, and loading. All these tasks can be easily automated using data engineering tools like Apache Airflow, and Luigi, which are great for data pipeline development.

  Also, various self-service analytics platforms are getting a lot of traction these days such as Tableau and Power BI. As we already know, these tools are helping businesses and data engineers explore and analyze data independently without relying on specialized technical talent.

  Although this data democratization is of great help to businesses, a strong data governance framework is required to ensure responsible data usage is carried out.

• Evolution of more specialized roles

  Along with the evolution of the data engineering industry, the job roles in this industry are also going to evolve and become more specialized. We can expect new job roles like data reliability engineers who will be responsible for ensuring data pipelines are functioning properly and delivering high-quality data.

  The data product managers will be responsible for building and maintaining various kinds of data products that will serve different businesses with specific needs.

  DevOps in Data Engineering will grow exponentially and encourage collaboration between data engineering and data science teams in various data stages of the data lifecycle.

• Hybrid Data Architectures

  Well, the cloud technology revolution is underway even as you are reading this, but industry experts predict hybrid cloud solutions as the best option, going into the future, especially where proprietary and Personally Identifiable Information is concerned. By taking the help of this hybrid cloud and on-premises data architecture, organizations will have better control over sensitive data that will also allow scalability and flexibility that are mostly offered by cloud-native solutions.

• Ethical considerations

  The field of data engineering is highly controversial as biased data can lead to entirely wrong predictions and never-before-seen business calamities. Also, wrong data can give rise to data privacy concerns. So, in the future, we can expect stronger data governance and regulatory agencies to prioritize ethical practices right from data collection to its usage. Data engineers will be important to see through that the best practices are followed.

Conclusion

Data engineering is undoubtedly one of the most important industries in today’s world and as we move toward the future, we can expect more advanced tools and technologies to be incorporated in this domain that will revolutionize how data collection, storage, and processing is done. With the implementation of cloud solutions, managed services, and automation, data engineers will be able to work on more productive work rather than wasting time on repetitive mundane tasks.