What are the most promising areas of growth in data engineering?

I agree with this. Data quality is crucial for data engineering, and it is something that is sometimes rushed or added in as an afterthought. Data Quality Management is a continuous process that involves implementing standards and procedures to improve data quality across the data lifecycle. Goals for Data Quality include automation throughout the data lifecycle, root cause remediation, company-wide responsibilities, quality monitoring, and having a unified approach to ensuring your data meets business requirements and any applicable compliance standards. Data quality is a fundamental pillar of a good data management and governance framework, leading to better business outcomes.

These last few years have been an exciting time for AI/ML, and given how quickly this is evolving, it's only going to get more exciting as the landscape changes. Starting a machine learning journey is challenging. The hardest part is finding a ‘hook’ - a compelling reason that resonates with people and demonstrates the value of ML. This could be a problem to solve, a process to optimise, or an unanswered question. Once you have your ‘hook’, it’s about building the necessary infrastructure to support your projects, including data pipelines and platforms for handling large datasets. It also involves collaboration with data scientists and ML engineers. Remember, this journey is about people as much as technology.

There are so many data ethics issues to consider: lack of accountability, misleading data, bias and discrimination, privacy and consent, security, and transparency. In my experience, it is important to build relationships with the teams responsible for any overarching policies and regulations specific to your industry. They live and breathe it every day so it's a good way for you to learn and get an understanding of what you need to do as a Data Engineer, or a Data Manager. These legislations and statutory compliance regulations can also change frequently so it's important to make sure you are up to date and staying ahead of any changes that are required on the data side of things.

Promising growth areas in data engineering encompass real-time data processing, data governance, cloud-based solutions, DataOps practices, data quality, integration, and the intersection with machine learning engineering. These areas reflect the evolving landscape of data engineering, driven by the increasing need for agile, efficient, and compliant data management to support data-driven decision-making and innovation.

One trend that is sure to continue is the move to cloud. As organisations continue to move to cloud, there would be a need to standardise and optimise cloud compute and storage in order to minimise cloud cost. It is surprising, that in spite of decades of cloud experience, cloud providers don’t promise services which promise minimal cost for their data engineering needs. Data engineering teams continue to spend efforts to develop ETL frameworks and later methodologies to minimise resource consumption and cost. The need to catch up with latest trends like realtime analytics, generative ai integration and automation, newer data science solutions will only cause companies to focus more on cost optimised services which can be readily adopted.