**London**: Experts gathered at a DBTA webinar to discuss transformative trends in data engineering, focusing on AI integration, automation strategies, and the evolving dynamics between data and software engineering teams, as industry leaders predict significant shifts and challenges ahead for 2025.
The landscape of data engineering is undergoing transformative changes as professionals gather to deliberate on the future trends and best practices shaping the industry. Recently, experts convened for a webinar titled “Top Trends in Data Engineering for 2025” hosted by Database Trends and Applications (DBTA), where they explored emerging patterns, from the rise of AI-driven tools to the necessity for real-time data processing.
Sean Knapp, founder and CEO of Ascend.io, remarked, “Data teams are subjected to this overwhelming amount of toil. And data engineering wasn’t always the most enjoyable experience.” This sentiment is reflected in findings from a DataAware Pulse Survey, which indicated that 83% of data practitioners believe AI technology has improved their productivity. Despite this, a significant portion of data teams report feeling overburdened: one-quarter of respondents stated they are significantly over capacity, while 95% have been at or above their work capacity for five consecutive years.
The survey also highlighted that nearly half of all data engineering efforts are consumed by maintenance tasks, hampering the ability of teams to engage in innovative projects. Knapp pointed out that as new technologies, particularly AI, are integrated into business operations, the resultant workload pressures often fall heavily on data engineers without adequate support.
Looking ahead, productivity is set to be a focal point for data engineers in 2025, with automation emerging as a critical strategy for managing workload demands and addressing maintenance challenges. Knapp elucidated the approach taken by Ascend, explaining that their framework for automation involves three key layers: a unified metadata layer, an automation layer powered by metadata, and an AI Agents layer designed to improve user experience.
Marc Lamberti, head of customer education at Astronomer, illuminated further trends set to shape data engineering in the coming years. Among these trends is the shift of data operations from analytics toward operational use cases, a growing resemblance between data engineering teams and traditional software engineering teams, the need for standardisation within platform teams, and an understanding that modern data teams are now focusing on building data products for strategic outcomes.
Astronomer, a prominent player in the field with its Apache Airflow framework, is poised for enhancements with the anticipated release of Airflow 3.0. This significant update introduces features such as multi-language support for execution, expanded data awareness, improved user interfaces, and novel support for ML operations, all aimed at streamlining data workflows and increasing efficiency.
Saket Saurabh, CEO and co-founder of Nexla, addressed the vital trend of generative AI (GenAI) and the concept of converged data integration as central to the future of data engineering. He described the evolving methodology at Nexla as schema-centric abstraction, which allows data engineers to unify and simplify various data sources into a coherent construct called a Nexset. This approach not only alleviates the burdens faced by data engineers but also supports GenAI applications by harmonising both structured and unstructured data types.
The discussions at the DBTA webinar encapsulated a snapshot of emerging trends in data engineering, underscoring the industry’s responses to the evolving demands of data management and analysis in an increasingly automated and AI-driven environment. For those interested in a comprehensive understanding of these developments, an archived version of the full webinar is available for viewing.
Source: Noah Wire Services
Subscribe to Industry Updates
