By Arnab Sen, VP, of Data Engineering, Tredence
The world is becoming more data-driven, and enterprises are racing to extract maximum value from their datasets. By 2025, various transformational trends will enable organisations to improve operational efficiency and enhance innovation at a faster rate.
The following nine key trends will shape the future of data engineering:
Generative AI-Based Data Management: Generative AI revolutionises data management by providing autonomous control over critical processes like data cataloging and detecting threats or anomalies. It allows users to ask questions and improve data quality, thus increasing efficiency and accessibility.
The rise of data mesh architecture: Data Mesh is gaining prominence, especially in large companies, as it allows teams to own and manage data as a product. This ensures that high-quality data can be shared seamlessly and autonomously across departments, enabling scalability and collaboration.
Scalability through DataOps: DataOps is inspired by DevOps and emphasises enhanced collaboration and automation in data engineering. This helps manage scalable data pipelines, enhances operational agility, and improves efficiency in data-driven processes.
Stronger Data Security and Privacy: Increasing compliance and regulatory requirements have created the need for stricter security measures, such as data encryption, tokenisation, and data masking. At the same time, devising these stronger measures is becoming a standard practice to comply with security regulations and ensure that sensitive information is adequately protected throughout data pipelines.
AI/ML-Powered Data Engineering: Artificial intelligence and machine learning are enhancing the efficiency of data engineering by automating processes, accelerating data analysis, and giving insights in real time. These enhancements enable faster decisions and unlock greater value for enterprises.
Real-Time Analytics and Streaming Data: Real-time processing tools such as Apache Kafka transform how organisations deal with data. Such tools expedite insights to enable businesses to tailor customer experiences and enhance operational efficiency in dynamic environments.
Focus on Data Observability: Enterprises increasingly focus on data observability to supervise pipeline performance, identify anomalies in advance, and ensure data reliability. The approach reduces downtime and builds trust in data-driven workflows.
Democratised Data Engineering: Low-code and no-code platforms democratise data engineering by allowing users to design data pipelines with minimal technical knowledge. This reduces dependency on specialised teams and broadens access across organisations.
Sustainable Data Practices: Sustainability is increasingly becoming integral to data engineering. This is because enterprises employ environmentally sustainable technologies and optimise energy consumption to reduce carbon footprints, thereby achieving strategic environmental goals. These trends epitomise the dynamic growth of data engineering as the essential pillar for digital transformation. Adapting these innovations helps companies stay competitive, adaptable, and prepared for the possibilities of a data-driven future.