Data engineering predictions 2025: Data Mesh, Automation, and AI

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Data engineering, as a profession and as a practice, is steadily evolving to meet the growing demands of businesses. Data engineering uses tools like SQL and Python to make data ready for data scientists and hence it is an essential part of the drive to accumulate and understand business data.

By 2025, businesses can expect to see a surge in key trends that will shape the future of data management.

Yet what are these trends and what should business leaders be on the look out for? Arnab Sen, VP of Data Engineering at Tredence, has outlined some of the most significant predictions for the coming year and discussed these with Digital Journal.

Data Mesh Architecture

Decentralization of data ownership will become more prevalent, allowing teams to manage their own data as products. This will be particularly beneficial for large organizations seeking independent, high-quality data exchange.

DataOps and Automation

The application of DevOps principles to data engineering will accelerate the deployment and maintenance of scalable data pipelines. Increased automation will streamline processes and reduce manual effort.

Data Security and Privacy

With stricter regulations in place, organizations will need to prioritize data security and privacy. Encryption, tokenization, and data masking will become essential components of data pipelines.

AI/ML-Powered Data Engineering

Artificial intelligence and machine learning will enhance automation in data pipelines, improving data quality and classification. This will enable faster, real-time insights and streamlined data management.

Real-Time Analytics and Streaming Data

The ability to process data in real-time will become increasingly important. Tools like Kafka will enable businesses to make faster decisions and deliver personalized customer experiences.

Low-Code/No-Code Data Engineering

The rise of low-code/no-code platforms will democratize data engineering, making it accessible to a wider range of users. This will reduce reliance on technical experts and accelerate the development of data pipelines.

Sustainability and Green Data Engineering

As organizations become more conscious of their environmental impact, sustainability will become a key consideration in data engineering. AI-driven frameworks can help manage costs while promoting green practices.

Generative AI-Driven Data Management

Generative AI will automate tasks like data cataloguing, anomaly detection, and data governance. This will improve data quality, increase accessibility, and enhance efficiency.