Ramesh Kumar Saxena

AI is reshaping the landscape of data engineering in transformative ways. One key trend is the rise of AI-powered data pipelines, where machine learning automates tasks like data cleaning, transformation, and feature engineering, significantly accelerating development cycles. Alongside this, AI-driven data observability tools are improving data reliability by detecting anomalies and quality issues in real time. Intelligent data catalogs are also evolving, using AI to automatically generate metadata, track lineage, and simplify data discovery. Natural language interfaces, such as Text-to-SQL systems powered by large language models, are making it easier for non-technical users to query databases using plain English. ETL and ELT processes are becoming smarter too, with AI automating schema mapping and transformation logic, reducing the need for manual coding. In parallel, synthetic data generation is gaining traction, enabling the creation of realistic datasets for testing, training, and privacy-sensitive use cases. Real-time data processing is becoming more intelligent as AI models integrate directly into streaming frameworks like Kafka and Spark, supporting use cases like fraud detection and personalization. Governance is also being augmented by AI, with tools now capable of automatically detecting sensitive data like PII and enforcing compliance with regulations.