Data engineering in 2025 is being shaped by cloud platforms, real-time processing, and AI/ML integration. The shift from ETL to ELT processes with tools like dbt and Apache Airflow are central themes alongside discussions about cost-effectiveness of cloud managed services such as AWS Glue and Snowflake. AI is automating schema inference and pipeline optimisation, while data quality and governance remain critical, especially with regulations like GDPR and CCPA. The evolving job market demands upskilling in AI/ML and cloud technologies, with online communities replacing Twitter/X as primary hubs for collaboration and knowledge sharing. These forces demand data engineers balance automation with oversight in an AI-driven landscape.

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