While data science gets most of the spotlight, data engineering is becoming just as crucial. AI models are only as good as the data they’re trained on, and ensuring high-quality, well-structured data is key to successful machine learning.
Data engineers focus on ETL (Extract, Transform, Load) pipelines, database management, and data warehousing to provide clean and efficient data for analysis. Tools like Apache Spark, Airflow, and Snowflake are becoming industry standards.
As AI adoption grows, companies are realizing that a strong data foundation is essential. Could data engineering be the unsung hero of the AI revolution?
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