How does AI help in Data Engineering?

Building something tangible that actually moves the needle in a meaningful way for a business is where the real test of these “AI-anything” things comes from.

I’ve closely observed that AI to only save me time when I know exactly what I’m doing. If it’s something I don’t know, it just slows me down by going through rabbit hole after rabbit hole.

As you know that if you are building a data pipeline for extracting the data from the multiple sources where data has duplicity, anomalies, incomplete and messy then it becomes very hard to know how well AI will get at managing the mess, but for now it sucks at it. AI is useless without context and lots of how a data pipelines need to be engineered depends on the business itself and unique needs.

AI improves data engineering by automating complicated operations, increasing data quality, and allowing for scalable, efficient data pipelines. Data engineering include gathering, storing, processing, and translating raw data into accessible formats for analysis, and AI simplifies these activities while bringing predictive and adaptive capabilities.

1. Automated Data Pipeline Construction and Optimization

• Use Case: AI automates the design, deployment, and optimization of data pipelines, reducing manual…