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There’s growing interest in using AI agents to build ETL pipelines, generate SQL, automate data transformations, and maintain workflows.

Some engineers report major productivity improvements, while others question whether AI-generated pipelines are reliable enough for production.

I’m interested in what teams are actually experiencing.

Are AI agents reducing development time without increasing debugging and maintenance work? And how are you handling data quality, testing, and accountability when AI generates the pipeline?

With AI tools now capable of writing SQL queries, cleaning datasets, and creating dashboards, I’m wondering how much hiring expectations have changed.

Many aspiring analysts are still focusing on SQL, Python, Excel, and Power BI. But are employers now expecting AI skills, stronger business understanding, and real project experience too?

For those hiring or interviewing data analysts in 2026, what skills actually make a candidate stand out? Are certifications and portfolio projects still valuable?

I’ve noticed more Python developers moving toward Polars for handling large datasets, especially when Pandas starts struggling with memory usage and processing speed.

But is switching libraries really the solution, or can better optimization make Pandas just as effective for everyday data workflows?

For those who’ve worked with both, what differences have you noticed in performance, memory usage, and ease of use? At what dataset size did switching actually make sense?

AI-powered intelligent claims portals are transforming insurance claims management by automating workflows, reducing manual efforts, accelerating claims processing, and improving operational efficiency while delivering a better customer experience.