Why Data Engineering Is Broken and How AI Agents Will Fix It
About this episode
Ranjith Prabu discusses the challenges of data engineering and how AI agents can address these issues.
Why would someone leave Apple, LinkedIn, and Meta to join an early stage startup? That was the first thing I wanted to ask Ranjith Prabu, CTO when he sat down with me at the WALT AI office in Santa Clara on The Ravit Show. He spent two decades building and scaling data platforms at some of the biggest companies on earth. Now he is the CTO of WALT AI. His answer was simple. Even the best resourced companies on the planet still struggle with data engineering. It is the bottleneck nobody talks about. Engineers build the pipelines but never reach the insight. Analysts have the questions but cannot touch the plumbing. Work gets thrown over the wall, and value leaks at every handoff. Ranjith calls this the chasm. He left to close it. A few things from our conversation that stuck with me. Data engineering used to be locked away. It needed huge teams, huge budgets, and armies of consultants. The way cloud opened up infrastructure, agents are starting to open up data engineering. Determinism matters more than people think. If the CEO asks the same question twice, the answer has to be identical. A model writing fresh SQL every time cannot promise that. That is the line between a demo and…
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