AI

Originally published on Kepler AI produces numbers that are incorrect, untraceable, and unverifiable. Asking the same question twice yields different answers, and there’s no way to distinguish right from wrong outputs. This unreliability poses serious risks across industries like healthcare, legal, insurance, government, and finance where numerical accuracy directly impacts outcomes. Through conversations with 137 financial firms - including private equity, hedge funds, and investment banks - a consistent pattern emerged: everyone wants to use AI, but nobody trusts it. As one managing director stated, “I can’t put a number in front of a client if I can’t show where it came from.” ...

I joined Joe Reis on The Joe Reis Show to talk about open source communities — what makes them strong, what causes them to stall, and what contributing to a project like Apache Parquet actually looks like from the inside. The conversation also covers LLMs and AI, and where they do and don’t change the fundamentals of data engineering work. Link YouTube Spotify Apple Podcasts

Tell us about your background Throughout my career, I have dedicated myself to creating tools, products, and technologies that help people effectively utilize their data. My passion lies in developing products that enable users to efficiently and scalably gain maximum value from their data. My journey in understanding the intricacies of data and its potential began at Palantir Technologies, where I began working on search and indexing products. As data volumes grew, I focused my efforts on solving some of Palantir customers’ core problems across the financial and defense verticals before leading customer focused compute teams. After Palantir, I served as CTO at Veraset, a cloud-based data-as-a-service company. Veraset delivered high-quality, large scale data to a number of enterprises and grew to 15M ARR before being acquired. Following this, I joined Citadel Investment Group as the Head of Business Engineering of Ashler. In that role, I managed crucial data operations, including overseeing data pipelines, investment platforms, data lakes, and the software and data engineering teams responsible for them. ...

Managing SQL code at scale is one of the biggest challenges in data engineering. As data teams grow and pipelines become more complex, traditional approaches to SQL development quickly become unwieldy. This LinkedIn Learning course explores how dbt (data build tool) transforms the way we think about SQL development, bringing software engineering best practices to analytics engineering. Course Approach Real-World Problem Solving: Each chapter presents actual situations and challenges that data engineers face, with focused code examples showing practical solutions. ...

Originally streamed live on August 3, 2023 - LinkedIn Learning’s “The Future in Tech” series Data engineering is the unsung hero fueling the rapid growth and consumption of artificial intelligence. It transforms AI’s potential into reality, driving digital innovation and reshaping the world. In this comprehensive discussion, we explore how data engineering unlocks and enables democratized use of Artificial Intelligence. Video: The Future in Tech - Data Engineering and AI Discussion (1,668 views) ...