Architecture

Originally published on Kepler Everyone’s talking about context engineering right now, but most of the conversation is focused on the wrong thing. Read the blog posts, the guides, the thought leadership. They’re all asking the same questions: What should I include in the context window? How do I manage tokens efficiently? How do I curate what the model sees? These are valid questions. They’re also the easy part. The hard part isn’t deciding what context to include. It’s building systems that deliver that context reliably, with provenance, at scale, every single time. That’s not a context problem. That’s an engineering problem. And engineering means something specific. ...

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.” ...

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. ...

In this course, instructor Vinoo Ganesh gives you an overview of the fundamental skills you need to become a data engineer. Learn how to solve complex data problems in a scalable, concrete way. Explore the core principles of the data engineer toolkit—including ELT, OLTP/OLAP, orchestration, DAGs, and more—as well as how to set up a local Apache Airflow deployment and full-scale data engineering ETL pipeline. Along the way, Vinoo helps you boost your technical skill set using real-world, hands-on scenarios. ...