This blog argues against the rush to adopt the latest AI technologies, emphasizing the importance of strong data engineering foundations and "Classic Analytics." It advocates for a balanced, pragmatic approach to AI adoption, ensuring long-term success over short-term gains.

Transitioning to Databricks reduced costs by $19K/month and streamlined data operations. Learn how Databricks' unified platform simplifies data engineering and boosts efficiency in our latest blog.

Disappointed by the high failure rate of AI initiatives? You're not alone. This post unveils the secrets to planning and executing a successful AI project. We detail our proven methodology for ensuring AI success, focusing on identifying high-ROI use cases and navigating the Proof-of-Concept stage effectively. Learn how to choose the right supplier, avoid common pitfalls, and ensure your AI project transitions smoothly from pilot to production, delivering real business results and a competitive edge.

Building on our exploration of Generative AI applications, this article dives deeper. We'll uncover the technical details: what makes it work and what challenges it faces. This equips you to understand how projects are built, their hurdles, and the best practices to overcome them.

The democratization of AI birthed fantastic products, but also downsides. Information overload, a talent gap, and poorly executed projects highlight the need for "know-how." This article empowers you to navigate this new landscape.

Funny or not, building a secured, governed and scalable data platform that supports multiple types of use cases along with the data management processes and practices is very similar to building a skyscraper - the higher the building grows and supports more units and people, the complexity increases.

This guide will help you understand the complexities of Databricks, ensuring your data skyscraper stands tall and proud.