In the last few months, I’ve been working hard to onboard new data and analytics engineers. Along the way, I’ve learned which parts of dbt and dbt Cloud are hard to teach, what confuses people, and where tools or training fall short. I’d like to swap ideas and hear how others are handling this.

Master the art and science of analytics engineering with 'Fundamentals of Analytics Engineering.' This book takes you on a comprehensive journey from understanding foundational concepts to implementing end-to-end analytics solutions. You'll gain not just theoretical knowledge but practical expertise in building scalable, robust data platforms to meet organizational needs. What this Book will help me do Design and implement effective data pipelines leveraging modern tools like Airbyte, BigQuery, and dbt. Adopt best practices for data modeling and schema design to enhance system performance and develop clearer data structures. Learn advanced techniques for ensuring data quality, governance, and observability in your data solutions. Master collaborative coding practices, including version control with Git and strategies for maintaining well-documented codebases. Automate and manage data workflows efficiently using CI/CD pipelines and workflow orchestrators. Author(s) Dumky De Wilde, alongside six co-authors-experienced professionals from various facets of the analytics field-delivers a cohesive exploration of analytics engineering. The authors blend their expertise in software development, data analysis, and engineering to offer actionable advice and insights. Their approachable ethos makes complex concepts understandable, promoting educational learning. Who is it for? This book is a perfect fit for data analysts and engineers curious about transitioning into analytics engineering. Aspiring professionals as well as seasoned analytics engineers looking to deepen their understanding of modern practices will find guidance. It's tailored for individuals aiming to boost their career trajectory in data engineering roles, addressing fundamental to advanced topics.

Performance is a crucial factor in delivering timely and accurate data to organizations. However, debugging the performance of dbt models can be a challenge, as most resources available focus on legacy databases or tips for specific data engines that do not translate to modern data platforms.

In this talk, Juan Manuel Perafan focuses on optimizing performance for dbt users, without focusing on any specific data warehouse. He explores the commonalities across most data warehouses and provides practical tips and strategies for improving the performance of dbt models. From query optimization to materialization strategies.

Whether you're new to dbt or a seasoned user, this talk provides valuable insights and best practices for improving the performance of your dbt models.

Speaker: Juan Manuel Perafan, Analytics Engineer, Xebia

Register for Coalesce at https://coalesce.getdbt.com