The Analytics Engineering Podcast

Moritz Heimpel from Siemens and Ben Flusberg from Cox Automotive discuss their experiences adopting a data mesh architecture and collaborating with data at scale in large organizations. They explore topics such as setting up infrastructure to scale, domain specific ownership of data, data security, and the future of the data industry.

Brandon Taylor and Michael Olschimke discuss the Data Vault approach, its adoption in Europe, alignment with data mesh architecture, and the ongoing debate over Data Vault vs. Kimball methods. They also explore the impact of DataMesh on data warehouse design, recommended data modeling approaches, and hopes for the data industry.

Jonathan Frankle, Chief Scientist at MosaicML, discusses the future of training specialized models, MosaicML inside Databricks, and responsible AI practices. They explore LLM-based systems, one-way hash functions, and the integration of Databricks platform. The trade-off between model size, cost, and complexity in AI training is also touched upon.

Kasey Mazza, analytics engineering manager at HubSpot, discusses the roles of data analysts and analytics engineers, building internal data communities, and the evolving landscape of data teams. They also explore career growth and satisfaction, the interplay between organizational structures and tooling, challenges in decision-making and central governance, using DBT across the company, and the future of the data industry.

Arjun Narayan, CEO of Materialize, and Nathan Bean, a data leader at General Mills, discuss operationalizing warehouses, streaming analytics, and the challenges of manufacturing cereal. They cover the maturation of streaming technology, data management challenges, real-time operational decision-making, managing variation in manufacturing, digital twins for manufacturing line automation, operationalizing warehouses, trade-offs between batch and real-time analytics, the evolution of streaming analytics, and query languages in data analytics.

Yannick Misteli, Head of Engineering at Roche, shares insights from his transformative journey in a leading pharmaceutical company. He discusses moving over 120 engineers to a modern data stack and the important interplay of people, processes, and technology. Yannick highlights the shift from banking to pharmaceuticals and emphasizes the role of soft skills, innovation in cloud computing, and the challenges of self-service business intelligence. His passion for data integration and organizational modernization is infectious!

Andy Pavlo, a professor of databaseology at Carnegie Mellon and founder of OtterTune, talks about the complexity and specialization of database systems, the trend of separating storage and compute, scaling challenges, and the future of the data industry.

Jerry Liu, CEO and co-founder of LlamaIndex, discusses how companies are bringing their data to tailor large language models (LLMs) for their needs. Topics include working on LLMs versus autonomous systems, skill set and data preparation for LOMs, using databases for storing embeddings, capabilities of LMs in analyzing user questions, and exploring agents and specialized microservices in analytics engineering.

Ian Macomber, head of analytics engineering and data science at Ramp and formerly the VP of analytics and data engineering at Drizly, and Ryan Delgado, a staff software engineer at Ramp, have played pivotal roles in establishing Ramp's data team from the ground up and are spearheading the development of their comprehensive roadmap. In this conversation with Tristan and Julia, Ian and Ryan share insights on how Ramp's data team transformed unstructured data from contracts into valuable insights to enable faster decision-making. The $8 billion company values speed and empowers teams to build, ship, and measure products quickly. Ian and Ryan also talked about their approach to adopting new tech and elevating data as an equal player alongside product engineering and design. For full show notes and to read 6+ years of back issues of the podcast's companion newsletter, head to https://roundup.getdbt.com. The Analytics Engineering Podcast is sponsored by dbt Labs.

Daniel Le is the CFO at dbt Labs where he has built multiple teams. He is also the former head of FP&A and operations at Zoom, and he helped scale FP&A as the former finance director at Okta. In this conversation with Julia, Daniel shares his view as CFO on the challenges SaaS companies face and the importance of finance teams creating a holistic view of their business. Daniel gives advice to data leaders about how they can automate business processes with dbt Cloud and use self-service analytics to automate revenue recognition, generate consistent headcount analytics, and more to impact their organization. Read more about Daniel's story here. For full show notes and to read 6+ years of back issues of the podcast's companion newsletter, head to https://roundup.getdbt.com. The Analytics Engineering Podcast is sponsored by dbt Labs.