In this bonus episode, Eric and Kostas preview their upcoming conversation with Kunal Agarwal of Unravel Data.

Stanford University PhD student, Tony Wang, discusses his research focus on time series data management. Topics include challenges in academia and industry, academic lab structure, decision to move from hardware to data research, data modeling in time series, issues and potential solutions for parquet format, and the role of external indices in parquet files.

Tony Wang, an academic researcher at Stanford University, discusses his research in data systems and databases, the connection between academia and industry, and shares insights on data processing systems and future trends with the hosts.

Highlights from this week’s conversation include:Peter's background and journey in data (0:26)Introduction to PLG (4:18)Starting in data at Heroku (6:05)Building the data stack at Heroku (8:13)Data stack requirements for early-stage companies (12:00)Differentiating PLG companies from open source companies (19:26)Venture capital and open source as a lever for growth (22:56)Initial data modeling and analysis (25:38)Operationalizing Data (29:16)Sales and Marketing Operationalization (31:52)Identifying Signals (34:16)Challenges in Developing Signals (37:07)Account Management for Developer Tools (42:30)Challenges in Achieving Margins (45:02)Leveraging Infrastructure for Margins (47:35)Inference vs Training (54:55)Final thoughts and takeaways (57:02)The Data Stack Show is a weekly podcast powered by RudderStack, the CDP for developers. Each week we’ll talk to data engineers, analysts, and data scientists about their experience around building and maintaining data infrastructure, delivering data and data products, and driving better outcomes across their businesses with data.RudderStack helps businesses make the most out of their customer data while ensuring data privacy and security. To learn more about RudderStack visit rudderstack.com.

GTM consultant Peter Chapman discusses the importance of data in business operations, focusing on PLG and financial implications of data-driven tools. The episode also includes a preview of Peter's data journey at Roku and personal connection with the hosts in San Francisco and Silicon Valley.

Rishabh Bhargava, an expert in AI-based data cleaning, data labelling, and data enrichment with LLMs, discusses topics like the evolution of AI and LLMs, implementing use cases and cost considerations, categorizing search queries, benchmarking and evaluation, utilizing customer support ticket data, understanding confidence scores, and training models with human feedback.

Rishabh Bhargava, AI and ML expert, discusses his background in data and AI. The hosts also explore refuel's mission to make reliable data accessible to teams and businesses.

Highlights from this week’s conversation include:Viren’s background in data (0:39)Evolution of Orchestration (1:52)AI Orchestration (3:00)Understanding Conductor and orkes (6:26)Event-Driven Orchestration (8:10)Viren’s Transition to Founder (12:27)Non-Technical Aspects of Being a Founder (15:50)Democratizing AI for Developers (18:16)The evolution of microservices orchestration (21:56)Challenges in appealing to the 99% developer group (24:32)Value of orchestration for developers (30:31)Role of orchestrators in managing faults (37:37)The intersection of AI and orchestration (40:27)Evolution of AI (44:04)Thriving in AI Environment (47:58)Final thoughts and takeaways (51:25)The Data Stack Show is a weekly podcast powered by RudderStack, the CDP for developers. Each week we’ll talk to data engineers, analysts, and data scientists about their experience around building and maintaining data infrastructure, delivering data and data products, and driving better outcomes across their businesses with data.RudderStack helps businesses make the most out of their customer data while ensuring data privacy and security. To learn more about RudderStack visit rudderstack.com.

In this bonus episode, Eric and Kostas preview their upcoming conversation with Viren Baraiya of orkes.io.

Data systems experts Wes McKinney, Pedro Pedreira, Chris Riccomini, and Ryan Blue discuss the concept of composable data systems, the challenges and incentives for composable components, specialization and modularity in data workloads, and the efficiency and common layers in data management systems. They also explore the evolution of data system composability, exciting new projects in data systems, and the challenges of standardizing APIs.