Kir Tititevsky: Modern Streaming Architecture Transforming the Service Bus
Kir Titievsky, Product Manager at Google Cloud with extensive experience in streaming and storage infrastructure, joined Yuliia and Dumky to talk about streaming. Drawing from his work with Apache Kafka, Cloud PubSub, Dataflow and Cloud Storage since 2015, Kir explains the fundamental differences between streaming and micro-batch processing. He challenges common misconceptions about streaming costs, explaining how streaming can be significantly less expensive than batch processing for many use cases. Kir shares insights on the "service bus architecture" revival, discussing how modern distributed messaging systems have solved historic bottlenecks while creating new opportunities for business and performance needs.Kir's medium - https://medium.com/@kir-gcpKir's Linkedin page - https://www.linkedin.com/in/kir-titievsky-%F0%9F%87%BA%F0%9F%87%A6-7775052/
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43:05
Serhii Sokolenko: Building a Python-First Compute Platform for Data Engineers
Serhii Sokolenko, founder at Tower Dev and former product manager at tech giants like Google Cloud, Snowflake, and Databricks, joined Yuliia to discuss his journey building a next-generation compute platform. Tower Dev aims to simplify data processing for data engineers who work with Python. Serhii explains how Tower addresses three key market trends: the integration of data engineering with AI through Python, the movement away from complex distributed processing frameworks, and users' desire for flexibility across different data platforms. He explains how Tower makes Python data applications more accessible by eliminating the need to learn complex frameworks while automatically scaling infrastructure. Sergei also shares his perspective on the future of data engineering, noting in which ways AI will transform the profession.Tower Dev - https://tower.dev/Serhii's Linkedin - https://www.linkedin.com/in/ssokolenko/
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55:44
Andrii Yasinetsky: Building an AI Startup & Reshaping Healthcare, AI Democratization and Changes It Brings to Tech Jobs and World Economy
Andrii Yasinetsky, CTO and co-founder of Diadia Health and AI expert, joined Yuliia to share his perspective on the current AI landscape and its future implications. Currently Andrii and his team are building an AI-first healthcare platform focused on metabolic and hormonal health. Andrii talks about how AI is changing both technology stacks and business economics. We discuss what's wrong with AI claims in enterprises and why it doesn't match reality, he points out the decreasing cost of intelligence, and why the middle layer of tech jobs may disappear within five years. Andrii also shares his take on how the recent US administration change has created a "timeline split" that could dramatically accelerate AI innovation, potentially transforming the global economy.Diadia Health - https://diadiahealth.comAndrii's Linkedin - https://www.linkedin.com/in/yasinetsky/
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58:55
Jess Kyle: How Communication, Empathy, and Trust Transform Data Work
Jess Kyle, a Data Engineering Leader with 13 years of experience across startups and enterprises, joined Yuliia to share her mission of making data work more enjoyable for everyone involved. As a leader of a data engineering team at a sports betting company, Jess shared how transparent communication can transform team dynamics and stakeholder relationships. In her work she challenges the common notion of "soft skills" in data, emphasizing that communication is a hard skill that should be screened for in interviews. Jess provides practical insights on managing overwhelm, delivering difficult feedback, and emphasizes why empathy and humility are the two most crucial qualities for data leaders - even though they're often undervalued in the tech industry.Jess's linkedin page - linkedin.com/in/jesskyle
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58:17
Deepti Srivastava: Systems Thinking for Enterprise AI
Deepti Srivastava, Founder of Snow Leopard AI and former Spanner Product Lead at Google Cloud, joined Yuliia to chat what's wrong with current approaches to AI integration. Deepti introduces a paradigm shift away from ETL pipelines towards federated, real-time data access for AI applications. She explains how Snow Leopard's intelligent data retrieval platform enables enterprises to connect AI systems directly to operational data sources without compromising security or freshness. Through practical examples Deepti explains why conventional RAG approaches with vector stores are not good enough for business-critical AI applications, and how a systems thinking approach to AI infrastructure can unlock greater value while reducing unnecessary data movement.Deepti's linkedin - https://www.linkedin.com/in/thedeepti/Snowleopard.ai - http://snowleopard.ai/
Netherlands