Summary
In this conversation, the founders of Bauplan, Jacopo and Ciro, share their extensive backgrounds in AI and data infrastructure, discussing the evolution of NLP and the challenges faced in the industry. They highlight the importance of data pipelines in AI effectiveness and the complexities of building data infrastructure.
The discussion also covers lessons learned from previous ventures, the shifting dynamics of the AI market, and the need for collaboration between data scientists and engineers. They emphasize the significance of simplicity in data tools and the future of data management focusing on standardization and accessibility.
In this episode
If you care about making AI features shippable by regular software teams—not just data specialists—this conversation maps the terrain and the trade-offs.
Chapters
00:00 Introduction to Bauplan and Founders' Background
02:27 The Evolution of NLP and AI Challenges
05:05 Shifts in Data and AI Application
07:56 Lessons from Previous Ventures
10:20 The Search Market Landscape
13:05 Behavioral Data's Role in Search
15:52 Building Data Infrastructure vs. Applications
18:22 The Complexity of Data Management
21:03 Bridging the Gap Between Data Science and Engineering
23:39 Challenges in Infrastructure Development
29:52 Navigating the Infrastructure Landscape
32:19 The Pendulum of Centralization and Decentralization
34:00 The Need for Standardization in Data Infrastructure
36:52 Simplifying Data Workflows
40:29 Radical Simplicity in Data Management
45:28 Overcoming Resistance to Change
48:50 The Future of Data Abstractions and Git for Data
Summary
Brett — founder & CEO of Micro — joins Nitay and Kostas to share how he’s turning email into a knowledge graph and rebuilding CRM right inside the inbox. He traces a path from Google’s M&A and Allo product team to Clearbit and Launch House, then digs into why most “inbox zero” workflows fail, how interoperability and AI agents shift power to the interface, and what it takes to design an email experience people actually live in.
What you’ll learn
Chapters
00:00 Brett's Journey: From Consulting to Tech Innovator
02:41 The Role of Strategy in Tech Companies
05:16 Understanding M&A: Successes and Failures
07:55 The Evolution of AI in Corporate Strategy
10:26 Transitioning to Product Management
13:19 Lessons from Clearbit: Culture and Growth
15:50 The Impact of Burnout on Career Choices
18:15 Finding Fulfillment in Entrepreneurship
21:09 Navigating the B2B Landscape
23:34 The Necessity of Products in a Crisis
33:24 The Unexpected Layoff and New Beginnings
34:39 The Launch House Experience
37:16 Transforming Reality into an Accelerator
39:17 The Evolution of Founders and Content Creation
41:52 Introducing Micro: A New Email Experience
47:02 Extracting Information for Better Workflows
53:49 Integrating with Existing Ecosystems
01:01:16 The Future of Email and AI
Summary
Steve Klabnik has spent the last 15 years shaping how developers write code—from teaching Ruby on Rails to stewarding Rust’s explosive growth. In this wide-ranging conversation, Steve joins Kostas and Nitay to unpack the forces behind Rust’s rise and the blueprint for developer-first tooling.
Whether you’re chasing segfault-free code, dreaming up a new PL, or just curious how open-source movements gain momentum, this episode is packed with insight and practical takeaways.
Chapters
00:00 Introduction and Personal Connection
00:59 Journey from Ruby on Rails to Rust
02:21 Early Programming Experiences and Interests
07:20 Community Dynamics in Programming Languages
13:59 The Importance of Community in Open Source
14:37 How Ruby on Rails and Rust Built Their Communities
21:44 Standardization vs. Unified Development Models
30:55 Community Debt in Programming Languages
36:24 Release Cadence vs. Feature Development
37:36 Rust's Unique Selling Proposition
43:30 Attracting Diverse Programming Communities
52:31 The Future of Systems Programming Languages
Summary
Josh Howard, Senior Engineering Manager at Cloudflare, joins Kostas and Nitay to discuss Cloudflare's innovative serverless platform, Durable Objects, and Workers.
Learn how Cloudflare enables developers to build stateful applications with global scale, consistency, and simplicity at the network edge.
Chapters
00:00 Introduction and Background
02:01 Journey into Storage Systems
04:24 Cloudflare's Evolution and Developer Platform
06:29 Understanding Durable Objects
08:57 Durable Objects in Modern App Development
11:18 Use Cases for Cloudflare's Developer Platform
13:36 Building Agents and Real-Time Applications
16:19 Developer Experience and Migration Strategies
25:09 Exploring Workflow Systems: OLAP vs Applications
26:47 Cloudflare's Development Platform: Future Offerings for Data Professionals
28:42 Transitioning from Data Processing to Application Development
31:37 The Impact of LLMs on System Design
33:44 Serverless Platforms: Challenges and Limitations
40:01 Future Directions: Cloudflare's Storage Relay Service and Global Expansion
Summary
In this episode, Erik reflects on his long and storied tech career—from the days of punch cards to founding multiple startups, including a stint at Splunk.
At 61, he offers a unique perspective on how the industry has evolved and shares candid insights into what it takes to build a successful company. He discusses the evolution from building simple tools to creating comprehensive solutions and eventually platforms, emphasizing the importance of starting with a “hammer”—a focused, simple tool—before scaling to a broader offering.
Eril introduces his concept of the “physics of business,” a framework for understanding go-to-market dynamics, pricing, and the critical role of brand in differentiating a product in a crowded market.
He also touches on the challenges of product-led growth, the importance of achieving a strong “K value” (viral or network effects), and the pitfalls of allowing short-term quarterly pressures to derail long-term vision. Toward the end, he hints at his current project, Bestimer, which aims to apply lessons from his past ventures and leverage modern AI to tackle a massive, data-intensive problem.
Chapters
00:00 Erik's Journey Through Tech History
04:06 The Philosophy of Designing for Success
09:49 Understanding the Physics of Business
14:29 Timing and Luck in Startups
18:09 Lessons Learned from Splunk
23:30 The Power of Brand in Business
28:02 Leveraging AI for Brand Development
32:04 The Resilience of Splunk
36:45 Building a Competitive Edge
37:28 From Tool to Solution
40:59 The Importance of Onboarding
44:32 Navigating Growth and Market Fit
51:11 Innovating with AI: The Next Chapter
Summary
In this episode, Gilad Kleinman, co-founder of Epsio, shares his unique journey from PHP development to low-level kernel programming and how that evolution led him to build an innovative incremental views engine.
Gilad explains that Epsio tackles a common challenge in databases: making heavy, complex queries faster and more efficient through incremental materialization. He describes how traditional materialized views fall short—often requiring full refreshes—and how Epsio seamlessly integrates with existing databases by consuming replication streams (CDC) and writing back to result tables without disrupting the core transactional system.
The conversation dives into the technical trade-offs and optimizations involved, such as handling stateful versus stateless operators (like group-by and window functions), using Rust for performance, and the challenges of ensuring consistency.
Gilad also contrasts Epsio’s approach with streaming systems like Flink, emphasizing that by maintaining tight integration with the native database, Epsio can offer immediate, up-to-date query results while minimizing disruption.
Finally, he outlines his vision for the future of incremental stream processing and materialized views as a means to reduce compute costs and enhance overall system performance.
Chapters
00:00 From PHP to Kernel Development: A Journey
07:30 Introducing Epsio: The Incremental Views Engine
10:56 The Importance of Materialized Views
15:07 Understanding Incremental Materialization
19:21 Optimizing Query Performance with Epsio
24:53 Integrating Epsio with Existing Databases
27:02 The Shift from Theory to Practice in Data Processing
29:42 Seamless Integration with Existing Databases
32:02 Understanding Epsio Incremental Processing Mechanism
34:46 Challenges and Limitations of Incremental Views
36:49 The Complexity of Implementing Operators
39:56 Trade-offs in Incremental Computation
41:21 User Interaction with Epsio
43:01 Comparing EPSIO with Streaming Systems
45:09 Architectural Guarantees of Epsio
50:33 The Future of Incremental Data Processing
Summary
In this episode, Viktor Kessler shares his journey and insights from his extensive experience in data management—from building risk management systems and data warehouses to working as a solutions architect at MongoDB and Dremio, and now co-founding a startup.
Initially exploring data mesh concepts, Viktor explains how real-world challenges—such as the disconnect between technical data models and business needs, inconsistent definitions across departments, and the difficulty in managing actionable metadata—led him and his co-founder to pivot toward building a lake house solution.
His startup is developing Lakekeeper, an open source REST catalog for Apache Iceberg, which aims to bridge the gap between decentralized data production and centralized metadata management.
The conversation also delves into the evolution of data catalogs, the necessity for self-service analytics, and how creating consumption-ready data products can transform data functions from cost centers into profit centers.
Finally, Viktor outlines ways for interested listeners to get involved with the Lakekeeper community through GitHub, upcoming meetups, and a dedicated Discord channel.
Chapters
00:00 Introduction to Viktor Kessler and His Journey
04:57 Transitioning from Data Mesh to Lake House
09:15 Understanding Data Mesh: Pain Points and Solutions
13:47 The Role of Metadata in Data Management
18:16 The Evolution of Catalogs and Metadata Management
28:14 Stabilizing the Consumption Pipeline
31:18 Centralizing Metadata for Decentralized Organizations
37:09 Bridging the Gap: Technical and Business Perspectives
43:17 Rethinking Data Products and Consumption
50:45 Finding Balance: Control and Flexibility in Data Management
Summary
In this episode, Apurva Mehta, co-founder and CEO of Responsive, recounts his extensive journey in stream processing—from his early work at LinkedIn and Confluent to his current venture at Responsive.
He explains how stream processing evolved from simple event ingestion and graph indexing to powering complex, stateful applications such as search indexing, inventory management, and trade settlement.
Apurva clarifies the often-misunderstood concept of “real time,” arguing that low latency (often in the one- to two-second range) is more accurate for many applications than the instantaneous response many assume. He delves into the challenges of state management, discussing the limitations of embedded state stores like RocksDB and traditional databases (e.g., Postgres) when faced with high update rates and complex transactional requirements.
The conversation also covers the trade-offs between SQL-based streaming interfaces and more flexible APIs, and how Responsive is innovating by decoupling state from compute—leveraging remote state solutions built on object stores (like S3) with specialized systems such as SlateDB—to improve elasticity, cost efficiency, and operational simplicity in mission-critical applications.
Chapters
00:00 Introduction to Apurva Mehta and Streaming Background
08:50 Defining Real-Time in Streaming Contexts
14:18 Challenges of Stateful Stream Processing
19:50 Comparing Streaming Processing with Traditional Databases
26:38 Product Perspectives on Streaming vs Analytical Systems
31:10 Operational Rigor and Business Opportunities
38:31 Developers' Needs: Beyond SQL
45:53 Simplifying Infrastructure: The Cost of Complexity
51:03 The Future of Streaming Applications
In this episode, we chat with David Jayatillake, VP of AI at Cube, about semantic layers and their crucial role in making AI work reliably with data.
We explore how semantic layers act as a bridge between raw data and business meaning, and why they're more practical than pure knowledge graphs.
David shares insights from his experience at Delphi Labs, where they achieved 100% accuracy in natural language data queries by combining semantic layers with AI, compared to just 16% accuracy with direct text-to-SQL approaches.
We discuss the challenges of building and maintaining semantic layers, the importance of proper naming and documentation, and how AI can help automate their creation.
Finally, we explore the future of semantic layers in the context of AI agents and enterprise data systems, and learn about Cube's upcoming AI-powered features for 2025.
00:00 Introduction to AI and Semantic Layers
05:09 The Evolution of Semantic Layers Before and After AI
09:48 Challenges in Implementing Semantic Layers
14:11 The Role of Semantic Layers in Data Access
18:59 The Future of Semantic Layers with AI
23:25 Comparing Text to SQL and Semantic Layer Approaches
27:40 Limitations and Constraints of Semantic Layers
30:08 Understanding LLMs and Semantic Errors
35:03 The Importance of Naming in Semantic Layers
37:07 Debugging Semantic Issues in LLMs
38:07 The Future of LLMs as Agents
41:53 Discovering Services for LLM Agents
50:34 What's Next for Cube and AI Integration
In this episode, we chat with Yaron Hadad, a fascinating individual who transitioned from theoretical physics to entrepreneurship.
We explore his groundbreaking work on black holes and gravitational waves, and learn about the Ramanujan Machine - an algorithmic system he helped develop that discovers new mathematical formulas and democratizes mathematical research. We'll hear about the scientific community's mixed reactions to this innovative approach.
The conversation then shifts to his work with Neutrino, a company he founded that uses AI and continuous monitoring devices to understand how food affects individual health. We delve into the complexities of nutrition science, the challenges of processing multiple data streams, and the future of personalized health monitoring.
Throughout the episode, Yaron shares insights on bridging theoretical research with practical applications, and the role of AI in advancing both pure mathematics and healthcare.
00:00 Yaron Hadad's Journey: From Physics to AI in Healthcare
04:50 The Complexity of Einstein's Equations and Their Solutions
10:12 AI in Mathematics: The Ramanujan Machine and Conjectures
15:41 Navigating Criticism: The Scientific Community's Response to Innovation
29:24 The Impact of Algorithms in Mathematics
35:30 The Planck Machine: A New Approach
41:15 Neutrino: A Personal Journey in Nutrition
50:11 Connecting Food Complexity to Health Metrics
In this episode, we chat with Philippe Noël, founder of ParadeDB, about building an Elasticsearch alternative natively on PostgreSQL.
We explore the challenges and benefits of extending PostgreSQL versus building a separate system, diving into topics like full-text search, faceted analytics, and why organizations need these capabilities.
We discuss the emerging bring-your-own-cloud deployment model, the state of the PostgreSQL extension ecosystem, and what makes a truly production-ready database extension.
Philippe shares insights on the future of search technology and how recent AI developments are actually increasing the demand for traditional search capabilities.
The conversation also covers the misconceptions around PostgreSQL's scalability and the trade-offs between multi-tenant and single-tenant architectures in modern data infrastructure.
Chapters
00:00 Introduction to ParadeDB and Its Mission
06:35 User-Facing Search and Analytics
11:45 The Role of Postgres in Modern Data Solutions
17:30 Future of Multimodal Databases
31:04 The Rise of Fintech and Data Integrity
36:36 Deployment Models: BYOC and Control Plane
43:41 The Evolution of Cloud Infrastructure and Serverless Databases
49:38 The Future of Search and Community Engagement
In this episode, we chat with Ben, founder of Espresso AI, about his journey from building Excel Python integrations to optimizing data warehouse compute costs.
We explore his experience at companies like Uber and Google, where he worked on everything from distributed systems to ML and storage infrastructure.
We learn about the evolution of his latest venture, which started as a C++ compiler optimization project and transformed into a system for optimizing Snowflake workloads using ML.
Ben shares insights about applying LLMs to SQL optimization, the challenges of verified code transformation, and the importance of formal verification in ML systems. Finally, we discuss his practical approach to choosing ML models and the critical lesson he learned about talking to users before building products.
Chapters
00:00 Ben's Journey: From Startups to Big Tech
13:00 The Importance of Timing in Entrepreneurship
19:22 Consulting Insights: Learning from Clients
23:32 Transitioning to Big Tech: Experiences at Uber and Google
30:58 The Future of AI: End-to-End Systems and Data Utilization
35:53 Transitioning Between Domains: From ML to Distributed Systems
44:24 Espresso's Mission: Optimizing SQL with ML
51:26 The Future of Code Optimization and AI
In this episode, we chat with David Mytton, founder and CEO of Arcjet and creator of console.dev.
We explore his journey from building a cloud monitoring startup to founding a security-as-code company. David shares fascinating insights about bot detection, the challenges of securing modern applications, and why traditional security approaches often fail to meet developers' needs.
We discuss the innovative use of WebAssembly for high-performance security checks, the importance of developer experience in security tools, and the delicate balance between security and latency.
The conversation also covers his work on environmental technology and cloud computing sustainability, as well as his experience reviewing developer tools for console.dev, where he emphasizes the critical role of documentation in distinguishing great developer tools from mediocre ones.
Chapters
00:00 Introduction to David Mytton and Arcjet
07:09 The Evolution of Observability
12:37 The Future of Observability Tools
18:19 Innovations in Data Storage for Observability
23:57 Challenges in AI Implementation
31:33 The Dichotomy of AI and Human Involvement
36:17 Detecting Bots: Techniques and Challenges
42:46 AI's Role in Enhancing Security
47:52 Latency and Decision-Making in Security
52:40 Managing Software Lifecycle and Observability
58:58 The Role of Documentation in Developer Tools
In this episode, we chat with Ivan, co-founder and CEO of Daytona, about the evolution of developer environments and tooling.
We explore his journey from founding CodeAnywhere in 2009, one of the first browser-based IDEs, to creating the popular Shift developer conference, and now building Daytona's dev environment automation platform. We discuss the changing landscape of development environments, from local-only setups to today's complex hybrid configurations, and why managing these environments has become increasingly challenging.
Ivan shares insights about open source business models, the distinction between users and buyers in dev tools, and what the future holds for AI-assisted development. We also learn about Daytona's unique approach to solving dev environment complexity through standardization and automation, and get Ivan's perspective on the future of IDE companies in an AI-driven world.
Chapters
00:00 Introduction to Ivan and Daytona
07:22 Understanding Development Environments
13:59 The User vs. Buyer Dilemma
22:20 Open Source Strategy and Community Building
29:22 How Daytona Works and Its Value Proposition
37:44 Emerging Trends in Collaborative Coding
44:38 Latency Challenges in AI-Assisted Development
50:41 The Future of Developer Tooling Companies
01:02:29 Lessons from Organizing Conferences
In this episode, we chat with Roy Ben-Alta, co-founder of Oakminer AI and former director at Meta AI Research, about his fascinating journey through the evolution of data infrastructure and AI. We explore his early days at AWS when cloud adoption was still controversial, his experience building large language models at Meta, and the challenges of training and deploying AI systems at scale. Roy shares valuable insights about the future of data warehouses, the emergence of knowledge-centric systems, and the critical role of data engineering in AI. We'll also hear his practical advice on building AI companies today, including thoughts on model evaluation frameworks, vendor lock-in, and the eternal "build vs. buy" decision. Drawing from his extensive experience across Amazon, Meta, and now as a founder, Roy offers a unique perspective on how AI is transforming traditional data infrastructure and what it means for the future of enterprise software.
Chapters
00:00 Introduction to Roy Benalta and AI Background
04:07 Warren Buffett Experience and MBA Insights
06:45 Lessons from Amazon and Meta Leadership
09:15 Early Days of AWS and Cloud Adoption
12:12 Redshift vs. Snowflake: A Data Warehouse Perspective
14:49 Navigating Complex Data Systems in Organizations
31:21 The Future of Personalized Software Solutions
32:19 Building Large Language Models at Meta
39:27 Evolution of Data Platforms and Infrastructure
50:50 Engineering Knowledge and LLMs
58:27 Build vs. Buy: Strategic Decisions for Startups
In this episode, we chat with Nitzan Shapira, co-founder and former CEO of Epsagon, which was acquired by Cisco in 2021. We explore Nitzan's journey from working in cybersecurity to building an observability platform for cloud applications, particularly focused on serverless architectures. We learn about the early days of serverless adoption, the challenges in making observability tools developer-friendly, and why distributed tracing was a key differentiator for Epsagon. We discuss the evolution of observability tools, the future impact of AI on both observability and software development, and the changing landscape of serverless computing. Finally, we hear Nitzan's current perspective on enterprise AI adoption from his role at Cisco, where he helps evaluate and build new AI-focused business lines.
03:17 Transition from Security to Observability
09:52 Exploring Ideas and Choosing Serverless
16:43 Adoption of Distributed Tracing
20:54 The Future of Observability
25:26 Building a Product that Developers Love
31:03 Challenges in Observability and Data Costs
32:47 The Excitement and Evolution of Serverless
35:44 Serverless as a Horizontal Platform
37:15 The Future of Serverless and No-Code/Low-Code Tools
38:15 Technical Limits and the Future of Serverless
40:38 Navigating Near-Death Moments and Go-to-Market Challenges
48:36 Cisco's Gen .AI Ecosystem and New Business Lines
50:25 The State of the AI Ecosystem and Enterprise Adoption
53:54 Using AI to Enhance Engineering and Product Development
55:02 Using AI in Go-to-Market Strategies
From GPU computing pioneer to Kubernetes architect, Brian Grant takes us on a fascinating journey through his career at the forefront of systems engineering. In this episode, we explore his early work on GPU compilers in the pre-CUDA era, where he tackled unique challenges in high-performance computing when graphics cards weren't yet designed for general computation. Brian then shares insights from his time at Google, where he helped develop Borg and later became the original lead architect of Kubernetes. He explains key architectural decisions that shaped Kubernetes, from its extensible resource model to its approach to service discovery, and why they chose to create a rich set of abstractions rather than a minimal interface. The conversation concludes with Brian's thoughts on standardization challenges in cloud infrastructure and his vision for moving beyond infrastructure as code, offering valuable perspective on both the history and future of distributed systems.
Links:
Brian Grant LI
Chapters
00:00 Introduction and Background
03:11 Early Work in High-Performance Computing
06:21 Challenges of Building Compilers for GPUs
13:14 Influential Innovations in Compilers
31:46 The Future of Compilers
33:11 The Rise of Niche Programming Languages
34:01 The Evolution of Google's Borg and Kubernetes
39:06 Challenges of Managing Applications in a Dynamically Scheduled Environment
48:12 The Need for Standardization in Application Interfaces and Management Systems
01:00:55 Driving Network Effects and Creating Cohesive Ecosystems
Click here to view the episode transcript.
In this episode, we chat with JP, creator of FizzBee, about formal methods and their application in software engineering. We explore the differences between coding and engineering, discussing how formal methods can improve system design and reliability. JP shares insights from his time at Google and explains why tools like FizzBee are crucial for distributed systems. We delve into the challenges of adopting formal methods in industry, the potential of FizzBee to make these techniques more accessible, and how it compares to other tools like TLA+. Finally, we discuss the future of software development, including the role of LLMs in code generation and the ongoing importance of human engineers in system design.
Links
FizzBee
FizzBee Github Repo
FizzBee Blog
Chapters
00:00 Introduction and Overview
02:42 JP's Experience at Google and the Growth of the Company
04:51 The Difference Between Engineers and Coders
06:41 The Importance of Rigor and Quality in Engineering
10:08 The Limitations of QA and the Need for Formal Methods
14:00 The Role of Best Practices in Software Engineering
14:56 Design Specification Languages for System Correctness
21:43 The Applicability of Formal Methods in Distributed Systems
31:20 Getting Started with FizzBee: A Practical Example
36:06 Common Assumptions and Misconceptions in Distributed Systems
43:23 The Role of FizzBee in the Design Phase
48:04 The Future of FizzBee: LLMs and Code Generation
58:20 Getting Started with FizzBee: Tutorials and Online Playground
In this episode, we chat with Dean Pleban, CEO of DagsHub, about machine learning operations. We explore the differences between DevOps and MLOps, focusing on data management and experiment tracking. Dean shares insights on versioning various components in ML projects and discusses the importance of user experience in MLOps tools. We also touch on DagsHub's integration of AI in their product and Dean's vision for the future of AI and machine learning in industry.
Links
DagsHub
The MLOps Podcast
Dean on LI
Chapters
00:00 Introduction and Background
03:03 Challenges of Managing Machine Learning Projects
10:00 The Concept of Experiments in Machine Learning
12:51 Data Curation and Validation for High-Quality Data
27:07 Connecting the Components of Machine Learning Projects with DAGS Hub
29:12 The Importance of Data and Clear Interfaces
43:29 Incorporating Machine Learning into DAGsHub
51:27 The Future of ML and AI
In this episode, Kostas and Nitay are joined by Amey Chaugule and Matt Green, co-founders of Denormalized. They delve into how Denormalized is building an embedded stream processing engine—think “DuckDB for streaming”—to simplify real-time data workloads. Drawing from their extensive backgrounds at companies like Uber, Lyft, Stripe, and Coinbase. Amey and Matt discuss the challenges of existing stream processing systems like Spark, Flink, and Kafka. They explain how their approach leverages Apache DataFusion, to create a single-node solution that reduces the complexities inherent in distributed systems.
The conversation explores topics such as developer experience, fault tolerance, state management, and the future of stream processing interfaces. Whether you’re a data engineer, application developer, or simply interested in the evolution of real-time data infrastructure, this episode offers valuable insights into making stream processing more accessible and efficient.
Contacts & Links
Amey Chaugule
Matt Green
Denormalized
Denormalized Github Repo
Chapters
00:00 Introduction and Background
12:03 Building an Embedded Stream Processing Engine
18:39 The Need for Stream Processing in the Current Landscape
22:45 Interfaces for Interacting with Stream Processing Systems
26:58 The Target Persona for Stream Processing Systems
31:23 Simplifying Stream Processing Workloads and State Management
34:50 State and Buffer Management
37:03 Distributed Computing vs. Single-Node Systems
42:28 Cost Savings with Single-Node Systems
47:04 The Power and Extensibility of Data Fusion
55:26 Integrating Data Store with Data Fusion
57:02 The Future of Streaming Systems
01:00:18 intro-outro-fade.mp3