Data Integration and Ingestion for AI & LLMs, Architecting Data Flows | changelog 3

In this episode, Kirk Marple, CEO and founder of Graphlit, shares his expertise on building efficient data integrations. Kirk breaks down his approach using relatable concepts: The "Two-Sided Funnel": This model streamlines data flow by converting various data sources into a standard format before distributing it. Universal Data Streams: Kirk explains how he transforms diverse data into a single, manageable stream of information. Parallel Processing: Learn about the "competing consumer model" that allows for faster data handling. Building Blocks for Success: Discover the importance of well-defined interfaces and actor models in creating robust data systems. Tech Talk: Kirk discusses data normalization techniques and the potential shift towards a more streamlined "Kappa architecture." Reusable Patterns: Find out how Kirk's methods can speed up the integration of new data sources. Kirk Marple: LinkedIn X (Twitter) Graphlit Graphlit Docs Nicolay Gerold: ⁠LinkedIn⁠ ⁠X (Twitter) Chapters 00:00 Building Integrations into Different Tools 00:44 The Two-Sided Funnel Model for Data Flow 04:07 Using Well-Defined Interfaces for Faster Integration 04:36 Managing Feeds and State with Actor Models 06:05 The Importance of Data Normalization 10:54 Tech Stack for Data Flow 11:52 Progression towards a Kappa Architecture 13:45 Reusability of Patterns for Faster Integration data integration, data sources, data flow, two-sided funnel model, canonical format, stream of ingestible objects, competing consumer model, well-defined interfaces, actor model, data normalization, tech stack, Kappa architecture, reusability of patterns