How AI Is Built 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:
Nicolay Gerold:
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