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

How AI Is Built

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:

1. The "Two-Sided Funnel": This model streamlines data flow by converting various data sources into a standard format before distributing it.
2. Universal Data Streams: Kirk explains how he transforms diverse data into a single, manageable stream of information.
3. Parallel Processing: Learn about the "competing consumer model" that allows for faster data handling.
4. Building Blocks for Success: Discover the importance of well-defined interfaces and actor models in creating robust data systems.
5. Tech Talk: Kirk discusses data normalization techniques and the potential shift towards a more streamlined "Kappa architecture."
6. 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