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:

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

Show Notes

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:

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

What is How AI Is Built ?

How AI is Built dives into the different building blocks necessary to develop AI applications: how they work, how you can get started, and how you can master them. Build on the breakthroughs of others. Follow along, as Nicolay learns from the best data engineers, ML engineers, solution architects, and tech founders.