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

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

Update: 2024-06-25
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Description

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

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

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

Nicolay Gerold