Eric Flanigam — Generative Value - The Unstructured Data Landscape - January 2025
FreeExploring the infrastructure for the new wave of AI applications built on unstructured data.
FreeFree tier
LinksX
About Eric Flanigam — Generative Value - The Unstructured Data Landscape - January 2025
An in-depth analysis of the unstructured data landscape, covering sources, ingestion, processing, storage, and end-use. Discusses historical trends, current tools (e.g., Apache Spark, Databricks, Snowflake), and the role of unstructured data in emerging fields like robotics, autonomy, and AI agents. Written by Eric Flaningam, an investor, as part of the Generative Value newsletter.
Key Features
Overview of unstructured data ecosystem
History of NoSQL databases and data processing
Categories: sources, ingestion, processing transformation, storage, end use
Discussion of open-source and distribution networks
Relevance to robotics, autonomy, AI agents
Pros & Cons
Pros
- Comprehensive overview of unstructured data ecosystem
- Insightful historical context
- Connects data trends to emerging AI applications
- Written by an investor with market perspective
Cons
- Article is a high-level analysis, not a technical deep dive
- Opinions and predictions may not be accurate
- Focuses more on market trends than technical implementation
Best For
Understanding the unstructured data market landscapeIdentifying investment opportunities in data infrastructureLearning about technologies for processing large-scale unstructured dataContext for building applications in robotics and autonomy
FAQ
What is unstructured data?
Data not stored in tabular format in SQL databases, including text, images, audio, video, sensor, and geospatial data.
What are the main categories in the unstructured data landscape?
Sources, ingestion, processing transformation, storage, and end use.
Why is unstructured data important now?
The new wave of AI applications (robotics, autonomy, agents) is built on unstructured data, and textual data is tapped out.