Spatial Stack with Matt Forrest

There are trillions of dollars invested in the physical world every da: infrastructure, supply chains, and our planet.

Yet many of these massive decisions are made without the data to back them up. For too long, geospatial analytics has been gated behind specialized teams and siloed technology, treated as "spatial is special" rather than just another data type.

In this episode, we sit down with Damian Wiley from Wherobots to break down how cloud architecture is finally closing this gap. With a heavy-hitting background from AWS EC2 and Databricks, Damian explains the shift from transactional databases to the Lakehouse architecture and why "Zero ETL" is the holy grail for data engineering.

We dive deep into why spatial data shouldn't be gated, how open table formats like Iceberg are changing the game, and why the future involves AI agents that can directly query the physical world.

If you are a data engineer, developer, or leader looking to unlock location intelligence without the headache of complex infrastructure, this conversation is for you.

✅ Sign Up for Wherobots: https://wherobots.com/
✅ Learn more about Apache Sedona: https://wherobots.com/apache-sedona/
✅ What is Apache Sedona: https://wherobots.com/blog/what-is-apache-sedona/
✅ Test out SedonaDB: https://sedona.apache.org/sedonadb/latest/
✅ Connect with Jia on LinkedIn: https://www.linkedin.com/in/wyliedamian/

00:00 - The Trillion Dollar Data Gap: Investing in the physical world without intelligence
02:15 - From AWS EC2 to Geospatial: Damian’s journey from cloud infrastructure to spatial data
06:40 - "Spatial is Special" No More: Breaking down silos and making spatial data "just data"
09:00 - The Lakehouse Advantage: Decoupling storage and compute for economic agility
12:15 - Fragmented History: Why geospatial tech became so compartmentalized
17:30 - Real-World Impact: Optimizing supply chains and climate response with frequent data
22:45 - The Economics of Analytics: Lowering the Total Cost of Ownership (TCO) for pipelines
28:30 - AI Agents & The Physical World: Connecting LLMs to ground-truth reality
37:00 - Compute Strategy: When to use OLAP vs. OLTP for spatial workloads
46:00 - Zero ETL & The Future: How Iceberg and open standards enable interoperability
51:20 - Getting Started with SedonaDB: Vibe coding and the future of spatial queries

📰 Daily modern GIS insights: https://forrest.nyc

CONNECT WITH ME
📸 Instagram: https://www.instagram.com/matt_forrest/
💼 LinkedIn: https://www.linkedin.com/in/mbforr/
📧 Newsletter: https://forrest.nyc
🌐 Website: https://forrest.nyc

Large Language Models can write poetry and debug code, but they still don't understand the fundamental physics of the real world. Ask an AI to find the "nearest restaurant" to a specific coordinate, and it struggles because it lacks Spatial Intelligence.
In this episode, we sit down with Jia Yu, the co-creator of Apache Sedona and co-founder of Wherobots, to discuss why geospatial data breaks standard big data engines and how he built the solution that now powers over 2 million downloads a month.
We trace the 10-year journey from a PhD research paper to a top-level Apache project, diving into the deep technical challenges of distributed computing. Jia explains why spatial data requires a completely different architecture than standard text or numbers and how the industry is finally moving toward a "Spatial Lakehouse" to break down data silos.
In this episode, we explore:
- The "Multimodality" Trap: Why mixing vector, raster, and LiDAR data crashes traditional systems.
- How SedonaDB is bringing massive scale to single-node machines (so you don't always need a cluster).
- The hardest problem in distributed computing - How to split a map across 1,000 servers without breaking the data.
- The multi-year fight to get native geometry support into Apache Iceberg.
- Why the next generation of models must evolve from text-based to spatially intelligent.
✅ Sign Up for Wherobots: https://wherobots.com/
✅ Learn more about Apache Sedona: https://wherobots.com/apache-sedona/
✅ What is Apache Sedona: https://wherobots.com/blog/what-is-apache-sedona/
✅ Test out SedonaDB: https://sedona.apache.org/sedonadb/latest/
✅ Connect with Jia on LinkedIn: https://www.linkedin.com/in/dr-jia-yu/ 
00:00:00 - Intro & Welcome 
00:00:51 - The Origin Story: From GeoSpark to Apache Sedona 
00:06:03 - Why Geospatial Data is "Special" (The Multimodality Problem) 
00:09:47 - When to Move to Distributed Computing? 
00:13:21 - The Secret to Maintaining a Vibrant Open Source Community
00:18:11 - The Features That Drove Adoption: Spatial SQL & Python 
00:22:35 - Deep Dive: How Spatial Partitioning Works 
00:28:57 - Why Build a Cloud-Native Platform? 
00:33:05 - The Rise of the Spatial Lakehouse & Apache Iceberg 
00:40:17 - Introducing SedonaDB: A Single-Node Engine 
00:45:10 - The Future: Why AI Needs Spatial Intelligence 
00:48:44 - Advice for Getting Started with Spatial Engineering
📰 Daily modern GIS insights: https://forrest.nyc
CONNECT WITH ME
📸 Instagram:  https://www.instagram.com/matt_forrest/
💼 LinkedIn: https://www.linkedin.com/in/mbforr/
📧 Newsletter: https://forrest.nyc
🌐 Website: https://forrest.nyc

In 1963, the US Postal Service introduced "Mr. Zip" to make mail delivery faster. They never intended for those five digits to determine your insurance premiums, your home value, or your health outcomes.
In this short deep-dive, we explore how an arbitrary logistical tool became a shorthand for community and why that’s dangerous. From the misleading boundaries of Dallas, Texas, to the tragic data failures during the Flint water crisis, we uncover the real story behind the map.

Listen in to learn why it's time to move beyond the zip code and start looking at the details that actually matter.

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Whenever you’re ready, here are 3 ways I can help you:
🎓 Modern GIS Accelerator: The step-by-step roadmap to master Python, Spatial SQL & Cloud workflows. Stop just "making maps" and start building spatial solutions. 👉 https://forrest.nyc/accelerator/
🧪 The Spatial Lab: Join the top 5% of geospatial professionals in our private community. Get access to exclusive courses, mentorship, and the network you need to level up. 👉  https://forrest.nyc/spatial-lab/
📰 Daily modern GIS insights: https://forrest.nyc
CONNECT WITH ME
📸 Instagram:  https://www.instagram.com/matt_forrest/
💼 LinkedIn: https://www.linkedin.com/in/mbforr/
📧 Newsletter: https://forrest.nyc
🌐 Website: https://forrest.nyc

Maps have been around for thousands of years, but what they represent and how they work is changing faster than ever.
In this episode, I’m joined by Cliff Allison, who has spent more than 30 years building enterprise-scale mapping systems for governments and global organizations. Today, he leads government global sales at TomTom, helping bring modern, AI-powered mapping infrastructure to some of the most demanding use cases in the world.
We talk about how maps have evolved from static snapshots into living systems that update continuously, how open standards and collaboration made global mapping possible at scale, and why machines are now increasingly interacting with maps and with each other.
We also explore what this shift means for defense, intelligence, humanitarian response, and decision-making, and why mapping is no longer just a visualization layer, but a foundational system for understanding and predicting the world.
If you work in geospatial, data, AI, or infrastructure, this conversation will change how you think about maps.
---
Whenever you’re ready, here are 3 ways I can help you:
🎓 Modern GIS Accelerator: The step-by-step roadmap to master Python, Spatial SQL & Cloud workflows. Stop just "making maps" and start building spatial solutions. 👉 https://forrest.nyc/accelerator/
🧪 The Spatial Lab: Join the top 5% of geospatial professionals in our private community. Get access to exclusive courses, mentorship, and the network you need to level up. 👉  https://forrest.nyc/spatial-lab/
📰 Daily modern GIS insights: https://forrest.nyc
CONNECT WITH ME
📸 Instagram:  https://www.instagram.com/matt_forrest/
💼 LinkedIn: https://www.linkedin.com/in/mbforr/
📧 Newsletter: https://forrest.nyc
🌐 Website: https://forrest.nyc

We have an incredible amount of public geospatial data—high-resolution elevation, weather forecasts, floodplain maps, real-time sensors—yet most people still can’t easily answer a simple question:
“What’s my flood risk right here, right now?”
In this episode, I’m joined by Kevin Bullock, an aerospace engineer and remote sensing expert at Development Seed, to talk about how he turned years of geospatial expertise into Hydra Atlas, a mobile app designed to make flood risk understandable and accessible for everyday users.
We explore why so much critical data remains difficult to use, how Kevin pulled together datasets from FEMA, NOAA, and USGS, and why mobile—not web—was the right platform for this problem. Kevin also shares what it was like building a geospatial app with Swift, testing real-world use cases, and designing an interface that prioritizes clarity over complexity.
This conversation goes beyond flooding. It’s about modern GIS, product thinking, open data, and what happens when geospatial professionals stop building tools for other experts and start building tools for people.
If you’re interested in geospatial product development, public data, mobile mapping, or turning complex systems into usable software, this episode is for you.
Download HydraAtlas: https://apps.apple.com/us/app/hydraatlas/id6749492232
Follow Kevin on LinkedIn: https://www.linkedin.com/in/kevbullock/
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Whenever you’re ready, here are 3 ways I can help you:
🎓 Modern GIS Accelerator: The step-by-step roadmap to master Python, Spatial SQL & Cloud workflows. Stop just "making maps" and start building spatial solutions. 👉 https://forrest.nyc/accelerator/
🧪 The Spatial Lab: Join the top 5% of geospatial professionals in our private community. Get access to exclusive courses, mentorship, and the network you need to level up. 👉  https://forrest.nyc/spatial-lab/
🧭 Career Compass: Not sure where to start? Get the fast, practical steps to land the GIS role you actually want. 👉 https://forrest.nyc/career-compass/
📰 Daily modern GIS insights: https://forrest.nyc
CONNECT WITH ME
📸 Instagram:  https://www.instagram.com/matt_forrest/
💼 LinkedIn: https://www.linkedin.com/in/mbforr/
📧 Newsletter: https://forrest.nyc
🌐 Website: https://forrest.nyc