No Clocks

Instead of including examples directly in the documentation, you can put them in separate files and use @example path/relative/to/package/root to insert them into the documentation.

The traditional approach will likely bring us problems both on the front-end side and on the backend side. On the front-end side we end up having leakage of business logic into the UI (ie. when we put use case logic in a controller or view, making it unreusable in other UI screens) or even leakage of the UI into the business logic (ie. when we create methods in our entities because of some logic we need in a template).

A port is a consumer agnostic entry and exit point to/from the application. In many languages, it will be an interface. For example, it can be an interface used to perform searches in a search engine. In our application, we will use this interface as an entry and/or exit point with no knowledge of the concrete implementation that will actually be injected where the interface is defined as a type hint.

An adapter is a class that transforms (adapts) an interface into another. For example, an adapter implements an interface A and gets injected an interface B. When the adapter is instantiated it gets injected in its constructor an object that implements interface B. This adapter is then injected wherever interface A is needed and receives method requests that it transforms and proxies to the inner object that implements interface B.

The adapters on the left side, representing the UI, are called the Primary or Driving Adapters because they are the ones to start some action on the application, while the adapters on the right side, representing the connections to the backend tools, are called the Secondary or Driven Adapters because they always react to an action of a primary adapter.

The Modern Geospatial Data Stack: Trends, Tools, and What They Mean for You

In this post, I’ll break down the key trends shaping the modern geospatial data stack, highlight the tools and platforms that are leading innovation, and explain what this means for practitioners, teams, and organizations.

File Formats and Catalogs: The Foundation of Cloud-Native Geospatial

Modern analytics workflows are no longer small, local projects—they’re massive, distributed, and data-heavy. That’s why cloud-native file formats and data catalogs are at the center of the stack.

Apache Iceberg and other table formats are becoming the backbone of large-scale geospatial data management.

Cloud-optimized formats (like GeoParquet and COGs) make spatial data portable, efficient, and accessible.

Specialized systems like Earthmover are also adding focus for specific file types, in this case climate data

What this means for you: If you’re still relying on ad hoc file storage, you’re missing out on performance and scalability. Learning how to use catalogs like Iceberg lets you fully leverage file-level optimizations, versioning, and schema evolution—critical for handling large and evolving geospatial datasets.

Data Processing: Beyond the Spatial Join

For years, the hallmark of a spatial database was the ability to run a point-in-polygon query. But in 2025, that capability has been commoditized. Most OLAP systems and modern databases can handle these joins at scale—even without compute layers optimized for geospatial.

The real differentiator now is advanced geospatial processing: Zonal statistics for climate and land-use analysis Mobility data pipelines for transportation and urban planning Feature engineering for AI and machine learning workflows

Platforms like Wherobots and Coiled are focusing directly on these workloads, while Apache Spark has begun supporting vector data types. Traditional relational databases still play a role—especially as AI applications demand fast transactional access—but the future belongs to systems that optimize for large analytical queries across massive datasets.

👉 What this means for you: Stop thinking of “point-in-polygon” as the benchmark. Systems that can go deeper—into advanced feature generation and distributed geospatial computation—will define the next generation of spatial analytics.

Transformation and Orchestration: Moving Beyond Simple Scripts

In the past, geospatial data pipelines often relied on one-off Python scripts. Today, that approach simply doesn’t scale.

Specialized spatial ELT tools like Seer AI and BigGeo are emerging to handle geospatial-specific transformations.

Orchestration platforms such as Apache Airflow and Astronomer are essential for managing dependencies, scheduling, and ensuring upstream data integrity.

👉 What this means for you: Don’t think of orchestration as overhead—it’s how you guarantee reliable and reproducible data pipelines. If your team is serious about analytics, orchestration is no longer optional.

Analytical Tools: From Niche to End-to-End

The analytics ecosystem for geospatial continues to expand, giving users more choice than ever. Specialized platforms: Foursquare, Dekart, Superset, Preset End-to-end systems: CARTO and Fused, which combine geospatial with AI, data management, and visualization 👉 What this means for you: The decision is no longer “which GIS platform do I use?” Instead, it’s about picking the right tool for the specific stage of your workflow—sometimes a lightweight visualizer, sometimes a comprehensive enterprise solution.

GIS: The Rise of Web-Native Platforms Web GIS is where most of the visible innovation is happening. Platforms like Felt and Atlas are reimagining the GIS experience: collaborative, browser-based, and designed for simplicity without losing power. 👉 What this means for you: Expect the center of gravity in GIS to continue shifting from desktop to the web. Professionals who adapt to these tools will be better positioned for collaborative, cloud-based work environments.

AI: A New Category of Geospatial Tools One of the most exciting areas is the emergence of AI-native geospatial platforms. These tools are building with machine learning and agentic AI in mind from the start. Vertical-focused AI: Aino (planning), Contour (cities) GIS-focused AI: Bunting Labs, optimizing traditional GIS workloads with AI Agentic AI for geospatial: Klarety and Monarcha, building agents as spatial tools 👉 What this means for you: AI isn’t just an add-on anymore—it’s a defining capability. Expect to see AI-powered agents and models become critical in workflows from automated labeling to decision support.

Python Ecosystem: Expanding AI and Spatial ML Python remains the glue of modern geospatial, and the ecosystem keeps growing: TorchGeo has matured into an independent framework for spatial deep learning. GeoAI from Dr. Qiusheng Wu provides new capabilities for applying ML to spatial data. 👉 What this means for you: If you’re serious about geospatial and AI, Python is unavoidable. The tools are expanding, and open-source continues to lead the way.

Final Takeaway: Where the Modern Geospatial Stack is Headed The geospatial data stack is no longer about static maps or one-off analyses. It’s about: Scalable architectures (Iceberg, GeoParquet, COGs) Advanced processing (beyond spatial joins) Reliable pipelines (orchestration + transformation) AI-native design (feature engineering, agents, ML-ready workflows) The modern stack is maturing into a foundation for spatial intelligence at scale. If you’re a GIS professional, data engineer, or analyst, now is the time to expand your toolkit—because the organizations that master this new stack will define the future of geospatial.