Real Estate Development Models — developer 0.3.0 documentation
What is GIS? | Geographic Information System Mapping Technology
Find the definition of GIS. Learn how this mapping and analysis technology is crucial for making sense of data. Learn from examples and find out why GIS is more important than ever.
DB schema design guide | PostgresAI
Database design principles and best practices for PostgreSQL schema design
publiclab/leaflet-environmental-layers: Collection of different environmental map layers in an easy to use Leaflet library, similar to https://github.com/leaflet-extras/leaflet-providers#leaflet-providers
Collection of different environmental map layers in an easy to use Leaflet library, similar to https://github.com/leaflet-extras/leaflet-providers#leaflet-providers - publiclab/leaflet-environmenta...
GIS
🐘 PostgreSQL Power User Cheatsheet: The Guide for DBAs & Developers
🚀 Supercharge your PostgreSQL skills! This cheatsheet covers architecture, performance tuning, advanced SQL, JSONB, MVCC, security, replication & essential commands. A must-have for DBAs & Developers looking to master Postgres.
Character encodings: Essential concepts
Introduces a number of basic concepts needed to understand other articles that deal with characters and character encodings.
pgModeler - PostgreSQL Database Modeler
Open source data modeling tool designed for PostgreSQL. No more DDL commands written by hand. Let pgModeler do the job for you!
Leaflet Environmental Layers
Collection of different environmental map layers in an easy to use Leaflet library.
jc5577/armcop: ArcGIS Map Server Copy To GeoJson
ArcGIS Map Server Copy To GeoJson
PostGIS Data Ingestion — CartoLab
PostGIS Data Ingestion
Clockwork Micro: Map tools that work like clockwork | Clockwork Micro
Map tools that work like clockwork. Map Tools. Modular, Interactive, Low code/no code
Dbdbml
tabde/R/utils-sfmisc.R at master · s-fleck/tabde
Work with table designs (col_names, col_types) in csv format - s-fleck/tabde
tabde/R/as_sql.R at master · s-fleck/tabde
Work with table designs (col_names, col_types) in csv format - s-fleck/tabde
From API to Database: A Step-by-Step Guide on Efficient Data Integration
A helpful step-by-step guide to learn about the benefits of integrating APIs with databases and how to do it.
The integration process typically follows a request-response cycle where client applications send HTTP requests to API endpoints, which then execute corresponding database operations. Middleware layers often handle authentication, rate limiting, and data transformation between API responses and database schemas.
Modern integrations also support batch processing for high-volume operations and implement caching strategies to reduce database load while maintaining data freshness.
Command Query Responsibility Segregation (CQRS)
CQRS separates read and write operations into distinct models, optimizing each for its specific purpose. In API-database integrations, this pattern allows you to design write-optimized database schemas for data ingestion while maintaining read-optimized views for query performance.
Event-Driven Architecture
Event-driven patterns complement traditional request-response API integrations by enabling asynchronous processing and loose coupling between systems. Instead of direct API calls to databases, events trigger database operations, allowing for better fault tolerance and scalability.
Microservices Database Patterns
The database-per-service pattern ensures each microservice maintains its own database, preventing tight coupling while enabling independent scaling and deployment. When implementing API-database integrations in microservices architectures, each service exposes its own API for data access rather than allowing direct database connections.
How Do You Implement Change Data Capture and Real-Time Synchronization?
Change Data Capture (CDC) represents a fundamental shift from traditional batch processing to real-time data synchronization, enabling organizations to react immediately to data changes and maintain current information across systems.
Storing External Requests
I’ve worked in payment systems for a long time, and one common theme is that they make a lot of 3rd party API calls. For example, there are numerous payment networks and banks, fraud and risk checks, onboarding and KYC/KYB (Know Your Customer/Business) services, and more. And then there are the inbound calls as well, such as webhooks and callbacks from integration partners, e-commerce stores, and other outside interactions.
The first step is storing all inbound and outbound API calls. People will often use logs for this, but I think it’s far more valuable to put them in the database instead. This makes them easier to query, easier to aggregate, and easier to join against other data.
You can use one table for both inbound and outbound or separate them depending on preferences, but generally it’s useful to store most of the available information, such as:
URL of the request
Datetime of the request
Request body
Response body
Response status/code (e.g. 200, 500)
Total time spent on the request
Request headers
Response headers
Metadata
For metadata, I like to use a JSON column and add in any metadata that links this request to an entity in the system. For example, it could include user_id, order_id, request_id, etc. As a JSON column, you can include more than one, and even include other complex nested information.
Response headers often include extra debugging information, such as request or trace Ids, version numbers, etc. It’s common when asking for 3rd party support to provide these values so they can go look in their own logging to find your requests.
Rather than try to write code for every API call, it’s often better to hook into the request/response lifecycle in one place and instrument all calls.
The way you do this depends on the language and libraries, but they are generally called interceptors.
Where possible, I like to record the request fields before the outbound call is started (e.g. request body, request headers) and then go back and update the row to store the response fields once the call is completed. There are several advantages over a single write at the end of the request cycle:
feddelegrand7/rlowdb: Lightweight, JSON-based database for R 🦩🦩🦩
Lightweight, JSON-based database for R 🦩🦩🦩
Customising condition messages — topic-condition-customisation
Various aspects of the condition messages displayed by abort(), warn(), and inform() can be customised using options from the cli package.
mkquartodocs
mkquartodocs extension
Understanding Slope and Aspect (Terrain Analysis)
Slope and Aspect (Terrain Analysis)
Slope 101
Slope is an important data point in insurance, enabling companies and agents to accurately price their policies.
PostGIS Performance: pg_stat_statements and... | Crunchy Data Blog
PostGIS performance basics. Second post in a series covering pg_stat_statements, shared buffers, work_mem, and parallel queries.
Using Cloud Rasters with PostGIS | Crunchy Data Blog
Paul shows you how to access raster data stored in the cloud or object storage for PostGIS using cloud optimized GeoTIFF (aka COG) files. He also includes some functions for working with raster elevation.
How to Determine Land Value: A Comprehensive Guide | AcreValue
Learn how to determine land value with key factors and methods. Explore how to utilize informed decisions with data-driven insights.
FEMA Flood Zones: Deep Dive | AcreValue
Explore AcreValue’s FEMA Flood Zones map layer to assess flood risk, protect investments, and make informed land decisions with real-time floodplain data.
Farmland Values, Soil Survey, & GIS Maps | AcreValue
View maps featuring agricultural data, sold land, and land for sale. Discover farmland values, soil productivity, crop mix, and parcel ownership.
R: Calculate slope and aspect from elevation data.
Digital Soil Mapping with R – digital-terrain-analysis
USGS 3DEP LiDAR Point Clouds - Registry of Open Data on AWS