FLEET_ROUTING: SCALE_TO_ZERO_ACTIVE REGION: GLOBAL_EDGE

Launch 4D Studio

OpenMetadata OGC Features ArcGIS REST Agent Context

Spatiotemporal Micro-Databases

Each project, parcel, or tenant runs in an isolated, zero-latency 4D SQLite instance.

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6standards

cadastre_parcels

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Platform Super Admin Console

Multi-tenant management, cross-database table aggregation, quota allocations, and live data throughput.

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ORGANIZATION / TENANT	TIER	ASSIGNED DATABASES	TABLES	PARCELS	STORAGE / QUOTA	STATUS	ACTIONS

LIVE STEP & DAG EXECUTION STREAM

Interactive SQL Console

Query tables, perform spatial aggregations, and manipulate data with direct 4D SQL execution.

4D SQL QUERY EDITOR Ready

Execute a query to inspect results.

# OpenMetadata / DataHub Ingestion Pipeline Recipe
# Crawls Lyhnidos Geo directly without enterprise agent bloat:
source:
  type: custom-rest-catalog
  service_name: "lyhnidos-geo-service"
  service_connection:
    config:
      hostPort: "http://127.0.0.1:8080"
      catalogEndpoint: "/v1/openmetadata/catalog"
      auth:
        type: bearer
        token: "YOUR_SCOPED_BEARER_TOKEN"
sink:
  type: metadata-rest
  config:
    mode: "upsert"

Click "Test Catalog Probe" to query the live OpenMetadata standard endpoint...

TABLE:

import pyarrow.ipc as ipc
import urllib.request
import polars as pl

# 1. Fetch native binary Arrow IPC RecordBatch stream (zero JSON parsing overhead)
req = urllib.request.Request(
    "http://127.0.0.1:8080/v1/databases/default/tables/cadastre_parcels/arrow",
    headers={"Accept": "application/vnd.apache.arrow.stream"}
)
with urllib.request.urlopen(req) as resp:
    reader = ipc.open_stream(resp.read())
    arrow_table = reader.read_all()

# 2. Ingest directly into Polars DataFrame with ZERO memory copy:
df = pl.from_arrow(arrow_table)
print(df.select(["id", "address", "zoning_code"]))

Click "Test Stream Wire" to stream binary Apache Arrow RecordBatches...

import ibis

# Option A: Direct embedded connection to the Lyhnidos micro-database file
con = ibis.sqlite.connect("lyhnidos_data/default.db")

# Option B: Lazy DataFrame query with deferred execution
parcels = con.table("cadastre_parcels")
query = (
    parcels
    .filter(parcels.zoning_code.isin(["C1Z", "MUZ"]))
    .select("id", "address", "lot_plan", "zoning_code")
    .limit(10)
)

# Execute into Pandas, Polars, or PyArrow
df = query.execute()
print(df)

CLI Quickstart: pip install ibis-framework[sqlite] duckdb pyarrow

import duckdb

con = duckdb.connect()

# Query local micro-database file directly using DuckDB's sqlite_scan:
df = con.execute("""
    SELECT id, address, lot_plan, zoning_code
    FROM sqlite_scan('lyhnidos_data/default.db', 'cadastre_parcels')
    WHERE zoning_code = 'C1Z'
""").df()

print(df)

# Export directly to Parquet or Iceberg Lakehouse:
con.execute("""
    COPY (SELECT * FROM sqlite_scan('lyhnidos_data/default.db', 'cadastre_parcels'))
    TO 'cadastre_export.parquet' (FORMAT PARQUET)
""")

Compatible with MotherDuck, Local DuckDB, dbt-duckdb, and Apache Iceberg pipelines.

STANDARD:

# Standard GIS Endpoints for QGIS, ArcGIS Pro, and Web Maps:

1. OGC API - Features Landing Page:
   http://127.0.0.1:8080/ogc/collections

2. OGC GeoJSON Items Stream:
   http://127.0.0.1:8080/ogc/collections/cadastre_parcels/items?limit=50

3. Esri ArcGIS FeatureServer Endpoint:
   http://127.0.0.1:8080/arcgis/rest/services/default/FeatureServer/0/query?where=1%3D1&outFields=*&f=geojson

Click "Test OGC Items" to query the live OGC API - Features collection...

Configured Automation Jobs

Job Name	Flow Preset	Interval	Target DB	Status	Actions

Upload your business data (Excel .xlsx / .xls, CSV, or GeoJSON). Lyhnidos automatically parses sheets, discovers address columns for spatial geocoding, builds 4D geometry, and streams it straight to your micro-database.

Destination Database

Target Table Name

Drag and drop your Excel, CSV, or GeoJSON file here

Supports Microsoft Excel (.xlsx, .xls), Comma-Separated Values (.csv), and Spatial GeoJSON (.geojson)