Space Exploration Problem

Your quick notes are very strong and show the kind of decompositional thinking Palantir loves. You’ve hit the core pillars:

✅ Users and stakeholders
✅ Goals and metrics
✅ Core product functionality
✅ Security (ABAC)
✅ Nonfunctional constraints (latency, bandwidth, safety)

That said, here’s how you can sharpen it just a bit further — both for clarity and to impress the interviewer with structured systems thinking:

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✅ Polished Breakdown (Use This to Speak in the Interview) §

🔹 Who are the users? §

• Primarily: Field scientists and exploration teams (1000 individuals)

• Secondarily: Gov analysts viewing exploration progress, biodiversity reports

🔹 Who are the decision makers? §

• Government partner (sponsors the mission, sets biodiversity goals)

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🎯 Core Functionality (Expanded) §

1. Species Cataloging

   • Users can log new species with metadata: images, tags, coordinates, environmental factors

   • Optional: integrate image recognition ML to suggest matches

2. Exploration Guidance

   • Map UI that shows “underexplored” areas (based on coverage gaps)

   • Suggests areas based on strategic or ecological value

3. Risk Detection

   • Device-level notifications if user enters zone with:

     • Dangerous species

     • Unidentified zones (low data density)

   • Low-latency alerts critical for safety

4. User Access Control

   • ABAC: Scientists may only upload/view from assigned sectors

   • Analysts only see aggregate dashboards, no raw sensitive data

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📏 Success Metrics §

Metric	Why It Matters
% of planet surface explored	Breadth of coverage
# unique species identified	Catalog depth
% of high-risk zones avoided	Safety effectiveness
Avg latency for risk alerts	System responsiveness

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⚙️ Non-Functional Constraints (Well Done!) §

• 🚨 Limited bandwidth: Must work offline with periodic sync

• 📶 Unreliable connectivity: Use store-and-forward model (cache locally → sync later)

• ⚡ Latency-critical alerts: On-device rule engine or mesh network for peer-to-peer alerting

• 🔐 Security: Encrypted local storage + ABAC for scoped access

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🚀 What You Can Say to Impress §

“I’d build a field-first system that assumes disconnected operation — using mobile devices with offline-first design, local caching, and a lightweight mesh or delay-tolerant network for syncing. We’d have an ABAC-secured backend that syncs logs, species data, and exploration telemetry to a central system when bandwidth allows.

For safety, I’d push critical species/zone data to user devices in advance so that real-time alerts can fire even when disconnected.”

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Let me know if you want to walk through:

• A system architecture diagram for this scenario

• A JSON format for species entries + user roles

• A FastAPI/Flask microservice for catalog + sync logic

Here’s everything you asked for:

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📊 1. Architecture Diagram §

You can walk through this in the interview:

• Field Devices: Run offline-first app with local storage and sync logic

• Central Sync Gateway: Acts as the bridge for syncing data when connection becomes available

• Services: Handle species cataloging, exploration coverage, and real-time risk alerts

• ABAC Policy Engine: Governs access for users and dashboards

• Gov Dashboard: Pulls from all services with scoped access for each stakeholder

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🧾 2. Sample JSON Formats §

🐛 Species Entry §

{
  "species_id": "spc-99827",
  "name": "Glowleaf Viper",
  "classification": "animal",
  "description": "Bioluminescent reptile found near water bodies",
  "location": { "lat": -3.154, "lon": 120.832 },
  "image_url": "https://cdn.planetx/species/glowleaf.jpg",
  "submitted_by": "user_102",
  "timestamp": "2025-04-23T13:00:00Z"
}

🧑 User Role + ABAC Attributes §

{
  "user_id": "user_102",
  "role": "field_scientist",
  "region": "zone_alpha",
  "clearance_level": "confidential"
}

📜 Access Policy (example with OPA) §

package access

allow {
  input.user.role == "field_scientist"
  input.user.region == input.resource.region
  input.resource.sensitivity == "confidential"
}

---

⚙️ 3. FastAPI Microservice Skeleton §

from fastapi import FastAPI, Depends, HTTPException
from pydantic import BaseModel
import datetime
 
app = FastAPI()
 
# Mock ABAC check
def abac_check(user, resource):
    return user["region"] == resource["region"]
 
class SpeciesEntry(BaseModel):
    species_id: str
    name: str
    location: dict
    submitted_by: str
 
@app.post("/species")
def submit_species(entry: SpeciesEntry, user: dict = Depends(get_current_user)):
    if not abac_check(user, {"region": "zone_alpha"}):
        raise HTTPException(status_code=403, detail="Access denied")
    # Store in DB logic here
    return {"status": "Species submitted", "timestamp": datetime.datetime.utcnow()}

get_current_user() would typically parse a JWT and extract user attributes for ABAC.

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Would you like a gov dashboard UI mock next (Map + Region Coverage + Data Table), or code for risk alert worker?

Designing apps to work offline — especially in remote or unreliable environments like the planet exploration scenario — requires a careful balance between local functionality, data consistency, and eventual sync. Here’s a complete breakdown of how offline-capable apps are designed:

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🛰️ How to Design Apps for Offline-First Use §

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🧱 1. Core Principles §

Principle	Description
Local-first storage	All user interactions write to a local DB immediately
Background sync	Periodically syncs to cloud when connection is available
Conflict resolution	Determines how to resolve changes from multiple sources
Optimistic UX	UI reflects changes instantly, assumes sync will succeed
Resilience to failure	Data is never lost on crash or restart, even mid-sync

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🧰 2. Technical Components §

Component	Tech Examples	Purpose
Local DB	SQLite, IndexedDB, Realm, PouchDB	Offline persistence
Sync Queue	Custom job queue or background task scheduler	Ensures ordered syncing
Conflict Resolver	CRDTs, operational transforms, last-write-wins	Data consistency
Connection Watcher	navigator.onLine, ping-based health checks	Detects re-connection
Push Mechanism	MQTT, WebSocket, Polling	Sync-triggering in near real time

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📲 3. Example Flow for Your Use Case §

Let’s say a field scientist logs a new species:

[ User clicks 'Submit New Species' ]
      ↓
✅ Local write to SQLite/IndexedDB
✅ App shows species in UI immediately (optimistic)
🕓 Queued for sync → stored in local queue
🔁 Background service detects internet
📡 Pushes data to cloud
✅ Cloud confirms write → marks as synced locally

If the app is restarted mid-sync? → queue is still persisted, and sync resumes.

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🔄 4. Sync Strategy §

• Push-only: From user to server (e.g., new entries, logs)

• Pull-only: From server to user (e.g., latest danger zones, map overlays)

• Bi-directional: Handle both + merge conflicts (e.g., shared notes, updates)

For most exploration apps, push from user, pull for metadata is enough.

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🔐 5. Offline Security §

• Encrypt data at rest (local DB encryption)

• Encrypt sync traffic (HTTPS, signed payloads)

• ABAC policies enforced both on device and server during sync

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✅ Summary for Interview §

“We’d use an offline-first architecture where local storage handles all writes, and a background sync service pushes data when connectivity is available. We’d design the system to survive crashes, ensure idempotent writes to the backend, and sync only deltas. For safety, users would get real-time feedback from the local DB, not rely on server responses.”

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Would you like:

• A sample local storage schema?

• A Python/JS background sync worker example?

• Or a conflict resolution policy write-up?