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hy This Matters? The scale, ambition and complexity of India’s rare-earth strategy demand unified data architectures, real-time intelligence, automation across the value chain, and traceability and compliance all of which align strongly with MonkDB’s core capabilities. For the customers (sensor + geospatial + mine planning) + MonkDB, this presents a value-chain-wide play from exploration to manufacturing to recycling by enabling data-driven autonomy and supply-chain resilience in the rare-earth domain.Opportunities Domestic demand for clean-tech (EVs, wind turbines, defence) is rapidly growing REEs are foundational to these industries. India can leverage this demand to create a home-grown supply-chain rather than remain dependent on imports. India Brand Equity Foundation Potential for India to become a regional hub for REE manufacturing and supply-chain services given its large reserves, growing industrial capacity and government support. Leveraging allied nations, global incentives, and partnerships to accelerate technology transfer and scale manufacturing. Implications for Stakeholders For mining and technology companies: Major opportunity to invest in India’s rare-earth value chain — from extraction to magnet production. For policy-makers and infrastructure planners: Need to coordinate exploration, processing, manufacturing, environmental & regulatory frameworks. For system integrators and data/AI-platform vendors (like MonkDB): The full value chain provides opportunities for deploying industrial data architectures, processing plants, digital twins, autonomous control, and trace-ability across REE operations.
Why This Cheat Sheet is Valuable? Provides concise reference guidance for both new and experienced users of MonkDB. Bridges the gap between architecture, implementation, and operations — helpful for product adoption, training, and rollout. Tailored specifically for industrial use-cases (mining, rare earth, heavy industry) — not just general database usage. Supports rapid onboarding of development, implementation and operations teams with standardised patterns and checklists.
The video begins with an installation segment guiding through prerequisites, installing MonkDB on a server (or edge), connecting the Pgwire and REST endpoints, configuring TLS. It then moves into data ingestion, showing how to connect to SCADA/Historian systems, sensor streams, RTLS tags, geospatial maps, documents and vector search inputs. Next it covers schema design and unified storage: how MonkDB handles SQL tables, time-series tables, vector indexes, document stores and geospatial layers all within one engine. The tutorial demonstrates writing queries across modalities (e.g., SQL + vector search for hyperspectral images, time-series analysis for ventilation fans) to extract insights. Further, it shows agent setup defining a MonkAgent, binding it to data sources, encoding domain logic (e.g., airflow/gas rules for mining), configuring decision policies, and deploying the agent in advisory mode. The video then transitions into semi-auto/closed-loop mode—how to shift from recommendations to automatic setpoint changes, monitor the loop, capture audit logs. Finally, the tutorial shows dashboard & analytics, M&V reports, compliance logs, traceability of decisions, and outlines best practices for scaling across sites.
In this video, we dive into the fascinating world of parallel AI agent orchestration using MonkDB. Learn how to leverage MonkDB’s powerful framework to efficiently manage and coordinate multiple AI agents working together in parallel to tackle complex tasks. We'll cover: What MonkDB is: An introduction to MonkDB and how it enhances AI agent performance. Parallel AI Agents: How to set up and orchestrate multiple AI agents running in parallel. Scalability & Efficiency: How MonkDB can help scale AI systems and improve processing efficiency. Practical Use Cases: Real-world examples of MonkDB in action, from multi-agent systems to collaborative decision-making. By the end of the video, you'll understand how to implement and optimize parallel AI agents within MonkDB, making your AI applications more powerful and efficient. Don’t forget to like, subscribe, and hit the bell icon for more AI-related content!
Learn how to leverage agentic AI with MonkDB
In today’s connected world, mobility drives innovation. Meet FleetMonk, powered by MonkDB, an AI native multi-model and distributed OLAP DataOS, FleetMonk is an intelligent edge-driven fleet management platform that’s transforming how businesses move, connect, and perform.
