# Data Infrastructure Robora's data infrastructure will be a robust, distributed system for collecting, standardizing, and analyzing robotics and IoT data, addressing fragmentation and scarcity head-on. It includes programmable collection engines that ingest multimodal streams (e.g., from sensors or cameras), filtering for quality using the Query Engine's AI scoring (e.g., motion, semantics, diversity). Data is standardized into formats like Parquet for efficient storage and querying, enabling developers to access tailored subsets without manual processing. Technically, the infrastructure uses containerized actions for automation—e.g., indexing topics (time-series sequences like "velocity\_data") and triggering events (time-anchored markers for anomalies). Feasibility relies on off-chain distributed storage (e.g., IPFS or cloud buckets) with cryptographic hashes for integrity, supporting terabyte-scale datasets without latency issues. In a use case, an IoT manufacturer collects fleet sensor data; the engine processes it to detect patterns (e.g., failure predictions), making it available for VLA fine-tuning. Integration with the SDK allows custom extensions, such as edge computing for real-time filtering, ensuring scalability for global deployments while incentivizing contributions via $RBR tokens based on data utility. --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://docs.robora.xyz/features/roboras-architecture-grid-network/data-infrastructure.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.