Make Devices Think: Embedded Data Management for Real-World of IoT and AI

By Sasan Montaseri

Founder

ITTIA

November 04, 2025

Blog

Image Credit: ITTIA

Human thinking mirrors data management: we ingest sensory inputs like a device sampling sensors, filter and index what’s relevant through attention, hold items in working memory like caches/queues, and store experiences with context and schema (who/what/when/why) for long-term recall. When we reason, we query and join fragments to form a picture; when we learn, we update models, so future inferences are faster and better. We also forget or compress low-value details to stay agile, and we practice metacognition, monitoring errors and drift, to self-correct. In short, the quality of our thought depends on how well we capture, organize, retrieve, and refine information, the very foundations of good data management.

Intelligent devices “think” by following the same loop, just in silicon. Sensors ingest signals (often via ISR/DMA) into an embedded database that gives deterministic writes, time-stamped context, and power-fail safety. An integrated database runs stream processing pipelines (windowing, filters, normalization) to turn raw samples into compact features, then an on-device machine learning model (rules, anomaly score, classifier, etc.) performs inference to decide and act in milliseconds. Results and key history records are indexed (time, keys) for fast recall and explanation, while observability tracks drift, errors, and resource health. With selective sync, only summaries/outliers/hard cases go upstream for retraining, and updated models come back OTA, closing the collect/store/transform/infer/act/learn loop that makes devices feel intelligent.

Devices can use either microprocessors (MPUs) or microcontrollers (MCUs) cores to process information. Think of an MCU like a person with very limited working memory but great focus: they can only hold a few facts at once, so they need tight routines, deterministic ingestion, tiny fixed buffers, simple time-series/log layouts, and lightweight indexes. MCU devices favor on-the-spot decisions (rules, tiny models), precomputed features, and strict so nothing spills when inputs spike; persistence must be power-fail safe and flash-friendly. MPUs are like someone with a big memory plus a notebook and library: they keep rich histories, multiple indexes (time, keys), run heavier analytics/ML, and juggle concurrent tasks without losing track. They can afford schema evolution, join-heavy queries, caching layers, and batch pipelines, and coordinate fleets via selective sync. In short: MCUs prize predictability, tiny footprints, and bounded latency; MPUs deliver breadth, depth, and analytical flexibility, together forming a tiered data system from reflex to reasoning.

In practice, developers choose MCUs primarily for cost, power, and tight real-time control; otherwise, MPUs are the easier path. But data management on MCUs is a different world than on MPUs: you have kilobytes of RAM, limited flash with wear-out concerns, strict power-fail safety needs, and hard latency budgets, plus no MMU and often a real-time operating system (RTOS.) A database for MCUs must deliver deterministic behavior, a tiny footprint, careful write amplification control, performance, flash-aware durability and several other important factors. That’s why it took us years of focused R&D to get this right. Not every “embedded database” is built for microcontrollers.

We think of the ITTIA DB Platform as a complete “digital body” information management for device intelligence: sensors feed raw signals like eyes and skin; ITTIA DB Lite (for MCUs) acts like the spinal cord and cerebellum, delivering reflex-level determinism, balance, and durability under stress; time-first storage and windows provide the device’s biological clock and attention rhythms; ITTIA DB (for MPUs) plays the cortex and hippocampus, organizing long-term memory with rich indexing for fast recall and reasoning; ITTIA Data Connect is the nervous system and bloodstream, securely sharing only essential information across the organism; and ITTIA Analitica serves as prefrontal metacognition, watching vitals, spotting drift, and guiding adjustments. Together, they let devices collect, store, transform, infer, and improve with the same integrated discipline that makes human thinking effective.

Now devices can say: ITTIA, I Think, Therefore I Am.