IoT initiatives generate massive volumes of high-frequency, high-cardinality sensor data. Yet many organizations struggle to turn that continuous stream of machine data into real-time operational intelligence. Data lands in silos. Queries slow down as cardinality grows. Analytics pipelines become complex and expensive. And feeding AI models with fresh, contextualized data remains a challenge.
In this webinar, we explore how to transform raw IoT and sensor data into real-time insights using CrateDB’s distributed SQL architecture, purpose-built for machine data.
You will learn how to
- Ingest high-throughput sensor streams with millisecond indexing
- Manage structured, semi-structured, and JSON data in a unified model
- Run complex aggregations and search queries across billions of time-series records
- Handle high-cardinality dimensions such as device IDs, locations, or asset hierarchies
- Scale seamlessly from edge deployments to cloud clusters
- Feed AI and ML models with fresh operational data without building fragile pipelines
We will demonstrate a practical IoT use case, showing how CrateDB enables
- Real-time dashboards for fleet, manufacturing, or energy monitoring
- Geospatial queries for asset tracking
- Predictive maintenance analytics
- Unified observability across distributed devices
- Unlike traditional time-series databases that limit flexibility or require separate systems for search and analytics, CrateDB combines distributed SQL, automatic indexing, full-text search, and horizontal scalability in a single platform.
Whether you are building smart manufacturing systems, connected fleets, energy monitoring platforms, or industrial IoT applications, this session will provide architectural guidance and concrete implementation patterns to simplify your data stack and accelerate insights.
Who Should Attend
- Data architects building IoT platforms
- Engineering leaders responsible for telemetry and sensor data
- DevOps and platform teams managing machine data pipelines, Product teams developing data-driven IoT applications
- Innovation leaders exploring AI on operational data
Takeaways
Attendees will leave with:
- A reference architecture for scalable IoT analytics
- Best practices for high-cardinality sensor data modeling
- Query optimization strategies for real-time aggregations
- Practical insight into integrating AI with live operational data