Predictive Maintenance

Identify failures with Chalk's data unification, enrichment, and high-performance serving
Bring everything together in one place

Predicting and preventing downtime requires a broad dataset from many different systems. Chalk’s platform combines streaming data from sensors, historical data in traditional databases, and data from external APIs into a single feature set that data-engineers and -scientists can enrich overtime and integrate into their predictive models.

High Performance Analysis
Compute close to the data

Chalk’s query planner intelligently pushes computations down to data systems when possible, and only does computation on data in-memory when necessary. The minimum amount of data travels between systems, and Chalk relies on existing infrastructure when possible to keep costs down and performance high.

Across models, in apps, in alert messages

Machine learning can produce amazing insights; if end-users can’t see the data used to generate these predictions, they won’t understand or trust them. Chalk’s platform provides batch and real-time querying in Python, Java, Typescript, and on the command line so that all your systems can access the same data no matter the use case.

Empower your teams with risk-free tools

With Chalk’s branch deployments and historical backfill capabilities, developers and data scientists can continue to innovate on models and features as deployed systems continue running securely. When improvements are ready deploy them and back-fill data with simple commands.

Get Started with Code Examples
Unlock the power of real-time data pipelines
Predictive Maintenance
Device Data
Easily listen to streaming data and parse messages with custom logic.
Predictive Maintenance
Failing Sensors
Combine batch, caching, and DataFrames to create a powerful predictive maintenance pipeline.
Predictive Maintenance
Sensor Streams
Compute streaming window aggregate functions on sensor data.
Predictive Maintenance
Historical Data
Access historical sensor data as-of any time in the past.