Data scientists access the C3 IoT Platform using the tools and frameworks they are familiar with - like Jupyter Notebook, RStudio, Scala, and Spark. Each tool or framework is tightly integrated to the C3 Type System. Practitioners no longer need to spend significant time exporting data from different databases, exploring a subset of the data, or writing complex code to merge different datasets into cohesive data frames.
Once they develop their algorithms, the C3 IoT Platform provides powerful capabilities to enable those algorithms to run robustly, in production, at-scale, and at optimal costs - without requiring complex re-architectures.
The C3 IoT Platform brings data science and application development closer together: Data scientists and application developers are able to access the same underlying datasets and processing frameworks at scale, through a simple object model interface.
Speeds up data science work: Data scientists are able to leverage the C3 Type System. This reduces the need to understand underlying databases, write SQL/CQL queries, or worry about underlying processing infrastructure.
Accelerates model deployment into production enterprise applications: Data scientists are able to develop their algorithms using the C3 IoT platform and perform model training against production data. Once models are tuned and trained, pipelines can be tested and promoted into production.
Reduces maintenance and speeds up upgrade cycles: Data scientists and architects no longer have to maintain individual, custom, expensive analytic pipelines. All analytics pipelines are directly developed against the C3 Type System and use underlying C3 IoT Platform infrastructure which manages all the infrastructure including processing (e.g., continuous or stream processing), auto-scaling, API access, ACLs, etc. Even large and complex models with thousands of feature inputs can have feature modifications and algorithm changes deployed rapidly.