Introducing Parana a cloud-based data science platform to support a model-driven data ecosystem
Capture
Complex enterprises require a data capture strategy to ensure that data can be captured from different sources. This includes ensuring that data is not only moved and appropriately stored at scale, but can also be annotated for both active use and preservation. WRC has developed a cloud-based platform to capture your data at scale.
Organize
Diverse data requires an organizational strategy that allows it to be both structured and annotated using an enterprise data model. As data increases in diversity, it is important that a data ecosystem can be extended. WRC has developed a multi-layer data model that integrates with Parana allowing it to be extended to organize diverse types of data for an enterprise.
Analyze
Capturing and annotating data is an important precursor to using data. This includes the ability to integrate data pipelines as part of the capture process as well as to develop ad hoc analytic pipelines that can be run to support analysis. Data from different sources often needs to be brought together to generate new analysis results. WRC’s Parana platform integrates on-demand computation which can integrate different machine learning and statistical capabilities. Further, WRC integrates with modern data science frameworks including Jupyter Notebooks to allow for user-driven analysis capabilities to be directly integrated with Parana’s cloud-based platform.
Results
Parana provides API-based accessed as well as a suite of tools that can maximize the effectiveness of working with big data infrastructures on the cloud. This includes optimizing where computation is performed. Parana integrates with modern data science frameworks including Jupyter Notebooks to allow for user-driven analysis capabilities including analytics and visualization to be directly integrated with Parana’s cloud-based platform.