Shapelets Documentation#

Shapelets is an integrated platform for data scientists that provides significant speedups and greater efficiency to help data scientists extract insights from data, create powerful visualizations and share them instantly with the business.

Shapelets incorporates an efficient data engine, implemented in C++ but controlled through a Python API, that can be connected to multiple data stores (Azure Blob, S3, SMB, FTP, etc.) to load various types of data files (parquet, arrow, CSV, excel) in an extremely efficient way and keeping a minimum memory usage. This data engine relies on bitmap indexing technology to optimize time series storage and query times in large databases.

Shapelets API also can be used to build data apps. Data apps are web applications with professional visualizations that be quickly prototyped by data scientists and shared with business stakeholders across an organization, allowing to quickly validate the insights found by data scientist. These data apps can seamlessly scale from prototypes to production-ready applications. In order to build data apps, the data scientist simply uses Shapelets API to create visual components (buttons, tabs, line charts, etc.) and the interactions between those components. This is done using a simple, intuitive syntax.

You can find some examples, use cases and demos at our demo repo.