This project aims to implement the vision of Visual TS - Code-free orchestration of data pipelines (or workflow) to respond to analysis use cases for time series data. Working with time series data often suffers from missing entries. Interpolate is a CDAP computation plugin that addresses this issue for Apache Spark DataFrames. A frequent requirement for many time series analysis methods is that the data need to be stationary (i..e mean, variance and auto correlation structure do not change of time). For practical purposes, stationarity is usually determine from linear auto correlation functions (ACF).

This project aims to implement the vision of Visual DL - Code-free orchestration of data pipelines (or workflow) to respond to deep learning use cases. Works DL is based on Intel's Analytics Zoo and makes distributed deep learning available as CDAP data pipeline plugins. Model building and prediction stages can be mixed with other plugins from CDAP-Spark plugin foundation.

CDAP Spark is an all-in-one library that externalizes Apache Spark based machine learning, deep learning, complex event processing and more in form of plugins for Google CDAP data pipelines. It boosts the work of data analysts and scientists to build data driven applications without coding. Externalization is an appropriate means to make advanced analytics reusable, transparent and notably secures the knowledge how enterprise data are transformed into insights, foresights and knowledge. Corporate adoption of machine learning or deep learning often runs into the same problem. A variety of existing (open source) solutions & engines enable data scientists to develop data models very fast.