Overview

PredictiveWorks. selected Google CDAP for following reasons:

Standardized Abstractions

Google CDAP offers standardized abstractions over common data processing patterns and complex big data technologies. This shifts focus from infrastructure and integration to insights and time to build predictive data pipelines is drastically reduced.

Standardized Plugins

Google CDAP offers standardized APIs to build reusable pipeline components and opens the door to the era of code-free plug-and-play analytics.

Logical & Physical Pipelines

Google CDAP separates data pipelines into a logical and physical representation. Their logical representations define configurations how plugins are organized as workflows, from data sources and transformation, to model building and predictions up to data destinations for insight storage.

Their physical representations specify executable predictive applications that do the hard work to build a certain data model or run for predictions and forecasts.

Implementation

PredictiveWorks. is built on top of Google CDAP and offers an ease of use point-and-click interface to change a pipeline’s life cycle stage from logical to physical ready to run for predictive answers.

This is one of the easiest ways to build and run Apache Spark based big data applications for any uses case without having to write a single line of code.