Detecting Contextual Anomalies with SAP HANA ML

Introduction

What is an Anomaly?

The goal here is to detect outlier data points, which do not follow the collective common pattern of the majority of data points, hence can be easily separated from the group.

Some of the possible use cases here are: 

Data Provisioning in HANA from Various Sources using Python

Why Python 

We have known that Smart Data Access (SDA) is much popular as a Data Provisioning method with SAP HANA to connect to various popular third party sources whether its SQL server, RDBMS etc. You can access data virtually as well as you can persist based on requirements. Flowgraphs plays an important role here.

SAP HANA Calculation View Columns Origin

After using so many Calc.Views that call another bunch of Calc.Views, etc. is really difficult to identify the correct origin (table.column/formula) of some field. This was causing some rework to understand all information needed and to correctly check and model the data

To solve that, I developed a really simple way to connect to HANA, get the dependency data and generates a JSON using Python and jQuery: