^{Big Data Analytics & Data Mining}

Biography

Biography: Puntis Palazzolo

Abstract

In the Age of Insights where we use IoT for smart parking and wearable technology for health monitoring, customers should be able to access the right information at the right time and that requires access and processing of Big Data. With more than 40 years in the enterprise software market and almost 20 years of experience developing in-memory computing solutions, at SAP we have experienced the below shortcomings in the area of Big Data Analytics:

Companies struggle to bring various types of Big Data such as structured together with the semi or unstructured data such as graph, time series, spatial, and text together and be able to run machine learning algorithms across them seamlessly.

Data scientists have their preferred language such as R or Python and want to use both in the same piece of code. They are looking for a platform which allows them to experiment and implement and is language-agnostic.

Development of production-level systems are more difficult than developing a proof of concept and running experiments. Implementing the orchestration, scheduling, monitoring, and automation is still a big challenge.

In the area of Big Data what matters is how to elastically extend or reduce the computation and storage power as needed without sabotaging the work done.

Customers need to work and access data in the cloud, on-premise or both with a little effort.

There are so many great open-source libraries out there. The users’ concern is how to benefit from them without being worried about the compatibility of the libraries and how to handle the versioning and support.

Our goal is to address some of these obstacles and share some success stories in the area of Big Data Analytics. We will unravel our journey toward our Big Data solution and walk you through the examples of our platform and invites you to rethink the traditional means.