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The explosion of data created by the pervasive use of the Internet in our daily life - such as IoT, usage of Smartphones to name just two - the ability to track and store events in near real-time and the increasing demand to personalize services and communication are drivers for change in the enterprise world.

Not only the challenges related to the growth in the amount of data, but also the needs to take advantage of the connectedness within the data and therefore coping with the increasing complexity to turn data into value do drive enterprise to rethink existing approaches.

Turning data into value gets more and more a competitive differentiator on the market, has fundamentally changed the economy of the 21st century and has created new information technology giants as well as transforms existing industries and market players into information companies.

Additional requirements to move away from „islands of information“ taking advantage of the deeply connected data result in the need to create polyglot architectures. Polyglot is defined as “someone who speaks and writes several languages” and in the context of architectures translates into using the optimal architecture, tools and infrastructure to fulfill the requirements.

A polyglot architecture is used when there is a need to model a complex architecture by breaking the architecture into segments and applying different architecture models. It is then necessary to further refine the overall business needs, define and analyze the subtasks, find optimal solutions fulfilling the goals to then aggregate the results.

New technologies such as Graph Databases support this new architecture approach by leveraging the technology of graph theory and structure, model, store and access the data natively as a graph by efficiently using the connections within the data. Graph databases overcome the burden of relational databases where connections need to be dissolved via “joins” and data models are not as intuitive when compared to graph data models.

Beside the technical benefits, the agility using Neo4j leads to being able to iteratively extend a polyglot architecture by adding use case after use case to the overall solution. Clearly alternative approaches using RDBMS for data storage need a extensive first phase defining a corporate data model compared to a schema free Neo4j database which can be extended or changed at any point in time without additional effort.

In this session you will learn about state of the art polyglot architectures and how they can help customers to address current and upcoming needs, enriched with examples and case studies of companies going this route using Neo4j as part of their infrastructure.