Graph databases and managing metadata with Neo4j

SPEAKER BIO

Kaushik Chaubal

Kaushik Chaubal is a software developer in BlackRock’s Core Software Infrastructure team. The team is responsible for enhancing and supporting developer productivity and Aladdin’s platform stability within BlackRock. He has been with BlackRock since early 2012 and has a masters degree in Electrical and Computer Engineering from Carnegie Mellon University, Pittsburgh.

Ioana-Alexandra Ifrim

Ioana-Alexandra Ifrim works as a software developer in BlackRock’s Core Transaction Processing Group. The team is responsible for product development in Aladdin’s order management and trading space. She joined BlackRock in August 2014 and studied Computer Science at the University of Manchester.

James Phare

James Phare has over 15 years of experience with creating and leading data teams in various roles in Financial Services. Prior to cofounding data consultancy firm Data to Value, he was Head of Information Management and Data Architecture at Man Group – one of the world’s largest Hedge funds. James started his career at Thomson Reuters after graduating in Economics from the University of York.

MEETUP SUMMARY

There are multiple Graph-based databases available today – both open-source as well as commercial. The basic fundamentals of most graph databases are similar while the underlying storage mechanism and the processing engine used to create these graph-structures vary between different implementations.

On May 25th, 2016, the Neo4j London User Group hosted a meetup on Graph Databases at BlackRock.

In the first half of the talk, Kaushik and Ioana, both software developers at BlackRock, walked us through the fundamentals of graph databases, their storage mechanisms and processing engines. The talk also included demos on implementations and examples of some popular graph databases, such as Neo4j, Titan and Sparksee.

In the second part of the meetup, James from Data to Value discussed how we can use graph technology and more specifically – Neo4j, in order to address some of the major challenges and questions of metadata management such as impact analysis, data lineage and definitions.

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