June 2017 Summaries

The Neo4j partnership with IBM Power Systems is a significant enterprise solution for companies across industries and continents. The collaboration has led to optimized performance, with IBM Power Systems providing up to 80-90% better price-performance compared to x86 servers. This enables faster data processing and reduced costs. The partnership also includes the addition of CAPI Flash support, allowing for efficient large-scale graph storage without high memory costs. Additionally, the joint efforts have led to exciting projects and clients, with a focus on addressing client needs in areas such as big data and connected enterprises. The relationship between Neo4j and IBM Power Systems is synergistic, bringing together two vendors to jointly address client requirements, resulting in significant progress and growth for both parties.

Firms in the financial services industry need a technology powered by data connections to tackle anti-money laundering challenges, as traditional methods are not designed to connect the dots across multiple intermediate steps. Financial services enterprises are using graph technology, specifically Neo4j, to model companies, accounts, and transactions as a graph to discover instances of money laundering. This allows AML teams to track how and where funds are moving through automated Cypher queries that map to traditional money laundering behaviors. The use of Neo4j has been successfully implemented by one of the world's largest money transfer companies, which moves approximately $600 billion each year, enabling it to comply with stringent anti-money laundering requirements and set a benchmark for investigatory compliance across its industry. By connecting the dots across the globe and across data centers, Neo4j helps create a sustainable competitive advantage over money launderers who would use financial services firms for criminal gain.

This week in Neo4j highlights various community activities, including the launch of Hetionet, a network of biology, disease, and pharmacology, created by Daniel Himmelstein. The Neo4j life sciences and healthcare workshop in Berlin brought together researchers to share their experiences with graph databases. The community also shared analysis of Neo4j tweets, participated in the IEEE Graph Challenge, and showcased projects like Pheno4J, a gene-to-phenotype graph database. Additionally, there are upcoming meetups and events, including presentations on Neo4j at various meetups and online events.

Docker containerization is a popular technology that allows developers to package their applications with all their dependencies, run-time environments, and code into a single unit, which can be easily shipped and deployed on any machine. However, as the number of containers grows, issues like scaling, replication, fault tolerance, and container communication become complex challenges. To overcome these limitations, container orchestration tools are needed to manage multiple containers across a fleet of machines. The presentation discusses three popular container orchestration tools: Docker Swarm, Kubernetes, and Apache Mesos. Each tool has its strengths and weaknesses, and the choice of which one to use depends on specific requirements and needs. Docker Swarm provides simplicity and ease of use, while Kubernetes offers more concepts, abstraction, stability, and maturity. Apache Mesos is a resource allocation manager that can manage both Docker and non-Docker jobs, providing flexibility and scalability. The presentation concludes by emphasizing the importance of choosing the right orchestration tool based on specific needs and requirements.

Himmelstein's PhD research began with the question of how to teach a computer biology, leading him to explore heterogeneous networks (HetNets), which are labelled property graphs. He created a graph with over 2 million relationships using Neo4j and integrated knowledge from public resources, resulting in a HetNet called Hetionet Version 1.0. This network contains 50,000 nodes of 11 types and 2.25 million relationships of 24 types, mined for drug repurposing by identifying patterns in paths that are predictive of treatment or efficacy. The project, called Rephetio, uses machine learning to predict the probability of treatment for all 200,000 compound-disease pairs, with notable successes including predicting bupropion's effectiveness against nicotine dependence. Himmelstein's work showcases the potential of HetNets and Neo4j in biomedical research and drug discovery.

Neo4j is being used by leading financial institutions to model and monitor data about customers, accounts, devices, locations, and other attributes to identify fraudulent activity from fraud rings using synthetic and stolen identities. Traditional relational database technologies are not suitable for this task due to their complexity and scalability issues. Graph databases, like Neo4j, provide an ideal tool for overcoming these hurdles with intuitive query languages like Cypher that enable real-time detection of fraud rings by navigating connections in memory. A Fortune 500 financial services company has seen significant improvements in reducing fraudulent transactions annually after adopting Neo4j's real-time graph analysis and simple data visualization capabilities. The company can now analyze customer information and related data more efficiently, allowing analysts to make fast and accurate decisions, thereby strengthening their position against less-prepared competition.

This week in Neo4j highlights the contributions of community members, including Jasper Blues who is a long-time Neo4j user and contributor. He has authored several projects, including Spring Data Neo4j and Neo4j-OGM, and has recently published an example for using Spring Data Neo4j with Spring Boot and Kotlin. The newsletter also features recommendations from OSCON, static code analysis, Marvel Social Graph, online meetups, new releases on GitHub, and interviews on the Graphistania podcast. Upcoming events include Graph Day San Francisco, a workshop in Berlin, and a presentation by Nikolas Pontikos on Pheno4J: A Gene To Phenotype Graph Database.

The Neo4j JDBC driver is a Java library that enables developers to connect to a graph database using standard JDBC APIs. The driver allows any Java application to run Cypher queries against Neo4j via the standard JDBC API, providing a way for developers to integrate Neo4j with existing applications and tools. A new version of the driver has been released, which includes improvements such as support for the Bolt binary protocol and modularization, allowing for smaller dependencies and easier maintenance. The driver is now available for download on GitHub, and its development team is actively working on testing it with the newly released Neo4j 3.2.0 and adding new examples. The community is encouraged to provide feedback and report bugs through GitHub issues or by joining the neo4j-users Discord channel.

This week in Neo4j highlights the accomplishments of community member Lorenzo Speranzoni, who has been a part of the Neo4j community since 2013 and has contributed to various projects such as Neo4Art and connectors for other databases. Additionally, Christian Miles wrote an article on visualizing crime patterns data as a graph using Neo4j and Keylines. The week also saw the release of new videos from GraphConnect Europe 2017, including a talk on openCypher transactions and analytics with Neo4j and Cypher for Spark. Upcoming events include presentations by Rik Van Bruggen, Christophe Willemsen, Pat Patterson, and Preston Hendrickson at various meetups. The Sinar Project shared an initiative to use open source technology to make Malaysian government transparent and accountable.

AWS has made Neo4j 3.2 Community Edition available on its marketplace, allowing users to quickly deploy and manage their graph database instances using a 1-click experience similar to Amazon EC2. This new deployment option complements the existing launch of Neo4j Enterprise Edition on Microsoft Azure Marketplace, which supports full Causal Clustering and scaling capabilities. Users can currently only access the Community Edition through AWS, but can also use the Enterprise Edition by installing it directly on an EC2 machine running their preferred operating system, with potential future additions to the marketplace based on customer feedback.

This week in Neo4j has seen several exciting developments across the graph database community. Featured community member Niklas Saers was highlighted for his contributions to the porting of Theo, a Neo4j Swift driver, and his ongoing efforts to improve its performance. Wikipedia analysis using Neo4j was also explored by Raj Shrimali, who shared his experiences importing the full dataset into the graph database. The release of Neo4j 3.2 marked an important milestone for the project, with the browser being rewritten using Preact, a fast alternative to React. Several users shared their experiences getting started with graph databases, including James Hughes and Diane Kierce. Additionally, four different versions of Neo4j were released this week, each addressing various issues and features. The community is also looking forward to upcoming events, such as Neo4j GraphDay Amsterdam and Neo4j GraphTalks London, which will focus on use cases, training, and data quality.

Neo4j is a graph database that enables enterprises to transform into dynamic, scalable, and modern organizations by bringing together disparate data from aging infrastructure. This allows companies to glean insights quickly, reducing the need for manual data wrangling and analysis. FactGem provides a platform for starting on Neo4j immediately, allowing users to load their data into Neo4j and build applications. The company's expertise in graph technology helps enterprises overcome challenges such as handling relationships between data entities, scaling data storage, and providing a schema-less database solution. By adopting Neo4j and creating a DataFabric, companies can unlock new insights and value from their data, while keeping their existing investments intact.