April 2019 Summaries

This free Neo4j training course provides an update to the original version, reflecting changes in managing and configuring clustering, security, and logging in Neo4j 3.5. The course focuses on administrators of the Enterprise Edition and covers essential tasks such as managing a Neo4j instance, performing backups, and configuring Causal Clusters. It also delves into security settings, monitoring, and logging for a deployed application. With hands-on exercises that can be done on Linux, OS X, or Windows, students will gain proficiency in best practices for Neo4j Administration, including managing clusters, plugins, and security configurations.

Graphs are a versatile and dynamic technology that can help solve complex problems faced by government agencies. They offer benefits at scale, such as storing data for the U.S. Army's strategic assets management system and recalling NASA's lessons learned over 50 years. Graph databases like Neo4j enable deep and complex queries, reduce infrastructure costs, maximize value from existing resources, deliver immediate answers at scale, and meet security demands. They are particularly useful in criminal investigations, where connected data can point to potential suspects and contextualize their activities and associations. By leveraging graph technology, government agencies can iterate and expand on current datasets, gaining momentum for bigger ideas and deeper contextual meaning in the data.

This week's Neo4j community newsletter highlights various topics such as using graphs to fight diabetes and an overhaul of the graph visualization tools page. Featured community member Amy Hodler is introduced, who has been educating the community about graph analytics and AI for two years. Dr. Alexander Jarusch presented on combining multiple data sources to build a knowledge graph that can answer questions about diabetes and other diseases. The graph visualization tools page has also been overhauled by Jennifer Reif, breaking down available tools into three categories and taking users on a guided tour of each category. Additionally, Vinodh Subramanian shows how to consume and produce events for RabbitMQ using Neo4j, while Michael Hunger shares tips and tricks for using the Arrows graph modeling tool.

EY has embarked on an ambitious graph enterprise AI and machine learning initiative to uncover fraudulent activities more effectively. They've chosen graph technology for its simplicity in visual constructs, semantic information, and speed up the development process. The cost of computing has fallen precipitously over the last two decades, making graphs a perfect scale-up solution for cloud-based storage. Graphs are popular due to their ability to connect data across multiple domains with processes that rely on relationships and dependencies to uncover patterns. EY is working towards applying AI and machine learning to graph models to assign additional context, inferences or domain knowledge to make better decisions. A knowledge graph is the perfect tool for achieving these goals. Graphs are becoming increasingly popular due to their growing popularity, cost-effectiveness, and ability to drive growth and interest from the community of practitioners. Traditional relational databases will eventually be replaced by graphs as they compete on speed and relevance. Graphs are a natural fit for semantic representation that's easy to develop and understand. They provide a common data fabric that allows pulling up and connecting important data, and through Neo4j APIs, driving applications. The customer 360 use case is one of the most common graph use cases due to its difficulty in enterprise settings. The B2B use case involves master data management challenges. Financial use cases involve identifying rare patterns like money laundering through machine reasoning inference. Graphs are also useful for network optimization and providing additional context through implied relationships. EY aims to implement features such as current AML processes, graph analytics, and deep learning models using Neo4j at scale. Ingesting large data is done by building graph-form tables of nodes and mappings that can be uploaded into Neo4j efficiently. Advanced analytics can be used to make use of Neo4j data, and productionalizing analytics can be easily achieved. To identify whether or not you have a graph problem, consider questions such as getting better customer understanding, mobilizing and syndicating data, business value from existing data, and the next best action for your company.

Graphs are a versatile and dynamic tool used to solve complex problems in various domains, including government agencies. They offer benefits at scale, particularly when dealing with massive amounts of data, as seen in the U.S. Army's graph database for managing strategic assets. Graph databases provide a flexible, scalable, and powerful platform for uncovering relationships between data locked in different repositories. In contrast to traditional relational databases, which are better suited for well-understood, aggregated, and minimally connected data structures, graph databases enable organizations to discover connections among data much faster. A native graph database like Neo4j stores and manages data relationships as first-class entities, offering index-free adjacency and allowing connected nodes to physically point to each other. By leveraging graph databases, government agencies can fulfill their mission-critical objectives and tackle complex problems that require traversing data relationships across different applications or repositories.

This week, the Neo4j community showcased various projects and initiatives, including building a content recommendation system using knowledge graphs, augmenting business intelligence with graph power, and analyzing password hashes from two different domains using BloodHound. The community also celebrated Global Graph Celebration Day, which was organized by numerous individuals and communities worldwide. The featured community members were recognized for their efforts in driving the global initiative. Additionally, there were releases of new Neo4j drivers, foreign data wrappers, and updates to the APOC library, as well as a case study on using BloodHound for password analysis.

Developers are using tools like yWorks to create visualizations of graph data in Neo4j, enabling them to explore and understand their data more effectively. These visualizations can be interactive, allowing users to engage with the data in new ways. Developers use these tools to realize their requirements and gain insight into the data, creating applications that offer a better user experience. The future of graph visualization is expected to be exciting, with new ideas and approaches emerging as customers continue to push the boundaries of what is possible with graph databases like Neo4j. As developers create innovative solutions using yWorks and other tools, it's clear that the potential for visualizing data in graph databases is vast and holds great promise for the future.

This video on what Global Graph Celebration Day is, why Euler is so important and how his discovery continues to resonate today.` was shown, along with over 60+ events in 6 continents, led by the community. Locations included Canberra, Australia; Petaling Jaya, Malaysia; London, United Kingdom; Accra, Ghana; Salem, Tamil Nadu, India; Columbia, South Carolina; Rio de Janeiro, Brazil; South Jordan, Utah; Salt Lake City, Utah; Surabaya, Indonesia; Karachi City, Pakistan; Dresden, Germany; Copenhagen, Denmark; Belgrade, Serbia; Chennai, Tamil Nadu, India; Accra, Ghana; Austin, Texas; Mumbai, Maharashtra, India; Dubai, United Arab Emirates; Frankfurt, Germany; Madrid, Spain; Munich, Germany; Grand Rapids, MI; Curaçao; Kanazawa, Ishikawa, Japan; Beijing, China; and other locations. The event was organized by a large number of people from different backgrounds and countries. The Global Graph Celebration Day is an annual event that celebrates the importance of graph databases and their applications in various fields. It was started by Neo4j and has since become a global movement with thousands of participants. The event features talks, workshops, and meetups on graph-related topics, as well as exhibitions and networking opportunities. It is a great opportunity for people to learn about graph databases, network with other professionals, and get involved in the community.

SpecterOps provides adversary simulation and detection tools for companies looking to assess their cybersecurity measures. Their experts have worked in defending government agencies and worldwide enterprises across various industries. Andy Robbins, Resilience Lead at SpecterOps, will discuss how graphs have changed the way hackers attack. He acknowledges prior works on Active Directory ACL Scanner and a French work from ANSSI, and explains that attackers think in graphs while defenders think in lists. Hackers follow a four-step methodology: recon phase, initial access phase, post-exploitation phase, and exfiltration phase. The recon phase involves gathering information about the target system, such as user names, IP addresses, and network topology. The initial access phase aims to gain access to the network by exploiting vulnerabilities or using social engineering tactics. The post-exploitation phase involves escalating privileges and gaining control over the system. Attackers use tools like PowerView and Mimikatz to achieve this. Identity snowball attacks, where attackers exploit user credentials and group memberships, are a common tactic. The BloodHound project automates the process of identifying and exploiting vulnerabilities by creating attack graphs. SharpHound is another tool that collects information about local admin group memberships across the enterprise. Attack path automation, enabled by projects like GoFetch and ANGRYPUPPY, allows attackers to quickly identify and execute the most effective attack paths, making it difficult for defenders to counter them.

The increasing complexity of global supply chains is driven by advances in transportation and communication, enabling the production of more diverse products. Consumers are demanding greater transparency from brands and retailers, requiring businesses to digitize their supply chains to better understand who they are working with, where they are located, and what measures are in place to ensure safe and ethical products. This transparency is crucial for meeting Corporate Social Responsibility (CSR) initiatives, which involve publicly committing to produce goods in a way that is socially responsible and environmentally friendly. Graph technology, such as Neo4j, provides the necessary tools and adaptability to tackle complex, interconnected supply chains and identify areas of risk, allowing businesses to gather information, analyze data, and address social and environmental issues at all levels of their supply chain.

Global Graph Celebration Day marks the birthday of Leonhard Euler, the founder of graph theory, with over 60 worldwide events, and coincides with the release of the O'Reilly book "Graph Algorithms: Practical Examples in Apache Spark & Neo4j." This book aims to simplify the adoption of graph analytics, which historically required significant expertise, by providing practical examples and guidance on using graph algorithms for predictive analytics. It is designed for developers and data scientists to enhance machine learning models and develop intelligent solutions by leveraging the inherent relationships in data. The book offers insights into over 20 graph algorithms, including pathfinding, centrality, and community detection, with sample codes and datasets for Apache Spark and Neo4j. The authors emphasize the power of graph algorithms in revealing predictive elements in data and offer practical advice on avoiding common pitfalls, ultimately aiming to empower users to extract more value from their datasets.

Neo4j has announced a new strategic partnership with Google Cloud, delivering Neo4j as a fully managed service deeply integrated with the Google Cloud Platform. This allows GCP users to access graph superpowers without worrying about operations and management. A new Graph App, NEuler - The Graph Algorithms Playground, was also released, providing support for various algorithms and allowing users to explore graph concepts in an intuitive way. Additionally, a Docker tutorial has been published, as well as a video on using virtual nodes and relationships in Neo4j. Other updates include the release of cartography, a Python tool consolidating infrastructure assets in a graph view powered by Neo4j, and the announcement that Neo4j is now part of the GraphQL foundation.

#GraphCast, a series by Neo4j, highlights intriguing developments in graph visualization and analytics, with a recent feature focusing on Neo4j Bloom, a tool designed for engaging graph data exploration. Jocelyn Hoppa introduces Neo4j Bloom as a visually captivating application that simplifies the presentation of complex data patterns, making it accessible and enjoyable for a wide audience, including professionals who can use it to effectively communicate insights within their organizations. The tool's ability to transform data exploration into a visually appealing and comprehensible experience is underscored through a demonstration by Rik Van Bruggen, who utilizes it to analyze suspicious financial transactions. Neo4j encourages viewers to subscribe to their YouTube channel for more updates and insights into graph technology, emphasizing the aesthetic and practical benefits of using Neo4j Bloom for data visualization and exploration.

UBS created the Group Data Dictionary, a real-time data governance platform to track data origins and lifecycles, in response to the new BCBS 239 regulations. The team originally built the governance platform in Oracle but transitioned to Neo4j due to its natural persistence in handling highly connected data. Despite some challenges, such as circularity, complexity, coupling, and missing standards for graph formats, the UBS team has made progress in implementing a lineage generation algorithm using Neo4j's Cypher query language. The team is working on enhancements to reduce coupling with the presentation layer and improve performance. Despite the difficulties, the Group Data Dictionary has shown promise in ensuring data integrity and accuracy for UBS.

Neo4j has announced a strategic partnership with Google Cloud, offering Neo4j as a fully managed service integrated into the Google Cloud Platform (GCP). This collaboration allows GCP users to leverage Neo4j's graph database capabilities without the complexities of operations and management, thereby enabling them to focus on application development. Google, which successfully employed graph technology to transform web search through its PageRank algorithm, is now making this technology widely accessible to its user base via GCP. Neo4j is optimized for cloud environments and built on Kubernetes, providing seamless integration with the GCP console and billing systems, and ensuring enterprise-grade reliability and speed. This initiative aims to provide developers and enterprises with a native, cloud-optimized experience, enhancing the ability to manage and query connected data efficiently. The offering is expected to become publicly available by the end of 2019, marking a significant step in making graph technology more accessible across industries.

Graph algorithms are powerful tools that can be used to analyze and make sense of connected data, as demonstrated by Yelp's dataset challenge. The Yelp dataset contains over 5 million reviews, 1.1 million users, and 150,000 businesses, making it an ideal platform for testing graph algorithms in practice. The dataset is represented as a graph model, where users are labeled nodes with friends relationships, users write reviews and tips about businesses, and metadata is stored as properties of nodes. Graph algorithms such as PageRank can be used to find influential users, similar categories, and make recommendations based on user behavior. These insights can be used in real-time workflows to provide personalized suggestions to users. The use of graph algorithms has the potential to take graph-powered applications to the next level, enabling organizations to gain a deeper understanding of their customers and improve their services accordingly.

The Neo4j Labs Team has launched the Graph Apps Gallery, an online platform where users can discover and install various Graph Apps, including Kees Vegter's Neo4j Database Analyzer. The team also showcased a series of blog posts from community members, including Jennifer Reif on her "Creating a Data Marvel" series, Tommy Jones as this week's featured community member, and Cesar Pantoja demonstrating how to load the Bitcoin blockchain into Neo4j in one day. Additionally, there are updates on various projects such as the Graph Technology Landscape, link prediction with Neo4j using scikit-learn, and a new Graph database of Academic Literature called GrapAL.

Lockheed Martin Space is utilizing Neo4j as a master data management tool to map its product's DNA, enabling the alignment of satellites in space and understanding business operations. This decision was made due to Neo4j's capabilities, which were deemed superior to those of OrientDB at the time of implementation. The use of Neo4j has led to surprising results, such as identifying a tube of adhesive as having the most influence on a spacecraft's functionality. Despite initial challenges, Lockheed Martin Space has found the flexibility and expertise required to effectively utilize Neo4j, with its schema-less nature allowing for easy data loading and process management. As a result, the company expects graph technology to become increasingly valuable in connecting data, leading to potential monetization opportunities and a growing interest in this field.

I've loaded the information from the article into a Neo4j graph database, creating nodes for the 10 large companies that dominate the grocery store shelves and their respective brands. The visualization shows these companies in red and their brands in blue, with many products overlapping between stores. After researching two nearby grocery stores, Tesco Express and Sainsbury's Local, I found that Kellogg's had the most products for sale, with two-thirds of cereals coming from the original 10 companies and one-third from other companies. The graph also shows relationships between the companies, brands, and their documentation on sustainability and corporate values. This knowledge graph can be used to power search engines or make product recommendations based on consumer preferences and values.

The Neo4j Speaker Program is a initiative launched by the company to support and recognize thought leaders in graph technology, providing them with an opportunity to share their knowledge at relevant conferences around the globe. The program offers a travel stipend of up to $1,000 for accepted talks, covering costs such as travel, hotel accommodations, and meals. By becoming a speaker, individuals can build their reputation, empower others through sharing insight and knowledge, open up new career opportunities, and learn from teaching others. The program is designed to foster connections between graph database experts and budding enthusiasts, providing access to a list of qualifying conferences and promoting accepted talks. Additionally, the company offers a limited-edition Neo4j Speaker hoodie for speakers who submit their talk to at least 5 qualifying conferences.

Neo4j humorously announced CryptoGraph, a fictional cryptocurrency trading platform, as part of an April Fool’s Day article. The platform would supposedly operate using a new digital currency called Nebuchadnezzar, or "NebberCoins," and feature a unique analog twist by allowing physical representations of digital currencies. The article describes an ambitious expansion plan for CryptoGraph, utilizing empty retail spaces for Nebber mining and trading, and outlines a bold vision for blending digital and analog experiences. The piece playfully mentions various technological elements, such as a new graph platform Neo5j and the NullDB database, presenting a satirical take on the future of cryptocurrency and connected data. Ultimately, the announcement is a playful nod to Neo4j's capabilities and innovations, ending with a lighthearted promise to fund future hovercraft inventions, emphasizing the article's fictional nature.