May 2020 Summaries

The Neo4j community has been actively sharing various resources and projects, including a presentation on graph data modeling best practices by Max De Marzi. Vladimir Plizga built a Spring Integration Graph using Neo4j, while Vlad Batushkov illustrated the circular money flow of a simplified financial system in Neo4j. The JHipster platform now supports Neo4j, with several articles and videos available for developers. Additionally, the CFP for NODES 2020 is open, and users can share their stories and projects to educate the wider graph community.

A graph database like Neo4j offers greater flexibility than traditional relational databases, particularly for businesses that need to visualize connections in their data. According to Dave Mohr, director of North American East Region sales for Neo4j, the technology is a game-changer for how customers leverage data and can drive significant value for their business. To get started with Neo4j, customers should reimagine their data as a graph, rethink their approach to data usage, and be prepared to overcome initial friction. One notable use case for Neo4j was its application in the Panama Papers scandal, where it helped investigators analyze complex connections between individuals and entities. Graph technology is expected to continue growing exponentially, with potential applications extending far beyond traditional database use cases. To help customers succeed with Neo4j, the company offers a range of tools and services, including visualization technology, data orchestration, analytics, and transactional workloads, as well as strategic consulting and implementation support. Customers can engage with Neo4j at any point in their journey, whether they have an inkling about graph problems or are seeking to address specific challenges.

Neo4j is a native graph database that stores data as graphs, processes data as graphs, and presents data as graphs. Unlike relational databases, which are table-oriented with rows and columns, Neo4j focuses on the connections between data, allowing for easy querying of relationships and performance at scale on large datasets. The graph database approach is especially powerful in today's era, where high volumes, velocities, and varieties of data are prevalent. Companies that adopt graph approaches to their problems are seeing significant benefits. Neo4j is used by various industries, including automotive, manufacturing, and more, to combine different types of data into a unified view, providing insights and driving competitive advantage. The database can be used for use cases such as supply chain management, warranty analytics, customer 360, knowledge graphs, and more. Case studies show how companies like Volvo Cars, U.S. Army, Schleich, NASA, and Lockheed Martin are using Neo4j to improve their products, services, and operations. By leveraging the power of graph databases, organizations can unlock new value from their data, gain a competitive edge, and drive innovation.

This week's video presentation by Jesus Barrasa and Adam Cowley introduces a Neo4j 4.0 compatible version of Neosemantics, a Linked Data Toolkit for Neo4j, along with its accompanying graph app. Jean-Michel Daignan analyzes Beat Saber data using Neo4j, while Lennert van Sever demonstrates how to transform a REST service into a graph service. Additionally, Sebastian Daschner shows how to use Neo4j with Quarkus, a Kubernetes Native Java stack. The week also saw releases of Neo4j Bloom version 1.3, Stormspotter, and the Graph Data Science Library version 1.2, which provide new features and tools for working with Neo4j. Furthermore, Llewellyn Preece, a community member and Ninja contributor, was featured as part of the Neo4j Community Program, offering his insights and expertise to help others succeed. The GRANDstack community has also been active this week, releasing updates on their documentation and featuring new tools and features.

Last time, our News Editor, Jessica Baumgardner, showed us how a social knowledge graph improves remote collaboration for the home workforce in these very remote times. This week, I'm taking knowledge graphs to the next level – beyond work productivity, presenting a COVID-19 knowledge graph built by the Graphs4Good community to help researchers and the general public answer key questions about the pandemic as our fight against the virus continues. The COVID-19 knowledge graph provides heterogeneous pandemic information left by the Graphs4Good community, which can be used freely. To stay updated on graph tech goods, subscribe to the Neo4j YouTube channel for new videos every week.

NODES 2020 is a free virtual conference featuring highly technical presentations on graph-related topics delivered by Neo4j experts, taking place on October 20, 2020. The event has multiple tracks and features content-rich sessions, as well as virtual community activities and advanced-level training sessions for Neo4j Certified Professionals at no cost. This year's summit promises to be even bigger and better than the previous one, with a focus on educational and engaging technical talks about Neo4j-related topics such as graph-powered AI and machine learning. The event also offers six free virtual training sessions prior to the summit, which will be recorded and made available for viewing afterwards. With its diverse range of content and activities, NODES 2020 is an exciting opportunity for attendees to learn from Neo4j experts and network with like-minded professionals.

Apache Zeppelin is a web notebook with a pluggable architecture, composed of three pillars: the display system, the interpreter and helium modules. The display system renders output on the front-end, while the interpreter enables Zeppelin to use specific programming languages or data processing back-ends like Spark, Pig, or Frink. The third pillar, Zeppelin Helium, is a plug-in system that allows users to easily extend Zeppelin with new features and tools. CAPS (Cypher for Apache Spark) is a project that extends Spark, allowing users to create and query graph data models / property graph models all over Spark, with three main features: built on top of the Spark DataFrame API, supporting a subset of Cypher, and supporting a wide range of data sources. Interactive notebooks are rising in popularity and replacing PowerPoint presentations in many organizations due to their ability to address common tasks in data-driven organizations such as running code, exploring data, and presenting results in real-time collaboration between users.

The Neo4j community has been actively engaging with each other through various projects, presentations, and blog posts. Jennifer Reif presented the APOC Library, showcasing its capabilities for data import, transaction handling, text manipulation, and more. Rik Van Bruggen explored COVID-19 contact tracing using Neo4j Bloom, while Tomaz Bratanic demonstrated NLP procedures within the APOC library. Meanwhile, Daniel Sharp analyzed chess matches with Neo4j, and Alexander Erdl explored data from Steam using Neo4j. The community has also featured Peter Rose and Ilya Zaslavsky as part of their "Graphs for Good" project to build a knowledge graph to fight COVID-19.

NEORIS is a digital accelerator that leverages graphs to address various use cases such as recommendations, anti-money laundering, and knowledge graphs. The company uses Neo4j for its biggest use cases including customer 360, ultimate beneficial ownership analysis, and music, which Demian Bellumio considers his favorite aspect of working with the platform. Demian emphasizes the importance of the Neo4j community, citing its nimble organization and personal touch. He believes that graph technology will become the cornerstone of any data infrastructure, enabling companies to bring together disconnected data sets. The future holds significant improvements in scaling, making graphs increasingly invisible behind the scenes, and the convergence of semantic and property graph worlds, which Demian sees as an exciting development for Neo4j users.

You will receive advanced virtual training sessions, seven hands-on sessions at no cost, covering various Neo4j topics such as Cypher Query Tuning, Graph Algorithms for Data Science, and Machine Learning Pipeline. The sessions are scheduled in June and October, with specific dates announced, and will be recorded and made available for viewing at a later time. To access these training sessions, you must be a Neo4j Certified Professional, and login to the Neo4j Community Site using your certification email address to gain automatic membership into the certified professionals group. Alternatively, if not yet certified, you can take the one-hour free certification exam to get started.

The process of translating data between different database models, such as relational databases to graph databases, requires a nuanced approach that takes into account the distinctive origins and vocabularies of each language family. Unlike simple translations between languages with similar roots, ETL tools must adapt to convert complex data structures and relationships found in relational databases into the more efficient and connected graph database model. With powerful graph ETL tools, this process can be made straightforward by extracting tables and foreign keys, transforming them into nodes and relationships, and loading those elements into a graph database, often uncovering important concepts or entities that were hidden in the original data.

The Neo4j community has been actively engaged with various projects and topics, including a series of blog posts and videos on graph algorithms, COVID-19 contact tracing, power laws, and the GRANDstack framework. The community has also featured an interview with David Fox, Senior Software Engineer at Adobe, who discusses his experience with Neo4j and its applications in various industries. Additionally, there are updates on the APOC library, Spring Data Neo4j RX, and a new flight search application built using Neo4j and the GRANDstack framework. The community has also recognized the contributions of Vivek Srivastava, a Neo4j Certified Professional and expert in data warehousing and analytics.

Our Managing Editor explored data modeling fundamentals using the Arrows tool, a key concept for working with graphs in Neo4j. Recently, Jocelyn Hoppa delved into how social knowledge graphs can improve remote collaboration by avoiding "lonely nodes" and leveraging Slack conversations to analyze collaboration patterns. This session highlights the importance of effective communication and connection strategies in remote work environments, where traditional collaboration methods may not be as straightforward. By utilizing tools like Arrows and analyzing conversation data, individuals can optimize their remote collaboration and stay connected with team members. The Neo4j YouTube channel offers additional graph technology resources and insights.

GraphTour Europe 2020 started in Amsterdam on February 4, right after the release of Neo4j 4.0, a key milestone in the graph technology landscape. GraphAware is excited about the new features included in this release because they revolutionize the way we approach some common graph challenges. The company's CEO spoke about the release at GraphTour events across six cities, highlighting practical applications of Neo4j 4.0 for law enforcement and intelligence, multi-database capabilities in single-tenant use cases, and increased scalability that enables stronger graph-based machine learning. At GraphAware, they believe Neo4j 4.0 will change many practices and enable more advanced use-cases, with their latest version on Hume supporting the new release and offering features such as a new space to manage resources and an intuitive way to visually manage data workflows.

Graph data science can help businesses detect and prevent fraudulent schemes by analyzing patterns in their data, particularly those involving multiple parties working together. Fraud rings often involve individuals with good reputations and valid transactions, making them harder to detect. By using graph technology, businesses can uncover suspicious patterns, identify strongly connected communities engaged in known fraud, and pinpoint influential individuals and high-frequency paths. Graph data science can also help investigators share results with the business and enable further exploration of a connected dataset. The approach is non-disruptive, adding new dimensions to existing machine learning pipelines without changing them. It enables businesses to scale their predictive accuracy with the data they already have.

This week's video showcases a social knowledge graph built on data from collaboration tools used at Neo4j, improving remote collaboration. The featured community member is Alex Law, who contributes to the Neo4j community with her "let's give it a try" attitude and dedication. A webinar discusses how to build such a graph, addressing common problems in remote working setups, including domain experts and knowledge gaps. Other topics include querying the COVID-19 Contact Tracing Graph, showcasing new features of the Graph Data Science Library, building a Neo4j app with Bloom Perspectives, and creating likes in a social network using stored procedures.