October 2022 Summaries

For the past few months, our product teams at Neo4j have been working hard to improve your experience with Neo4j's fully managed graph database, AuraDB. The latest updates aim to make it easier for users to get started with graph databases by introducing a new user interface called Neo4j Workspace, which combines several powerful tools in one place. This update also enhances the Graph Visualization tool, Bloom, with features such as automatic graph generation and synchronization of perspectives when adding new datasets. Additionally, Data Importer has received granular updates that improve developer productivity, including datatype detection, automatic mapping, and problem files. The platform now offers a Status Page to provide visibility into service level availability, as well as new security and privacy features, including GCP Private Service Connect for secure connectivity between the database and cloud services, and Security Logs that allow users to identify and troubleshoot issues before they become threats.

The electrical grid is a complex system that requires real-time data analysis to optimize performance and predict potential issues. The author, David Swank, was working on a microgrid project when he realized the need for a common platform to connect various systems and technologies. This led him to create the ENX Energy Platform, which uses graph technology to model the grid from generation to consumption, enabling utilities to gather intelligence on their operations and make data-driven decisions. The platform is designed to be interoperable, allowing different devices and systems to communicate with each other seamlessly. It also includes features such as predictive analytics, machine learning, and real-time insights, which enable utilities to anticipate and mitigate potential issues, ultimately improving the efficiency and reliability of the grid. By visualizing and analyzing data across all levels of the grid, ENX aims to unlock new use cases for AI/ML, such as optimizing energy consumption in buildings and predicting power outages. The platform has already been successfully deployed with utilities like Oregon Trails Electric Cooperative, enabling them to better manage their grids and prevent potential issues.

This article describes how to create a GraphQL API using NestJS, Neo4j graph database, and AWS Cognito for authentication. The process involves setting up a new NestJS project, installing required modules, defining the GraphQL schema, creating models, securing the API with JWT authorization, and testing the endpoint. The author provides step-by-step instructions, code examples, and explanations to help readers create their own GraphQL API using these technologies.

You are invited to bring friends to attend NODES for a chance to win $500 each, with the top 10 participants having the most friends attending receiving the prize. To enter, add your full name to the registration URL and ask your network to use it when registering for the event. A form is available to help with creating tweets and LinkedIn posts with personalized URLs.

Gartner predicts that by 2025, 80 percent of data and analytics innovations will use graph technology. However, many are unsure when to apply graph data science. To help teams understand the benefits of Neo4j Graph Data Science, a recommended four-step approach is outlined, including learning the basics of graph data science, exploring practical business applications, deepening technical expertise through resources such as live demos and courses, and getting answers from the community. By following these steps, teams can quickly see powerful changes in their data analysis and prediction capabilities.

The NODES 2022 online conference is approaching, featuring a 24-hour agenda packed with technical sessions on graph use cases and implementations. The event will cover various subjects such as knowledge graphs, graph visualization, and graph data science, presented by experienced practitioners from around the world. Keynote speaker Nicholas Christakis will discuss the effects of social networks on population behavior. Sessions will be live and unique throughout the conference, with speakers including Kateryna Nesvit, Ward Cunningham, Mark Heckler, Jennifer Reif, and many more. The event is free to attend and available for anyone in different time zones, including Asia Pacific, Europe, the Middle East, and Africa.

The text discusses a solution for virtually showing real-world graph scenarios in Neo4j without modifying the underlying model. A Neo4j Certified Professional presents a Cypher code snippet that uses the APOC Library to project nodes and relationships, effectively hiding intermediate nodes and revealing abstracted information relevant to business users. The code snippet is parameterized to work with any traveler present in the database, allowing developers to show their entire journey without modifying the model. This approach leverages virtual graph concepts used by popular graph visualization tools like Bloom and Hume, enabling developers to answer business problems while maintaining graph abstraction.

With the latest releases of Neo4j AuraDS Enterprise and Neo4j Graph Data Science 2.2, data scientists and machine learning engineers can make better predictions and move to production faster using their existing data. Neo4j AuraDS Enterprise offers fully managed and hosted Google Cloud instances for effortless scalability, while providing additional enterprise-grade security features such as Single Sign-On and Virtual Private Cloud isolation. The new release also includes larger cloud instances, 24/7 Severity 1 support, and a wider range of algorithms through the Graph Data Science library. Neo4j Graph Data Science 2.2 introduces context-rich machine learning pipelines, graph native deep learning with multilayer perceptrons, faster model training with graph sampling, and integration with other graph libraries like Deep Graph Library and TensorFlow GNN.

Neo4j Dashboards can be super-charged with NeoDash 2.1, an open-source, low-code Dashboard Builder for Neo4j. With its new features, users can create interactive dashboards with tables, graphs, bar charts, line charts, maps and more in minutes. The latest release comes packed with a dashboard gallery featuring sample dashboards on public datasets, new visualizations like sankey charts, choropleth map, and hierarchical charts, an improved user experience, and new ways to export data out of dashboards. NeoDash also supports drag-and-drop layout for easier report building, cloning reports, image downloads, and CSV file downloads from table reports. The tool will be featured at the NODES 2022 Online Developer Conference on Nov 16–17, where attendees can learn more about its features and how to use it.

In our latest update, we have introduced several new capabilities to enhance security and isolation in Neo4j Aura Enterprise. The availability of AWS PrivateLink and GCP Private Service Connect provides dedicated, secure connections between applications and isolated Neo4j-managed databases without traversing the public internet. Additionally, security logs are now available for up to 30 days, allowing users to identify and troubleshoot issues before they become threats. Security logs can be downloaded in JSON format and serve as an extra layer of reassurance with encrypted connections that never leave the target VPC.

The GraphAcademy course "Path Finding with Graph Data Science" has been released, teaching how to find the shortest paths between pairs of nodes in graph data science. The course includes examples for both weighted and unweighted relationships. Additionally, the free two-hour pre-NODES workshops on Apache Hop, Graph EDA, Apache Arrow, healthcare analytics, and Neo4j graph database are available, featuring top graph practitioners. The NODES conference will take place on November 16 and 17, with over 100 graph experts sharing their knowledge on various unique use cases. A new course by Dagmar Waltemath showcases a knowledge graph integrating multiple data sources during the NODES conference, highlighting the potential of graph databases for better data integration and exploration. Furthermore, a free one-hour Graph Academy course "Path Finding with GDS" is available to learn how to use the Neo4j Graph Data Science library to find weighted shortest paths, while another blog post discusses graph native storage in Neo4j. The event also features video tutorials, open-source projects, and data visualization tools, including a 5-part series on NeoDash 2.0 for data storytelling with Neo4j.

Neo4j is being used by Ria Money Transfer, a financial services company, to fight financial crime and improve compliance. Miguel Aguado, Compliance Director at Ria Money Transfer, has been using Neo4j for the last few years to manage the financial investigation unit and has seen surprising results from its use, including the ability to identify hidden trends and networks in data that were previously unknown. Aguado advises others getting started with Neo4j to spend time deciding on a suitable data model, as this is key to success with graph technology. In the future, Aguado expects regulators to increasingly require companies to use graph technology to combat financial crime and prevent money laundering, making it a mandatory tool for compliance.

Neo4j is the world's leading graph data platform, trusted by global brands and validated with successful use cases across industries and architectures. It is a pioneer in AI-powered technology and in-graph ML capabilities, and its data science library solves critical data challenges. Neo4j can be easily deployed as a self-hosted offering or as a cloud-native service, including on AWS, where it has achieved Data and Analytics Competency status, recognizing its success in helping customers evaluate and use the tools and techniques of working with data productively on AWS. The platform has been used by various organizations to overcome specific challenges, such as Tourism Media, which increased productivity by 8x, OrbitMI, which saw a 60% increase in productivity, and Meredith Corporation, which increased web traffic by 600%. Neo4j will be sponsoring AWS Re:Invent in Las Vegas with its best-in-class graph Database-as-a-Service, AuraDB.

The presentation discusses the challenges of career development in large organizations with disconnected systems. Michele Howard from DXC Technology shares their experience building a solution called Career Navigator on Neo4j's Graph Database and Graph Data Science, which aims to provide intelligent recommendations for employees based on their skills and career aspirations. The tool connects siloed data points and generates personalized development plans, enabling managers to support employees with skill gaps and helping them navigate the complex ecosystem of in-demand skills within the organization. By leveraging a knowledge graph and applying algorithms, Career Navigator provides insights and nudges employees towards new career pathways, ultimately benefiting both the employee and the business. The solution has been successfully implemented with 130,000 employees across the organization.

The article explains how Neo4j graph databases work and their benefits over traditional relational databases. The key points are that Neo4j models data in a way that resembles how humans think about relationships, making it easier to model complex data structures. It also eliminates the need for normalization, which can be time-consuming and cumbersome with relational databases. The article provides examples of how researchers used Neo4j to build complex graph models, including one for biomedical research, and demonstrates how to create a simple graph model using the Northwind retail database. The author argues that traditional relational databases are outmoded and that Neo4j's visual approach to data modeling makes it easier to work with complex data structures.

This week's featured article discusses RDF (Resource Description Framework) and LPG (Labeled Property Graph) models, providing an overview of their elements, languages used to model data, and querying capabilities. To expand graph skills ahead of NODES 2022, expert practitioners are offering free workshops on Apache Hop, Graph EDA, Apache Arrow, healthcare analytics, and Neo4j graph database. Ward Cunningham, a renowned expert in software development, will be presenting at the conference, discussing his work on wikis and collaborative frameworks. Additionally, Nicolas Rouyer demonstrates the predictive capabilities of Neo4j Graph Data Science on the CORA dataset, while Hugo Jiménez discusses the advantages of knowledge graphs in various applications. The article also highlights several other topics, including pathfinding algorithms using Neo4j, learning about graph databases, combining biochemical datasets in a graph database, and GDS with verifiable credential data.

The NODES conference on November 16 and 17 will offer a series of preparatory workshops to help attendees improve their skills in graph technology. These workshops are presented by industry experts with diverse backgrounds, covering various topics such as healthcare analytics and data problem-solving. The workshops are designed for both new and experienced professionals, providing a comprehensive overview of Neo4j and its applications. Participants will gain hands-on experience with graph databases, including loading and querying data using Neo4j Aura Free, and learn about Apache Hop and Neo4j's integration with Apache Arrow. Additionally, attendees will explore the challenges of performing Exploratory Data Analysis on graph data and acquire practical tools and techniques to apply to their projects.

The European GraphSummit roadshow recently concluded in Copenhagen, where a number of experts shared their experiences with graph technology. Novo Nordisk, a global healthcare company founded in 1923, presented on leveraging graph technology for clinical standards and trials using Neo4j database. Henrik Enquist, an Advanced IT Developer at Novo Nordisk, discussed the use of Neo4j in managing clinical trials and handling clinical standards, highlighting its convenience in connecting terms and standards. For someone getting started with Neo4j, Henrik advises trying it out, playing with example data sets, and becoming familiar with its logic. He also believes that graph technology is well-suited to describe the increasingly connected nature of modern data.

The Google Cloud Dataflow platform is being used to leverage graph databases, specifically Neo4j. A new flex template has been released that supports complex ETL processes through configuration, not code. This allows for cloud-native data integration without having to manage complex ETL processes. The template supports multiple sources, including BigQuery and text files, as well as targets such as nodes and edges in Neo4j. It also includes features like variable substitution, debugging tools, and support for various data types. The template is designed to be user-friendly and easy to use, with a graphical ETL import tool and a simple configuration process. It is ideal for joint Google Cloud and Neo4j customers who want to integrate their data without having to write complex code.