August 2022 Summaries

In this week’s newsletter, Chintan Desai demonstrates how a graph database can be used to perform analytics on a knowledge graph, providing insights such as top five drugs reported directly by consumers for their side effects. Neo4j has extended the CFP deadline for NODES conference and is offering a Code Golf challenge with prizes totaling $27,000. The newsletter also features Antonio Origlia, an assistant professor who uses Neo4j in his research on speech processing and human-computer interaction, as well as highlighting various Neo4j tools and releases, including Neo4j Migrations and a toolkit for data visualization.

Cyndi Streun is the Vice President of Information Services at the Federation of State Medical Boards, a small nonprofit organization over 100 years old. She leads a diverse team in the IT department and has seen exciting results from using Neo4j graph technology, including her team members' enthusiasm for its ease of use. Cyndi advises that anyone can learn Neo4j without needing to be a data expert or engineer, as it offers a "no-code" approach. She believes graphs are no longer just for researchers and universities, but have real-world applications in production environments with scalability and features.

The Neo4j Data Importer has introduced an enhanced approach to improve the fidelity of its "Preview" feature, which allows users to visualize graph data before committing it to their database. This new feature validates the graph model, scans corresponding files for relationship and node data, and attaches relationships to loaded nodes. It also loads disconnected nodes, including solo nodes without fully mapped relationships or those with no matched rows in previous steps. The enhanced workflow improves preview accuracy by utilizing a single transaction instance of neo4j-driver and rolling back the operation after completion. This feature is now available for users to try out in their Neo4j Aura console, with a progress bar indicating the loading process's progress.

The author of the text is Head of Product Innovation & Developer Strategy at Neo4j. They revisit the Marvel Data API, which they previously explored in a series with their colleague Jennifer Reif, and use it to create a new Neo4j AuraDB Free instance to store data from the API. The data model centers around the `ComicIssue` node, connected to other nodes like `Creator`, `Character`, `Story`, `Event`, and `Series`. They load the characters by iterating over their names, fetching comics details for each character, and creating nodes and relationships to connect them. The author uses Neo4j Browser, Explore (Bloom), and GraphQL API to visualize and query the data. They also generate an augmented GraphQL schema using the Neo4j GraphQL Toolbox.

The Cypher Intermediate Queries course on GraphAcademy has been released, teaching users how to speed up queries using indexes and constraints in Neo4j. The course is the second in the Cypher learning path and builds on the knowledge gained in the Cypher Fundamentals course. A Neo4j NODES conference is scheduled for November 16-17, 2022, with a CFP deadline extended until August 31, 2022. The Neo4j Code Golf challenge offers prizes totaling $27,000 for participants to optimize their code and golf in the summer. Featured community member Sefik Ilkin Serengil is a software engineer with experience in machine learning and cryptography, and has published several research papers. Jennifer Reif's blog follows her conversion of existing MongoDB-connected services to use Spring Cloud Config for accessing database credentials, while Victor Caciquinho and Willian Frantz explain how they used Neo4j and Elixir to evaluate student proficiency through a micro-services architecture. Various other resources and talks are available on GraphAcademy, including videos and interviews with experts in the field.

The text discusses healthcare analytics as an analysis activity that involves collecting data from four areas within healthcare: claims and cost data, pharmaceutical and R&D data, clinical data, and patient behavior and sentiment data. The connected data capabilities of a graph database can help achieve what is impossible or complicated with traditional relational databases. A demo use case is provided to explain the capabilities of Graph Database in healthcare, using the FDA Adverse Event Reporting System Data. This dataset is used to analyze side effects reported by consumers and health professionals, revealing interesting insights that cannot be easily obtained with traditional data querying methods. A pre-deployed Neo4j sandbox is available for exploring this example, allowing users to try out graph database capabilities without installing anything locally. The full source code for the example and guide is available on GitHub.

Neo4j's graph database provides a solution for capturing, measuring, and aggregating dynamic sustainability data, enabling businesses to make smarter decisions and reduce risk. The platform allows for the creation of digital twins that mimic complex processes, making it easy to model and track emissions and environmental compliance. With its flexible schemaless data model, Neo4j enables organizations to adapt to changing regulatory requirements and report at different levels, from individual sensors to entire regions. Graph databases also facilitate visualization and analysis of data, allowing companies to effectively address environmental hazards and meet climate action plans.

Here is a summary of the text in one paragraph, covering key points: The Neo4j community has published several articles and blog posts on graph-related topics, including a selection of articles on graph solutions and their applications, such as searching for chemical compounds and analyzing influenza data. The CFP deadline for the NODES conference is approaching, and there are still opportunities to participate in the Neo4j Code Golf challenge with prizes totaling $27,000. Additionally, the community has featured Siraj Munir, a data scientist using graph technologies to solve real-world problems, and shared knowledge graphs, such as one on full-text search in chemical names, and articles on API design decision-making and GraphQL APIs with Neo4j AuraDB.

The Neo4j Graph Framework is a structured approach to designing and implementing graph databases, covering aspects from initial use case assessment to scaling and expanding the solution with new data and business requirements. It consists of five core pillars: Use Case Assessment, Graph Readiness Review, Graph Development Review, Graph Operations Review, and Graph Scale and Expand, each outlining key activities for teams to consider. The framework is designed to help customers develop a mutual success plan, providing best practices, training recommendations, and identifying areas where professional services can work alongside the team to accelerate time to value. It serves as a reference model for customer's current and future graph solution deployment, providing clarity across the organization from executive stakeholders to project development and implementation teams.

OpenSanctions is an open-source project developed by Friedrich Lindenberg, a freelance software developer based in Berlin, Germany. The project aims to provide a comprehensive dataset of individuals around the world who are considered "persons of interest," including politicians, their associates, and other influential figures. By analyzing different datasets through graph technology, OpenSanctions seeks to identify significant connections between these individuals and various entities, such as companies and governments. This data can be used by investigative journalists, analysts, and businesses to gain insights into global corruption, illicit finance, and other complex issues. The project has gained attention for its potential to empower those working to hold power accountable and uncover dark secrets in international politics. With the increasing importance of sanctions and anti-corruption efforts, OpenSanctions is poised to become a valuable tool for researchers, journalists, and policymakers alike.

The interview discusses the evolution of data from collected data to connected data and knowledge graphs. Graph databases were developed to address the limitations of relational databases in handling complex relationships between data points. The conversation highlights the importance of leveraging connections in data for better insights and decision-making. Companies in various industries, such as financial services, cybersecurity, manufacturing, healthcare, and retail, are adopting graph technology to solve specific problems like fraud detection, supply chain management, personalized recommendations, and more. When choosing a graph technology, it's essential to prioritize data reliability over performance speed, as corruption in a graph database can have devastating consequences. The company Neo4j is growing rapidly in the APAC region, with big brands adopting its technology for various use cases. The interview also touches on the importance of sustainable growth and the need to make graph technology more accessible and user-friendly. Daniel Ng, one of the top 100 power leaders in marketing and communication, emphasizes the value of learning, contributing, and being happy while working, and invites everyone to explore the potential of graph technology for their data.

We built a demo of r/place using GraphQL subscriptions with Neo4j Graph Database. We created a collaborative 30x30 canvas where users can paint and their changes are shared in real-time across all connected users through WebSockets. The demo showcases how to create a production-ready real-time service with GraphQL, including scalability and performance considerations. We used Apollo Server, graphql-ws, urql, and CloudAMQP for the server-side implementation. The client-side implementation was built using HTML5 Canvas, JavaScript, and Puppeteer headless browser. We demonstrated how to scale the demo horizontally by using AMQP as a message broker and Neo4jGraphQLSubscriptionsAMQPPlugin. The demo can handle roughly 500 concurrent users painting at the same time, with one bot equivalent to 10 concurrent users in terms of load to the system.

Graph databases can help implement effective cybersecurity solutions due to their ability to provide a holistic view of enterprise networks, allowing for quick risk identification, anomaly detection, and system protection. This is demonstrated through an example use case using Neo4j, where a fictitious Active Directory environment is created to showcase the power of graph-based querying in cybersecurity. The concept of attack paths and network insights are also explored, highlighting the potential of graph databases in zero-trust security approaches.

Viasat, a satellite internet provider, is exploring ways to connect its large and growing company through digital transformation. The company's Compass tool aims to solve the challenges of intercompany connections, content discovery, communication, and collaboration, while promoting a startup culture in a large organization. Leveraging graph analytics and machine learning, Compass provides personalized profile pages for users, adaptive search, and a platform for consistent messaging across the company. The tool also focuses on speeding up onboarding, leveraging company wisdom, and delivering more relevant search results through a data flow that incorporates multiple technologies like Neo4j and Solr. Ultimately, Compass strives to create an awesome experience for employees, while pushing the boundaries of digital transformation and innersourcing.