April 2021 Summaries

The latest versions of Neo4j Desktop and Neo4j Browser have been released, introducing several key features and improvements. The new desktop tool now supports multiple database management in the UI, allowing users to create, manage, and view databases directly from the application. Additionally, users can now choose any folder as a project location, providing more control over where their project files are stored. Offline handling has also been improved by synchronizing with system connections. The Neo4j Browser has received a new guided experience, featuring a sidebar that lists available guides and allows for easy navigation through learning resources. A fresh new logo and icons have been introduced, while new manuals for desktop and browser users have been made available. Finally, the confirmation message when closing an edited reusable frame in Neo4j Browser has been removed to improve user workflow.

The Neo4j GraphQL Library is a low-code, open source library designed to help developers build API-driven, intelligent applications faster by tapping into the power of connected data. With this library, developers can create seamless user experiences for their customers while avoiding duplicate schema work and ensuring flawless integration between front-end and backend developers. The library makes cross-platform and mobile development simpler with JavaScript applications, allowing users to create predictive, mission-critical, and context-aware apps with data-rich APIs that are more secure and reliable. It provides a range of benefits including translating GraphQL queries into a single Cypher query, simplifying modernizing applications for exceptional performance, generating Cypher from GraphQL, and giving access to Cyper query language for expressing complex graph traversals. The library is optimized for graph traversals, bringing performance optimizations to GraphQL implementations, and has a low learning curve, helping developers quickly build API-driven applications across open source frameworks. It also provides access to the Neo4j native graph database, which makes complex and multi-hop graph traversals highly performant, has a native graph data model mapping for graphs in the GraphQL schema, and optimizes query performance with unlimited scale.

The Neo4j GraphQL Library 1.0.0 has been released, offering a redesigned and rewritten library in TypeScript for better type safety. The new library is a successor to the neo4j-graphql-js project and provides high-quality support and continuous improvement. It includes a stable version 1.0.0 of the Object Graph Mapper (@neo4j/graphql-ogm), which allows for programmatic API manipulation of data in Neo4j databases using GraphQL type definitions. The library also introduces new features such as nested mutations, camelCase query and mutation field names, and ISO 8601 strings for date and time scalars. Additionally, the library offers directives like @timestamp and @auth to simplify authentication and authorization processes. The team is committed to continuous improvement and has a migration guide available to help users transition from the old library. Future plans include implementing Relay specification, aggregations, and expanding the capabilities of the new library.

Neo4j AuraDB Enterprise offers unparalleled speed, allowing complex queries to deliver results in milliseconds, whereas relational databases are impacted by data growth and the number of JOINs. The native graph engine in Neo4j AuraDB Enterprise delivers performance at scale, supporting tens of billions of nodes and relationships without compromising data integrity. Additionally, Neo4j AuraDB Enterprise boasts enterprise-class security features, including encryption of data in transit and at rest, as well as role-based access control to ensure sensitive data is fully protected. With Neo4j AuraDB Enterprise, companies can benefit from fast queries over large datasets, real-time insights, and secure storage of sensitive information.

Cypher Sleuthing: Dealing with Dates Part 2 discusses truncating dates in Cypher, a query language used for graph databases like Neo4j. Truncation allows users to set default values for smaller time units when creating temporal instant values. This can be helpful for generalizing searches or calculating durations between two dates. The article also covers using durations, which measure the amount of time between two temporal instants, and provides examples of how to add or subtract durations from dates, calculate the difference between two dates, and set duration as frequency for medicine dosage. Additionally, it touches on duration precision, conversion of hour and minute values into seconds, and calculating specific dates and times as durations. The article concludes by highlighting the importance of calculating differences between two dates using `duration.between` and invites readers to explore more Cypher features in future posts.

Creating a Neo4j React App with use-neo4j Hooks` is an article that documents how Cylynx built a custom graph application to connect financial data points using Neo4j. The company used the `use-neo4j hooks` library, which provides React components and hooks for building applications that communicate with Neo4j, to speed up development and create an integration with Neo4j. To achieve this, they created two main components: ConnectDatabase and ExecuteQuery, which handle database connections and querying respectively. The application also uses the `Neo4jProvider` component to manage driver state and a `CypherQuery` component to allow users to input queries. The article provides tips for developers, including using the Neo4j sandbox to speed up development and taking advantage of `use-neo4j hooks` if you're already working on a React application with Neo4j integration in mind.

The Neo4j community has been actively contributing to the platform, with several notable achievements. The Leonard Euler Idea Contest is now open for registration, offering a prize pool of up to $10,000 USD and encouraging developers to build projects using Neo4j and GraphQL. Hadi published an article on exporting indexes and constraints from SQL Server graph tables to Neo4j, while Darrel shared how Hexology uses Neo4j through the Neo4j Startup Program to power their mobile application and AI. The Global Graph Celebration Day meetup provided a recap of graph history, foundations, innovations, and new developments in the community. Additionally, Will and Lju released an episode of the GraphStuff.FM podcast on the graph data platform, and David announced a new version of the Neo4j Apache Connector for Spark. The community is also active on social media, with several notable tweets and posts shared throughout the week.

Neo4j is being used by the Patient Journey project to analyze large populations of patients' treatments and effectiveness, allowing for quick analysis of patterns and outcomes. Before Neo4j, medical records data was typically stored in relational databases, requiring specialized knowledge and resources to query and present information, which was often time-consuming and expensive. The graph database technology enables dynamic presentation of patient journey information with just a few mouse clicks, providing insights into treatment patterns and outcomes. Ravi Anthapu and Matt Holford praise Neo4j's powerful features, including Cypher query language, custom plugins, and user community support, which have enabled them to analyze large datasets quickly and gain valuable insights. The project is expanding to include enhancements for customer projects, predictive analytics, and exploring the application of graph technology beyond healthcare to other business areas.

Today's developers and data practitioners face a number of challenges that require advanced analytics to inform business strategy, managing siloed data subject to increasing regulation, and creating an improved customer experience. Neo4j AuraDB Enterprise is a graph database-as-a-service offering fully managed and automated infrastructure, enabling rapid deployment and expansion of graph-powered applications without administrative headaches. The database is effortless to use, with zero administration required, and easily scalable, allowing for on-demand elasticity with no downtime or service interruption. This platform supercharges business efforts by leveraging the innovative nature of the database, making it a must-have for companies of all sizes.

Neo4j is a graph database that allows users to model complex relationships between data entities. A recent episode features tutorials and updates on using Neo4j with Python, including the Py2neo package, which enables connecting to Neo4j databases. Users can also learn how to model Git commits with Neo4j, which simplifies storing relationships between files and commits. Additionally, Neo4j is expanding its community interaction through a migration from Slack to Discord, offering features such as group video calls and screen sharing. The company has also released an early release repository for OS Open Names data in Neo4j, providing latitude and longitude coordinates for locations in Great Britain. Various weekly live demos showcase Neo4j's capabilities in use cases like supply chain management, algorithms playground, and fraud detection. Furthermore, Neo4j is introducing new certifications to demonstrate knowledge of the graph database, including a graph data science certification.

Leonhard Euler is a prolific mathematician whose work has had a significant impact on various fields including graph theory, calculus, differential geometry, number theory, and infinite series. His solution to the Königsberg bridge problem in 1735 laid the foundation for modern graph theory, which is now ubiquitous in technology and everyday life. To celebrate Euler's birthday, the Neo4j community comes together online for a Global Graph Celebration Day Meetup, where they share their knowledge, discuss the history of graphs, and engage with fellow enthusiasts. The event also features an announcement related to the GraphQL community and a Leonhard Euler Idea Contest, which invites developers to transform their ideas into reality using Neo4j's graph database and GraphQL library, with $10,000 in prizes at stake.

The Neo4j Graph Data Science (GDS) Library is a comprehensive framework that includes graph algorithms, the Neo4j Graph Database for persistence, and the Bloom data visualization tool. The library started with 42 graph algorithms in five categories and has since added nearly 60 new algorithms based on community feedback. New features include graph-native machine learning capabilities, such as three graph embedding algorithms and supervised machine learning workflows. The framework also includes transformation features to project, subset, and transform graphs, a new enterprise memory format that reduces the in-memory footprint by 75%, and support for persisting ML models in Neo4j. Additionally, GDS respects role-based access control and can execute graph projections and algorithms on each shard individually. Users can export an in-memory graph to a new database, and Neo4j Bloom has added categorical coloring and hierarchical layouts. The framework is available for download at the Neo4j website or GitHub repo.

This week's episode of Neo4j highlights various topics, including using Django and Neo4j together for enhanced application development, analyzing genomes in a graph database with KEGG data, and exploring change data capture connectors with GraphAware Hume. The Developer Relations team also features community members Andrea Santurbano and Giuseppe Villani, who have contributed to the Neo4j ecosystem through their work on APOC libraries and other projects. Additionally, there are various resources available for improving data import, reconciling taxonomies, analyzing clickstream data, and visualizing data with Hume. The week also includes Global Graph Celebration Day on April 15, where Neo4j is hosting an extended meetup for graph enthusiasts around the globe.

In a real-time transaction processing system, Minka is utilizing Neo4j to model transactions as graphs, which allows for efficient and fast processing of financial information. Prior to adopting Neo4j, the system used relational databases but found them to be slow due to the need to join tables to find connected nodes. The use of Neo4j's index-free adjacency feature has greatly improved performance by allowing for direct connections between nodes without the need for joins. Minka's experience with Neo4j has enabled the company to push the boundaries of what is possible in real-time transaction processing, including faster data migration and more efficient query execution. As the company continues to scale, they are exploring the use of full graphs in AuraDB and envisioning a future where multiple transaction-processing clusters are connected to facilitate fast and secure transactions across different regions. The developer sees a vast potential for graph technology in solving complex problems and modeling connections between seemingly unrelated entities, which is reflected in their enthusiasm for Neo4j and its applications.

Cypher Sleuthing: Dealing with Dates, Part 1` is a blog post about working with dates and time in Neo4j using Cypher. The author discusses the complexities of standard measures of time and how they can vary depending on daylight savings time, geographies, and calendars. They then dive into Cypher's date formats, including instant types such as `date`, `datetime`, and `time`, which are based on the ISO 8601 format. The post covers various scenarios for converting epoch Unix time values and strings in non-ISO8601 formats to Neo4j-supported temporal values using APOC library procedures. It also touches on multi-conversions and wrapping conversions together to achieve desired results. Finally, it concludes with a discussion of the utility functions available in the APOC library for working with dates and times in Neo4j.

FluxOps is a tool that enables developers to manage infrastructure as code (IaC) for Kubernetes. In this guide, we set up Flux and Helm on an EKS cluster to provision templated resources. We created a private repository using GitHub CLI and set up the folder structure with a "charts" directory for helm charts, a "releases" directory for Flux releases, and a "src" directory for our full-stack application. We installed GRANDstack, a composite project ready for development, and deconstructed it to make it more accessible for our folder structure. We created a docker-compose file, converted it to a helm chart using Kompose, and added values.yaml to define image strings. Finally, we made the Helm release object, seeded the database (optional), and committed changes to the repository, allowing Flux to provide resources automatically. This setup enables us to make infrastructure evolve in pace with our application, ensuring that changes are defined and automated.

Retool is a low-code tool for building applications with drag-and-drop components and JavaScript customizations, allowing users to create customized interfaces without extensive coding knowledge. It connects to diverse data sources, including relational databases and GraphQL APIs, providing a quick and easy way to display data in meaningful ways. The author has integrated Retool with Neo4j's graph database using GraphQL as the common connector, enabling the creation of a custom application that can retrieve and analyze Twitter data from Neo4j. The application includes features such as statistics on followers and popular influencers, recommendations for users to follow, and a newsfeed showcasing tweets from similar users. With its user-friendly interface and flexibility, Retool provides a viable option for developers, analysts, and others to build custom applications without extensive coding knowledge.

Today's developers and data practitioners face significant challenges, including the need for advanced analytics to inform business strategy, morasses of siloed data subject to increasing regulation, digital initiatives slowed or stalled, complex landscapes of systems that need rationalization, and pressure to create an improved customer experience ASAP. Neo4j AuraDB Enterprise is a graph database-as-a-service offered by the creators of the only enterprise native graph database, providing a fully managed and automated solution for rapidly deploying, building, and expanding graph-powered applications in the cloud without managing infrastructure. This database enables companies to find innovative insights into their data, leveraging its connectedness and network effects as a competitive advantage, and is built and managed by experts who invented graph databases, making it trusted by hundreds of enterprises for mission-critical apps.

Jesús Barrasa demonstrates building a life sciences knowledge graph using public RDF sources on infectious diseases, viruses, chemicals, and scientific literature. Meanwhile, Will Lyon builds playlist components for the GRANDstack podcast application, while Lju Lazarevic explores an Archimate model in Neo4j with Cypher and Graph Data Science library. Sarah C Evans advocates for using graph databases to analyze customer survey data. Additionally, Alex shows how to visualize indirect trust networks between users using Neo4j, while Hack.Commit.Push announces a free and open-source hackathon. The Neo4j Community Forum has various job opportunities available, including helping young creators from disadvantaged backgrounds or working as Data Scientists.

This week, I've chosen to highlight a use case where Trevor Marshall, the CTO of Current, a leading U.S. challenger bank, employed Neo4j's graph database as a flexible framework for building innovative banking products that leverage relationships. Choosing Neo4j was easy due to its widespread adoption and Cypher being the standard graph language. Utilizing Neo4j provided Current with the best possible graph experience, leveraging its unique nature to power their innovative banking products.