August 2021 Summaries

Neo4j AuraDB Enterprise is a fully managed, always-on graph database service on AWS that has been launched. A digital event will be held to share enthusiasm and learn about this new offering, which can be joined from anywhere in the world on September 9. The event will cover topics such as understanding the state of the data and analytics market, insights into how Siemens and Tourism Media are using Neo4j on AWS, and discovering how to build modern graph applications with Neo4j on AWS. The panel discussion will feature experienced leaders from Amazon Web Services, Siemens AG, and Accenture, who will share their expertise on building modern graph applications, the state of the data and analytics market, and unlocking the power of graph databases on AWS. The digital event is free to attend and registration details are provided for different regions.

The Neo4j community has been busy with various exciting projects and announcements. The company is re-running its NODES 2021 workshops in September, offering a chance for those who missed the initial sessions to attend again. Several community members have shared their work on using Neo4j for different purposes, including a virtual reality documentary experience on Steam VR and an app that illustrates the "Six Degrees of Separation" theory. The community is also welcoming Nigel Small, the author and maintainer of the popular py2neo Python driver, who has moved on to new opportunities after releasing version 2021.2.0 of py2neo. Additionally, Neo4j is offering training sessions in September, covering topics such as graph-based problems, Neo4j Bloom, and building APIs with Neo4j GraphQL Library. The company's Graph Data Science library now supports subgraph filtering, and users can get up and running with Neo4j using the Sandbox or AuraDB Free tier.

Graphs are designed to tackle complex problems by making data flows and relationships more manageable, as seen in the case of tracking data lineage across large organizations. Companies like Neo4j are leveraging graph technology to build innovative applications that can handle intricate data structures and connections. The video from MANTA highlights the capabilities of graphs, while a webinar featuring industry experts discusses the benefits of building on Neo4j, including its ability to simplify complex problems and make them more accessible. By embracing graph technology, companies can unlock new insights and opportunities, making it an attractive solution for tackling complexity in data-driven environments.

Neo4j has secured funding from Eurazeo, one of its latest investors, as part of its expansion plans in Europe and beyond. Nathalie Kornhoff-Brüls, who led the investment, highlights Neo4j's superiority in the market, citing its perfect combination of technology, market readiness, and leadership. The partnership signals great things ahead for Neo4j's growth, particularly in the graph data science space. Eurazeo brings extensive experience and network to support Neo4j's expansion into Asia and Latin America. Nathalie emphasizes the importance of geographic expansion, leveraging Eurazeo's network to provide broader access to talent, commercial partners, and other resources. She also shares her approach as a board member, focusing on being a sparring partner, enabling opportunities, and drawing conclusions from related industries. As a female investor, she advises others to find something they're passionate about, dare to grab opportunities, and leverage their strengths to bring value to the table.

Neo4j AuraDB Enterprise is now available on Amazon Web Services (AWS), offering a fully automated, reliable, and secure service for mission-critical applications. With AuraDB, developers don't have to worry about managing the database, as all management is fully automated, allowing users to deploy a database within minutes, scale on demand, get automated backups, and update transparently without downtime or effort. The service supports data connectors out-of-the-box, Cypher query language, flexible schema, Neo4j Bloom data visualization, and developer tools, making it easy for users to harness AWS native frameworks and embed the service in their current architecture with ease. AuraDB is built on Kubernetes, delivers consistent performance across any cloud provider and region of choice, is secure from end-to-end encryption, role-based administration, and granular node-level access controls, and features a 99.95% uptime guarantee. The service has already been adopted by leading brands such as Levi Strauss & Co. and The Orchard to drive agility, sustainability, and product development efficiencies in their data-first, analytics-first mindset.

The author of the text is reflecting on how a virus can spread quickly around the world, using the concept of "Six Degrees of Separation" as an analogy. According to this theory, anyone in the world is connected to another person through no more than six intermediate acquaintances. The author explains that this idea was first proposed by Stanley Milgram in 1967 and has since been popularized by various media, including a film starring Will Smith. To test this concept, the author created an app called Pathica, which allows users to find connections between individuals through their social networks. The app uses a graph database, Neo4j, to efficiently analyze large amounts of data and provide results in just 0.03 seconds, much faster than traditional relational databases. The author is excited about the potential applications of this technology and plans to write more articles on its development and challenges.

The article from Twin4j covers various topics related to graph databases, specifically Neo4j. It highlights the newly-released GraphQL library 2.0.0 and provides in-depth guides on three different graph embedding methods: FastRP, Node2Vec, and GraphSAGE. The article also explores creating recommendation engines, including a recent work by featured community member Sebastian Daschner, and discusses the popular podcast topic "Will it Graph?" which explores using graph databases as a general-purpose database within a larger ecosystem. Additionally, there are articles on building real-time recommendations with Kafka and Neo4j and a tweet of the week from TheTechromancer sharing his experience with importing and analyzing OSINT data in a Neo4j graph database.

The tutorial guides readers through creating a TypeScript Apollo Server with a live Neo4j database and unit tests. It covers the process of setting up an Apollo Server, creating integration tests using `executeOperation`, and mocking out the database calls using neo-forgery for unit testing. The tutorial assumes knowledge of Node.js, Apollo Server, and Neo4j basics but provides explanations for each step. The goal is to enable readers to create an Apollo Server with TDD capabilities.

The text provides a step-by-step guide on how to get up and running with the `:PLAY movies` example in Neo4j Sandbox or Neo4j Aura Free, a graph database. The guide highlights its benefits, including using a small data set that still covers essential concepts like Cypher queries and relationships. It also explains how to navigate to both Sandbox and Aura, setting up and launching the example, and provides an overview of the Neo4j Browser tool used for exploring the dataset.

In Cypher Sleuthing: Dealing with Dates, Part 4, we explore how to combine duration functions and components to translate durations across component categories. This allows for conversions among units within a group, such as days to weeks or months to years. The article also presents two challenges: one involving Postgres SQL ranges and the other visualizing progress through the year on a weekly basis using Cypher characters. Solutions are provided for both challenges, offering insights into efficient query writing and problem-solving with Cypher's temporal data capabilities.

In the graph database space, Neo4j is a pioneer and has been at the forefront of innovation since its inception in 2007. The company's focus on the Asia-Pacific region has led to significant growth in this market. With $325 million in series F funding, Neo4j plans to expand its business by investing heavily in product development, particularly in cloud computing and graph data science. As an entrepreneur, Emil Eifrem advises young entrepreneurs to prioritize understanding their customers, rather than focusing solely on the economic buyer. This approach can lead to deep intuition about how to shape both the product and organization. With a strong partnership with major cloud platforms, Neo4j is well-positioned for growth in the graph database market.

The Neo4j GraphQL Library 2.0.0 release introduces several new features, including support for relationship properties, cursor-based pagination, and count queries. Relationship properties allow storing information on relationships between nodes in a graph, addressing the need to avoid creating unnecessary data with extra nodes and relationships. The library also improves the developer experience with union relationship fields, more comprehensive validation of type definitions, and internal refactoring and bug fixes. Additionally, the release introduces Relay-style pagination, allowing for filtering, sorting, and paginating on connection fields using cursor-based pagination. Count queries have also been added to compute the total number of pages in a query. The library has undergone extensive backlog grooming to fix many bugs reported by the community, making it easier for users to migrate or use the library with existing issues.

The Neo4j community is actively working on various projects, including the release of the GraphQL library v2.0.0, which introduces new features such as relationship properties and cursor pagination. The community also explored graph data science and MLOps with examples like visualizing stock volume similarity with Neo4j and Power BI, and building MLOps native apps with Neo4j. Additionally, there were discussions about the release of AuraDB Free and its capabilities, including a demo of the world championship chess match, as well as a comparison between Discourse and Discord for community engagement. The week also featured two community members, Daniel Starns and Darrell Warde, who are software engineers on the GraphQL team at Neo4j in London.

Resotrack is a project that uses the Resonate music streaming platform's API to suggest popular tracks for each tag in its catalog. It leverages Django-Neomodel and Neo4j, deployed on Aura Free and Heroku, to create a simple recommendation tool for users interested in discovering new music within specific genres or tags. The project starts by creating constraints on the data model, importing playlists from the Resonate API, adding tracks with their associated metadata, and then populating tags for each track. The resulting graph database provides a unique way to explore and visualize the data, allowing users to discover new music based on their interests. By utilizing APOC functions like `apoc.periodic.commit` and `apoc.load.json`, Resotrack demonstrates how to efficiently load and process large datasets from external APIs into Neo4j.

The author reminisces about 25 years ago when they were in their first Product Marketing job at Arbor Software, a NoSQL database company. Oracle's acquisition of Express, an OLAP database engine, had a significant impact on the market, making it more competitive and forcing vendors to innovate. The author notes that Amazon saw the graph market coming and made a smart bet by announcing its graph cloud service, Neptune. Graph databases have gained traction, with Forrester publishing the first-ever Forrester Wave for Graph Data Platforms in late 2020. Research firms have continued to produce research on graphs, highlighting their importance in AI/ML efforts and business outcomes. The market has seen significant investment, with over half a billion USD poured into graph database companies in the first half of 2021. Despite challenges ahead, the author is excited about the future of the graph market.

A graph database can be used as a general-purpose database in applications that don't rely on a single database, allowing developers to use it as their primary source of truth. The choice of database depends on the ecosystem of framework and language integrations available, with Neo4j offering a wide range of drivers and tooling for integration into various tech stacks. Graph databases are particularly useful for speedy prototyping, enabling developers to quickly ask questions about data without declaring a schema. They also provide an intuitive way of modeling, storing, and querying data, which aligns with how we think of relationships in applications. By using a graph database as a general-purpose database, developers can rapidly prototype projects, answer questions quickly, and optimize their data storage structure easily.

This blog post guides developers through deploying Neo4j on Kubernetes using Helm Charts. The author, who has experience with Neo4j and Docker Desktop, aims to demonstrate how accessible Neo4j is for Windows users outside of the usual options. The process involves installing Docker Desktop, enabling WSL 2.0, and setting up Minikube and Helm for a seamless deployment. The post provides detailed instructions on configuring the Helm Chart, creating a custom YAML file, and accessing the Neo4j database from within the Kubernetes cluster or outside it using port forwarding. It also covers debugging and troubleshooting tips to ensure successful deployment and operation of Neo4j on Kubernetes.

The Neo4j GraphQL Library has reached version 2.0.0, featuring several new and improved features such as support for relationship properties, cursor-based pagination, count queries, and improvements to the developer experience with union relationship fields. The library's design decisions were guided by goals of low friction, easy understanding, and familiarity constructs, drawing inspiration from Relay and GraphQL Cursor Connections Specification. This release also includes non-functional improvements and breaking changes that require attention during upgrades, which are addressed in a migration guide.

Neo4j is being actively used by its community members for a variety of applications, including Twitter troll analysis, graphy problem detection, and knowledge graph-based solutions such as NASA's talent mapping database. The community is also exploring the use of Neo4j with other tools like AWS Kinesis and Spark, and has developed open-source Python packages to facilitate data backup and transfer. Additionally, Neo4j is being used in creative ways, such as calculating the "Kanye Number" for artists using Musicbrainz data, and a fun demo showcasing its power with Cypher queries.

Neo4j's recent webinar featured a discussion on the marriage of graph data platforms, artificial intelligence, and digital twins. The session was hosted by Neo4j Chief Scientist Dr. Jim Webber and included two VPs from Dataiku and Neo4j themselves, who shared their experiences with digital twins. A digital twin is essentially a virtual model of a system or object. This topic is gaining interest in the industry, as evidenced by its recent inclusion in a Neo4j webinar. The discussion highlights the potential benefits of combining graph data platforms, AI, and digital twins for various applications.

Throughout my career, I've consistently emphasized the importance of data, whether it's processing speed or storage capacity. As a marketer, data has been crucial in discussions, and its uses have expanded beyond direct contexts to include ancillary applications. The focus shifted from data generation to collection, leading to questions about the value of collected data. However, making sense of data requires a humanized approach that connects one piece of data to another and adapts to changing behavior. Table-based data organization is insufficient for real-time tracking and recommendation engines, which demands a more holistic perspective. Connecting data points provides new insights, enables discovery of hidden information, and leads to knowledge, ultimately driving innovation and solving complex problems like climate change.