December 2019 Summaries

It's been a decade since Neo4j was first established as a small open-source community in Malmö, Sweden. Over the past 10 years, the community has grown significantly, with hundreds of customers and thousands of developers worldwide. The company has received significant funding, including an $80 million series E round in 2018, making it the largest single investment in the graph space. Neo4j has also become a leader in the graph database sector, with various notable milestones such as releasing its first commercial product, Spring Data Neo4j, and launching its fully managed native graph database as a service, Neo4j AuraDB. The company's openCypher project helped standardize the Graph Query Language (GQL), which was later adopted as an ISO/IEC international standard. Additionally, Neo4j has been involved in various initiatives such as data journalism, artificial intelligence, and data visualization, with notable events like the Panama Papers being powered by Neo4j. As the next decade begins, the community is looking forward to continued growth and innovation, with a focus on building relationships one connection at a time.

The text highlights several significant events that shaped the graph database industry in 2019, including the initiation of GQL as an international standard, the launch of Neo4j AuraDB, a fully managed native graph database as a service, the first-ever NODES online conference, the expansion of the Neo4j Educator Program, and partnerships with Google Cloud and O'Reilly Media. These events demonstrate the rapid growth in popularity for graph technology and its increasing accessibility to developers and businesses. The text also mentions new products and integrations, such as Neo4j Bloom for graph exploration and data visualization, ignio from Digitate, and Neo4j Streams for real-time correlations on Apache Kafka. Overall, 2019 was a year of significant milestones for the graph universe, setting the stage for future growth and innovation in the industry.

The Neo4j company has curated a selection of its best videos from 2019, showcasing various applications and use cases of graph technology. These videos feature notable figures such as Emil Eifrem, the CEO of Neo4j, Mark Grover from Lyft, Amy Hodler, Analytics & AI Program Manager at Neo4j, and experts in fields like scientific research and open source intelligence. The selections cover a range of topics including data discovery, responsible AI, knowledge graphs for space exploration, and running Neo4j in multi-data center environments.

In 2019, the Graphistania podcast featured some notable interviews with innovative graph thinkers and practitioners from around the globe. The podcast was rebooted with a new format and co-host Stefan Wendin from the Neo4j Innovation Lab in December. This episode covered various topics including graph database technology, applications, and everyday use cases. A double interview with Jess Mason and Jason Cox discussed their work with Untitled Folder and data journalism projects. Another notable episode featured Amy Hodler from Neo4j discussing graph analytics for artificial intelligence and machine learning. The podcast also explored the future of graph tech space and its place in the larger big data ecosystem.

Oh, the exciting world of Neo4j blogs from 2019. Jocelyn Hoppa, Managing Editor at Neo4j, dives into her personal favorites among the numerous substantial and smart blog posts that kept her busy in the graph database space. Eight standout blogs are highlighted, covering topics such as responsible AI via real-life examples, the newly approved GQL project, graph theory and its application to solving complex problems like the opioid epidemic, the Neo4j community's celebration of Global Graph Celebration Day, and more. These engaging blog posts showcase the innovative work being done in the world of graph databases and their applications.

The Neo4j community is active and has been publishing various content, including videos from the NODES 2019 conference. Gerrit Meyer introduced the all-new Spring Data Neo4j RX, which uses the reactive driver architecture of Neo4j 4.0, to provide an even better support for mapping business domains in the Spring ecosystem. Alicia Frame released version 3.5.13.0 of the Neo4j Graph Algorithms Library, containing goodies such as K1-Coloring and optimized node similarity algorithms. The community also published guides, tutorials, and blog posts on various topics, including Bloom, GraphXR, and data visualization. Robin Moffatt, a featured community member, was mentioned for his work on Kafka-related topics and life lessons for developers. Michael Hunger and the Developer Relations team concluded the newsletter with holiday greetings and an announcement that they will skip sending the newsletter in the next week.

Neo4j is being used by NTT Data Services to implement deep access control, a critical area that handles complex security and access management. The company has partnered with Neo4j to analyze and manage access controls for various clients, including those with 5,000 to 10,000 users. By moving raw data into Neo4j, they can easily identify loop points and determine if a person should have access to certain areas. This approach also helps when a new employee joins or leaves the company, ensuring that their access is properly managed. Manish Jain, from NTT Data Services, finds Neo4j's graph technology particularly useful for this analysis, as it allows for easy comparison between old and new solutions, and can convert large databases into Neo4j, saving storage and improving performance.

The author of the text started by thinking about how digital content recommendations work and wondered if they could be applied to their own life. They decided to explore a large music graph for inspiration and thought it would be fun to try to find connections between their musical tastes and those of Neo4j CEO Emil Eifrem, who they had heard was a music lover. The author did some research on social media to learn more about Emil's musical preferences and found a few playlists that gave them a starting point for their graph model. They then loaded data from the MusicBrainz open music encyclopedia into the graph and created relationships between artists, recordings, and tags. After listening to some tracks by Robyn, they discovered a connection between her music and one of their favourite bands, Hercules & Love Affair, through the shared use of certain tags. The author also found connections between Emil's list of favourite artists and their own list, including the artist Loney Dear, who was entirely new to them. They used graph algorithms to compare the tags for both lists and identified some interesting overlaps, but noted that there were still gaps in the data that needed to be addressed before they could build a complete music recommendations engine. Ultimately, the author felt that their experiment had been successful in finding common ground between themselves and Emil through music, and hoped that it would inspire others to explore the power of graph technology for personalization.

Neo4j can be run quickly and easily on three major cloud platforms: Amazon Web Services (AWS), Google Cloud Platform (GCP), and Microsoft Azure. The company offers a self-managed cloud delivery option, allowing users to deploy Neo4j on their preferred cloud provider. This approach provides flexibility for power users who need customized configurations. Neo4j also supports Docker containers and Kubernetes, which enable users to run Neo4j in a containerized environment or as part of a larger Kubernetes cluster. The company's technology partner architect David Allen discusses the various modes of running Neo4j, including single instance, clusters, containers, and Kubernetes. He also explores deployment considerations, network security, and the benefits of using BYOL (Bring Your Own License) models. Additionally, Allen covers the different options and trade-offs for each cloud platform, including AWS, GCP, and Azure. The presentation provides an overview of how Neo4j can be run on these platforms and offers a range of deployment approaches to suit different needs and use cases.

A graph in the context of computer science and technology is a network of entities and how they relate to each other, consisting of nodes and relationships. It's a mental model that can resemble a list, a tree, a map, or any other structured entity. Graphs were first studied by Leonhard Euler in 1735 as part of the Königsberg bridge problem, and since then have been applied in various fields such as computer science, chemistry, social sciences, and database management. The graph data model is used to store and query data in a more flexible and efficient way than traditional relational databases, allowing for connection-oriented queries and enabling companies like Google, Facebook, LinkedIn, and PayPal to build their business empires. Graph technology offers agility, flexibility, and performance, making it the future of innovation and simplifying complex connections between entities.

Louise Söderström explains schema-based security in Neo4j 4.0, highlighting changes to user and role administration and new administrative commands. Meanwhile, Jorge Albarrán explores the Starwars Galaxy using Neo4j, Jesús Barrasa enriches a knowledge graph with Wikidata, and Eric Solender releases a Go OGM library. Rik analyzes products bought together in a Carrefour Basket Data Challenge, while Keith Damiani is featured as a community member for his work on Neo4Laravel, a library to build graph-backed Laravel applications. The Neo4j community thanks Keith for his contributions.

This week, I wanted to feature the reboot of the Graphistania podcast, hosted by Rik Van Bruggen and his new co-host Stefan Wendin. They started up Graphistania 2.0 with a new monthly format focused on innovative projects happening in the Neo4j community. The podcast is an essential resource for staying updated on the latest developments and trends in graph technology, particularly those reimagining what's possible by harnessing connected data. You can catch all their episodes on the Neo4j YouTube channel, which is updated weekly with tons of graph tech goods.

Walter Trotta, Global Head of Data Services at Citi Private Bank, presented on the use of graphs in private banking. He defined private banking as an industry that provides full financial services to individuals with a certain range of wealth, which impacts complexity and relationships with clients. The data domain refers to the party entity, including individuals, organizations, or groups of organizations. In private banking, Citigroup faces challenges in managing client relationships, with multiple stakeholders involved, such as spouses, children, grandchildren, lawyers, accountants, family offices, and more. Graph technology was used to solve these problems by creating a graph database that can handle complex queries and relationships between clients, their relationships, and the bank's interactions with them. The presentation highlighted the benefits of using graphs in private banking, including improved data governance, reduced latency, and increased value from data. Key takeaways include the need for fresh thinking, collaboration with business stakeholders, and a focus on business rules and conditions to model graph structures effectively.

The Neo4j community has been actively engaged with various content creators and developers, including the NODES 2019 conference. Christophe Willemsen shared tips and tricks for using full text search functionality in Neo4j, while Jimmy Crequer used Neo4j to learn countries of the world and build a graph-based CLI tool to help him remember Japanese characters. The community has also seen contributions from Michal Trnka on GRANDstack, Andrea Santurbano on transforming MongoDB collections into graphs, Rik on querying data from the Carrefour Basket Data Challenge, and Michael Simons discussing reactive Spring Data Neo4j. Additionally, Jesús Barrasa shared a tweet about importing Wikidata fragments into Neo4j with Neosemantics, highlighting the importance of graph visualizations in understanding complex relationships between data entities.

Graphistry is a company that leverages GPU technology to provide customers with the ability to view and analyze large amounts of data, significantly more than what they can currently see. This is achieved through its partnership with Neo4j, a graph database that has been around for longer and has distilled some of the problems associated with graph databases. Graphistry's technology allows customers to process information faster and make more informed decisions. The company recently released Graphistry 2.0, which builds upon its previous version and utilizes Nvidia's RAPIDS framework to further enhance data processing capabilities. Graphistry partners with Neo4j due to their complementary business approaches and shared struggles in the field of cybersecurity investigations. Looking ahead, Graphistry believes that visualization will play a significant role in helping customers interpret large datasets, particularly in fields like security where automation is key.

Michael Zelenetz, an analytics project leader at NewYork-Presbyterian Hospital, discussed the challenge of combining spatial and time data to understand hospital procedures, infections, and patient movements. He faced difficulties in graphing these two types of data together, but turned to graph databases, algorithms, and analytics to map out events in the hospital. Zelenetz proposes a model that combines time and space data by creating time trees and location trees, which are then connected with "time-place-space entities" representing visits at specific locations over time. This allows for advanced analysis on how diseases move around and who might be at risk.