October 2019 Summaries

The text discusses how to turn a stream of records into a graph and leverage graph processing to enrich and visualize data, with a focus on integrating this with event streaming applications like Kafka. The author uses Neo4j, a native graph database, to store and query the data as a graph, using Cypher, a declarative query language, to describe patterns in graphs. The text then covers how to use graph algorithms, such as the Common Neighbors Algorithm, to generate recommendations for potential friends in a social network example. Finally, it shows how to publish the results of these graph queries back to Kafka, either by publishing ad-hoc or by using Neo4j's streams source feature to automatically publish nodes and edges as they're created. The author concludes that this approach can be applied to any event streaming application, effectively adding "graph superpowers" to it.

In the wake of California's CCPA regulation, Nevada and New York have introduced their own privacy regulations, with Canada, Mexico, Texas, Washington, and many other states closely monitoring the development of personal data privacy compliance. To address these complex data lineage problems, a modern graph database platform like Neo4j is considered superior to relational and most NoSQL technologies. Traditional approaches to data privacy compliance often require colossal mazes of JOIN tables and thousands of lines of SQL code, leading to complexity and maintenance issues. In contrast, Neo4j stores and connects data as a graph, making it the ideal technology for privacy compliance. To achieve compliance, organizations can follow four steps: inventorying their systems, building a logical data model, developing and testing their system using Neo4j's Privacy Shield Framework, and visualizing and responding to compliance requests with tools like Neo4j Bloom. With Neo4j, organizations can propel themselves down the fastest and most cost-effective path to personal privacy compliance.

This week the Neo4j community is celebrating several exciting developments. Will Lyon has released the early access version of his book "Fullstack GraphQL Applications with GRANDstack", a comprehensive guide to building GraphQL applications using Neo4j. Max De Marzi has published a blog post on analyzing marketing activities, while Scott Sosna shows how to interrogate US Lobbying data in Neo4j. Nathan Smith has solved the perfect dorm room assignment problem using Neo4j's Graph Algorithms Library. Meanwhile, Augustine Correa, a passionate community organizer and thought-leader, is being featured as this week's community member of the month. The community is also celebrating the release of Will Lyon's video from the NODES 2019 conference on visualizing graph algorithms with Neovis.js.

Kineviz's GraphXR is a browser-based visualization tool that leverages Neo4j to bring speed, power, and flexibility to data exploration. Sony Green and his team use Neo4j with GraphXR for visual analytics and graph or data visualization to explore and analyze data beyond just numbers. Neo4j offers flexibility and power, enabling GraphXR to treat data in a different way by combining the structure of the graph with underlying statistical data. GraphXR is used in various domains such as law enforcement, healthcare, business intelligence use cases, and has been implemented in NIH's bioinformatics portal and the International Anticrime Academy. The tool provides dynamic modeling capability, allowing users to ask different questions without having to go back to the drawing board every time. GraphXR aims to make data literacy accessible to non-data scientists by leveraging graph visualization, which is closer to how humans think about things than tabular data.

The author of this text is a fan of "RuPaul's Drag Race" who, inspired by their friends' enthusiasm for the show, decided to create a graph database using Neo4j to analyze and visualize the data. The graph represents contestants and episodes, with relationships between them describing the outcome of each episode. The author explores the structure of the graph, extracting insights from it, such as identifying patterns in the results of season winners and predicting the winner of "Drag Race" UK Series 1 based on these patterns. The text also touches on potential applications of knowledge graphs beyond entertainment, including industries like retail and telecom.

The California Consumer Privacy Act (CCPA) imposes strict penalties on organizations that misuse and resell consumers' private information, with Nevada and New York having introduced their own privacy regulations. To meet CCPA requirements, enterprises must track personal data across various systems, provide transparency into data usage, and prove compliance to regulators. Graph databases are considered the right foundation for data privacy compliance due to their ability to model and visualize complex data relationships, enabling easy answers about personal data for regulators, data protection officers, and individual consumers.

We've returned from our team offsite and NODES 2019 conference in Sweden, where we showcased Neo4j's capabilities. The Developer Relations team is now gearing up for GraphConnect 2020, seeking community contributions to make the developer track exciting. We launched a new Educator Program, which includes features such as a comparison of the Northwind dataset in Neo4j and PostgreSQL. Our featured community member this week is Ryan Boyd, who has started a new job as Head of Developer Relations at Databricks. The NODES 2019 conference videos are now available on YouTube, featuring talks like Andrew Bowman's Tuning Cypher. We're closing our Call for Papers for GraphConnect 2020, and we need your help to fill the schedule with experience reports or talk ideas. Additionally, we've shared blog posts on various topics, including British MPs voting similarity using the Pearson Similarity algorithm, loading US lobbying data into Neo4j, and a comparison of the Northwind dataset in Neo4j and PostgreSQL. Our new Educator Program aims to empower educators teaching Neo4j in classroom labs, with features like official training materials and dedicated support channels.

This week, I've chosen to highlight the Bloom Series, a brief six-parter that spotlights some of Neo4j Bloom's standout product features. The series traverses through various capabilities in Bloom, including natural language search, graph exploration, graph visualization and advanced search features. You can catch all our videos on the Neo4j YouTube channel, updated weekly with tons of graph tech goods, or subscribe to be notified when new content is published.

GraphConnect 2020 is a conference focused on graph database technology, particularly Neo4j. The conference will feature two days of sessions and has opened its call for papers, encouraging attendees to submit their presentation ideas on various topics related to graph databases. Topics of interest include digital transformation with graphs, artificial intelligence and machine learning, knowledge graphs, design thinking, and sustainability, among others. The first deadline for submissions is November 15, after which the opportunity will be open until all slots are filled.

We're going to talk about some project themes that make sense when you're developing a graph project. We'll dive into the business problem and discuss the specifics of the business problem. We are going to cover manufacturing quality, how graphs apply to that problem and how this helped us get some business value through performance improvements and new capabilities. We'll also talk about the evolution of our graph model. We will discuss where it started and where it's going. Boston Scientific is a global medical device company that makes a lot of different therapies aimed at making procedures less invasive and have better outcomes for patients. They make pacemakers, stents, artificial heart valves, diagnostics, and more. The analysis of their supply chain is nothing new, but they've been doing it for years. Recently, they improved the output of the analysis by finding new methods and technologies that could improve the output. Tasked with finding alternatives and improving the methods, they set out to find a solution. They have gained business value by adopting Neo4j, primarily in non-functional areas such as simplicity, explainability, and white-board friendly nature of graphs. The data is much more accessible than it otherwise was. This has increased the efficiency and helped streamline the entire analytical process. The largest value they've gained isn't coming from performance improvements but from these non-functional areas. They've also gained new capabilities through Neo4j such as shortest paths and variable length queries, which have been incredibly useful to their business.

The California Consumer Privacy Act (CCPA) is a set of personal data protection regulations passed by the State of California in 2018, taking effect on January 1, 2020. The regulations aim to address the risks associated with the collection, use and resale of personal information about California residents. CCPA requires businesses to enable residents to know which information is collected, examine it, request its deletion, know if any of their information is sold or disclosed, block the sale of their information, and enjoy the same level of service whether they opt for privacy or not. While similar in spirit to the European Union's General Data Protection Regulation (GDPR), CCPA has some key differences in focus and scope. The regulations are aimed at protecting consumers' personal data and promoting transparency, while also addressing the resale of personal information. Businesses can address these requirements using powerful graph database frameworks, such as Neo4j, to manage their data and ensure compliance with CCPA and other privacy regulations.

The Neo4j Online Developer Conference (NODES) was held in Ängelholm, Sweden, where a team of developers from various countries gathered to share knowledge and experiences. The conference featured over 50 sessions across five tracks, with speakers presenting on topics such as the Neo4j community, new features in Neo4j 4.0, and graph algorithms. Keynote speaker Emil Eifrem previewed the upcoming release, while other talks covered various aspects of Neo4j development. The conference also saw the launch of several new projects, including a chat bot built on Neo4j, releases for Neo4j OGM and jQAssistant, and an Approximate Nearest Neighbors algorithm. Additionally, the team highlighted the achievements of community member Surya Gupta, who presented at various conferences and events, and showcased her work on protein-association networks. The conference concluded with a tweet from Rob Mayer expressing his excitement about discovering Neo4j for the first time.

The Global GraphHack 2019 event saw 17 teams work on innovative applications with Neo4j at their core, focusing on extending the graph ecosystem. The submissions were impressive, ranging from graph creation tools to solutions for performance optimization and even linear algebraic equations. The three winning teams will receive prizes including airfare and tickets to GraphConnect 2020, as well as opportunities to present their projects and meet with Neo4j executives. The event showcased remarkable projects from seasoned engineers to newcomers, highlighting the potential of the Neo4j community.

Neo4j has updated its GraphAcademy University Program with new features specifically designed for instructors teaching Neo4j in a classroom lab setting. The program is open to qualified instructors of accredited, non-profit educational institutions who can fill out a form to be evaluated for acceptance. Qualified instructors will receive access to the Neo4j Desktop application on multiple machines, operating instructions, and official training materials, as well as a dedicated group channel for educators to share tips, tricks, and educational content, and ask questions. The program is designed to help students learn about Neo4j and introduce graph database concepts to the education community, with resources available to support instructors in teaching Neo4j in the classroom.

The text discusses various topics related to Neo4j, a graph database platform. The Neo4j Online Developer Summit is coming up and several talks have been previewed, including ones on building Kubernetes custom operators, graph visualization, and using Neo4j for data science projects such as learning Japanese characters and creating chatbots. Additionally, the text mentions updates on the Neo4j ETL tool, which is now cloud ready, and features a community member, Dr. Estelle Scifo, who has published articles and videos on graph analytics and participated in the Global Graph Hack.

Neo4j has helped NASA capture and connect decades of lessons learned by enabling the visualization of complex data relationships across various information domains. According to David Meza, Chief Knowledge Architect at Johnson Space Center, Neo4j's graph database capabilities have allowed him to quickly pull out relevant information from large datasets, leading to faster query results compared to traditional SQL databases. The speed and ability to visualize connections between entities are key aspects of Neo4j that resonate with Meza, particularly in research applications where identifying knowledge gaps is crucial. Despite some initial challenges, Meza wishes he had focused more on learning Cypher, the graph database language, earlier in his journey. Looking ahead, Meza expects graph technology to expand and become more accessible, with new features like multiple database instances and enhanced visualization capabilities on the horizon for Neo4j.

The GraphHack is a community-driven project that encourages developers to build projects that benefit others in the Neo4j ecosystem. The theme of this year's GraphHack was "Extending the Graph Ecosystem", and participants were tasked with building something that would be useful to the community. A total of 20 projects were submitted, showcasing various use cases such as web scraping, graph visualization, and data analysis. Some notable projects include NeoWithNic, a tutorial for beginner programmers, and Baryon, a react component for composing organized UI views for Neo4j graphs. The winners of GraphHack will receive compensation, an exclusive invite to the Neo4j Ecosystem Summit, and the opportunity to present their project in DevZone at GraphConnect 2020.