March 2021 Summaries

Neo4j has appointed Kristin Thornby as its new VP of People, bringing her expertise in building award-winning cultures from the ground up. The company's leadership team is excited to have her on board, and she's looking forward to creating innovative systems and processes that attract top talent. Kristin joins Neo4j at a significant time, with the company growing rapidly and navigating the challenges of remote work due to COVID-19. As the first people leader in Neo4j's history, Kristin is focused on ensuring a clear career path for employees, developing a framework for growth and development, and addressing potential drawbacks of remote work, such as silos forming. She emphasizes the importance of kindness, empathy, and tough feedback in her approach to building a strong company culture. For those considering working at Neo4j, Kristin advises being aware of the startup mindset and hypergrowth, but also notes that the company offers an incredible team environment with brilliant, funny, kind, laid-back, and motivated employees.

The past year has seen significant advancements in innovation, particularly in graph technology. Companies such as Levi Strauss & Co., Boston Scientific, ADEO, and Worldline have adopted Neo4j AuraDB Enterprise to build applications for recommendation engines, supply chain management, digital transformation, and more. The adoption of graph technology is expected to continue, with trends driving interest including the need to tackle major issues like fraud rings and promoting diversity in the workplace. Experts predict that graph technology will be a dominant force in the 2020s, with potential applications in areas such as data analytics, fraud detection, and leadership development.

The Neo4j Certifications offer a way for developers to demonstrate their expertise with the graph database, which is widely used in application development. The certifications are free and include three exams: Neo4j Certified Professional, Neo4j 4.x Certified, and Neo4j Graph Data Science Certified. These exams test knowledge of Neo4j 4.x features, Cypher, multi-database management, and graph data science. The passing grade is 80% and candidates can take one exam every 24 hours. The certifications are updated regularly to reflect the evolving products offered by Neo4j.

Marco Falcier has updated the neo4j-versioner-core library, which helps track entity state in Neo4j databases. The closing date for this year's Neo4j Online Developer Expo and Summit (NODES) call for papers is April 5. Cristina and Alisson have migrated the GraphGist portal to the GRANDstack, while Will Lyon has started testing polyglot with Github Actions, Lju Lazarevic has created a fun game to play with data, and Davide Fantuzzi has built a CI pipeline for polyglot. A new podcast called GraphStuff.FM is now live, hosted by Will Lyon and Lju Lazarevic, exploring all things graph and connected data. The Developer Relations team also highlights Zuye Zheng, a Principal Architect at Salesforce who leads the development of the Salesforce Analytics experience, showcasing his interesting article on graphing Reddit posts with Neo4j.

GraphAware is a company that aims to help organizations adopt graph technologies by sharing expertise and building tools that make it easy for them to do so, with the goal of making graphs ubiquitous in ten years. The global expansion of GraphAware has brought unique challenges but also opportunities, including access to global talent and the ability to serve customers globally. During the COVID-19 pandemic, GraphAware learned to work remotely effectively and discovered the benefits of remote working for both employees and customers. The company is now focusing on its graph-powered insights engine, Hume, which aims to make it easier for people to adopt graphs and leverage their power. By doing so, GraphAware is executing on its mission to enable graph adoption and become a trusted partner in every organization's graph journey.

The text discusses the integration of a graph database with a Django application using Neo4j's Django-Neomodel module. The example app, Paradise Papers Search, uses this module to connect to the Neo4j Sandbox and search for entities, officers, intermediaries, and addresses in the Paradise Papers dataset. The article provides step-by-step instructions on setting up the local environment, deploying to Heroku, and using a Neo4j Aura database. It also highlights the benefits of using Neo4j with Django, including improved performance and scalability. Additionally, the text mentions the importance of data modeling and serialization in graph databases like Neo4j. The authors provide resources for further learning, including tutorials, documentation, and articles on Neo4j and its ecosystem.

The Neo4j community has migrated its online chat from Slack to Discord, a platform that allows for dedicated spaces for communities to hang out, text, video chat, or stream. The main motivation for the change was to overcome Slack's 10k message limit and gain more functionality in engaging with community members. The new Discord server offers features such as chat, video calls, live streaming, news, and events, making it a more suitable platform for the Neo4j community. The server is now live and accessible, with a variety of roles, channels, and bots to help users get started and navigate the community. The Neo4j team will also be hosting "Ask Me Anything" open office hours events to celebrate the launch and engage with the community.

We are thrilled to announce that Neo4j has been recognized by the industry leading, independent market intelligence organization Tech Breakthrough as the winner of 2021 Data Breakthrough Award for “Graph DBS Solution of the Year.” Neo4j powers more than 2,000 startups, 800 enterprises, 75% of the Fortune 500, and 200K+ developers, delivering a definitive foundation for connected data at unlimited scale and developer velocity. The award is a testament to our customer obsession and solving real-world problems with our products at unlimited scale and developer velocity. Neo4j is used by top retailers, aircraft manufacturers, insurance companies, banks, automakers, hotels, and telcos, boasting an active developer community and concluding 72,000+ meetups. Winning awards for our technology reflects our dedication to innovate and improve our product, helping customers solve critical connections in fraud detection, medical research, and more.

Neo4j, a leading graph database provider, is actively investing in the development and advancement of women employees through its sponsorship of LWT (Leading Women Together) and WILpower programs. These initiatives aim to empower women leaders by providing them with meaningful talent development opportunities, fostering a supportive community, and promoting their growth into stronger leaders. The WILpower program has been instrumental in helping Plushcap, a woman leader at Neo4j, gain confidence and develop essential skills such as public speaking, creativity, and emotional intelligence. By recognizing the importance of teamwork, nurturing strengths, and managing weaknesses, women can overcome the challenges of perfectionism and become more effective leaders. The LWT program offers a unique opportunity for women to reflect on their journey, unleash their expertise, and contribute to their company's growth.

The blog series explores the top 10 use cases for graph technology, including its application in solving tough problems such as managing Bill of Materials (BOM) in manufacturing firms. The BOM process is still crucial in manufacturing processes despite digital transformation, but new data management challenges arise due to the proliferation of multidisciplinary products and distributed teams. Graph technology is a good fit for this use case due to its ability to store and model interconnected master data as a graph. A real-world example from the U.S. Army demonstrates how Neo4j's graph database can quickly store, explore, and visualize logistical data, reducing data loading time from 60 person-hours to seven to eight hours, and enabling better ordering, budgeting, and parts delivery. The use of graph technology provides a 360-degree view of BOM master data, enabling meaningful connections between people, accounts, business entities, transactions, and other data, and facilitating real-time decision making.

The text discusses the growing importance of Graph Data Science and its applications in various fields, including supply chain management and genome analysis. Dr. Jim Webber, Neo4j's Chief Scientist, shares his insights on the benefits of Graph Data Science and its tools, while Pavan Kumar Kattamuri visualizes Google Cloud IAM hierarchy using Neo4j. Sixing Huang analyzes genomes using graphs, and Rik Van Bruggen presents a session on Graphistania, where he refactors graph models and tests performance with neo4j-locust. Additionally, Papa Yaw Ofori-Afriyie introduces graph databases using Uncle Kojo's family tree, while Frédéric Daniel, the featured community member, explains why graphs are great at querying supply chains and shares his experience presenting on their use in sustainable sourcing. The text also highlights a tweet from Axel Morgner about the upcoming release of Structr 4.0 with exciting features for Neo4j users.

Amy Hodler joins us from the Boulder BI Braintrust to discuss the growing interest in knowledge graphs, which are being used in real-world applications such as fraud detection and early interventions for long-term health conditions.

Thomson Reuters, a group working with state, federal, and local governments, is indexing its information to enable analysts and subject matter experts to easily search for insights. The company faces challenges in putting data into the hands of these experts due to data silos and the need for contextual or predictive search capabilities. To address this, Thomson Reuters uses knowledge graphs, which are a key component of its architecture. The graph is built using multiple stages, including mapping, stitching, tagging, and indexing, with Neo4j playing a crucial role in the indexing phase. The company's goal is to provide a searchable index that enables subject matter experts to gain insights from the data. Thomson Reuters has applied this approach to various use cases, such as localized centrality, relationship validation, and investment trends analysis. The company invites others to join graph conversations and provides resources for entity resolution, open identifiers, and graph literacy.

The Neo4j Connector for Apache Spark is a rewritten library that leverages the new DataSource API V2, allowing for multi-language support. It was developed to provide an official library and continuous service, replacing the old connector with custom "hacky" solutions. The development process was challenging due to lack of documentation, breaking changes between minor versions, and dealing with different Scala versions supported by each Spark version. To overcome these challenges, the team created a solution that maps nodes and relationships into tables using columns for properties and IDs. They also extracted a schema from a schema-less graph, handling cases where properties may have mixed types across nodes. The connector uses the official Neo4j Java Driver and generates Cypher queries through the API. It offers examples in Scala, Python, R, and more, with plans to release support for Spark 3.0 and 3.1 soon.

The authors of a content management system (CMS) for Neo4j Graph Examples migrated their Ruby on Rails-based portal to GRANDstack, a GraphQL-based stack that combines React and Apollo. The CMS is a self-serve platform where users can view, create, and edit graph examples, as well as rate and publish them. GRANDstack brings several benefits, including the ability to easily set up a new app with little to no previous experience with Cypher, automatic GraphQL-to-Cypher translations, and a starter kit that allows developers to define a simple data schema and manipulate data via an auto-generated GraphQL API. The CMS features anonymous users who can browse and view content, authenticated users who can create and edit graph examples, and administrator users who can manage content, assign categories, and handle notifications. The code for the CMS is available on GitHub, and GRANDstack provides resources for building full-stack GraphQL applications with ease.

Graph databases are being used by companies like Telenor Norway to solve complex problems in identity and access management. These systems store information about parties and resources, along with the rules governing access to those resources, and can handle the complexity of multiplex access and control structures. Graph technology is particularly useful for managing large amounts of data and complex relationships between entities, allowing for faster and more efficient queries than traditional relational databases. The use case from Telenor Norway showcases how a graph database solution improved performance and scalability, reducing queries that once took minutes to milliseconds, and providing better insights and controls for managing identity and access needs in real-time.

Neo4j's Developer Relations team has published a week's worth of videos and blog posts covering various topics related to graph databases, including fraud detection with graphs, graphing r/WallStreetBets and r/Investing communities, migrating SQL Server graph databases to Neo4j, phonetic search in Neo4j 4, Monaco Editor, OpenTelemetry Instrumentation, implementing Hidden Markov Models for POS tagging using Neo4j graphs. The team also highlighted a featured community member, Michael Moore, who is an expert in deploying large-scale knowledge graphs using cutting-edge technologies and has spoken at multiple GraphConnect and GraphTour events.

RLE International, an automotive engineering services provider in Germany, uses Neo4j to help startups in the automotive industry create individually tailored project plans. Before adopting Neo4j, they used a vast Excel spreadsheet with over 200 tabs, which was time-consuming and subjective. With Neo4j, they can quickly query their knowledge graph to provide startups with optimized project plans, reducing calculation times from weeks to minutes. The company appreciates the ease of debugging in Neo4j, making it an essential tool for their work. Graph technology is expected to become increasingly popular across industries as its potential and usefulness are discovered, and RLE International aims to explore more graph data science and artificial intelligence algorithms in their future projects.

The latest version of Neo4j Browser has been released, bringing several exciting changes to improve the user experience when writing and running Cypher queries. The browser now uses the Monaco editor, previously used by VS Code, providing syntax highlighting improvements, better autocomplete for browser commands, and a command palette (F1). A new feature called reusable frames allows users to edit query parts directly in an existing result frame, maintaining its own local history of commands and updating the main Cypher editor. The Favorites management has also been improved, with streamlined processes for creating and managing favorites. Users can now provide feedback through an easier-to-use interface, including submitting suggestions, requesting features, and seeing what others are talking about. Additionally, users who participate in user research sessions will be compensated for their time.

Graph databases are being used by financial institutions to solve their extensive data lineage challenges, which require tracking data connections across complex networks of investments and holdings. These organizations face stringent requirements, such as BCBS 239, that demand flexibility and persistence in tracing data dependencies through multiple data silos. Graph technology offers a single source of truth, revealing complex or hidden data connections within milliseconds, and can integrate information into a single, enterprise-wide logical data model. A real-world example is UBS, which used Neo4j's graph database to solve its data lineage challenges by modeling their data and providing governance over data movement, enabling consumers to understand the datasets they offer and how they relate to others. This approach offers beneficial outcomes such as tracing risk factors back to original data sources, spanning multiple data silos into a unified dataset, and handling mergers and reorganizations that affect trading desks.

Florent Biville builds out an Angular.js frontend for the Spring Data 6 application, while Jesús Barrasa computes similarity metrics of a taxonomy and Lju Lazarevic shows how to import and model a Retail Food Store dataset. Kristof Neys integrates Neo4j and the Deep Graph Library. Eric Wespi, a Data Scientist at Boston Scientific, was featured as a community member, having presented on using graph analytics for manufacturing quality improvement. The team shares various projects, including integrating Virtuoso SPARQL Query Results into a Neo4j Property Graph, merging nodes in Neo4j, and building the frontend with Angular.js. Tomaz Bratanic's tweet about Grammarly and Neo4j was also shared as the "Tweet of the Week".

The text discusses a video interview with Kara Doriani O'Shee, the Content & Demand Gen Copywriter at Neo4j, where she explains the theory behind graph database technology. The company is promoting their YouTube channel and encouraging viewers to explore graph databases and its endless possibilities. They also invite viewers to subscribe to the channel for updated weekly content.

The Neo4j Graph Data Science team has introduced a new certification exam for Neo4j Graph Data Science, which is designed to test skills in using the Neo4j Graph Data Science Library and performing analysis of graph data. The exam covers general use of the library, graph data science workflow, and specific graph algorithms. To prepare for the exam, developers and data scientists can take free online courses that cover topics such as Cypher, Neo4j administration, and graph algorithms. These resources will help individuals demonstrate their expertise in Neo4j Graph Data Science and become certified.

Graph databases are being used by companies to solve tough problems, and one of the key use cases is master data management. Master data is highly connected and shared, and poorly built systems can cost business agility. Graph technology allows for modeling, storing, and querying hierarchies, metadata, and connections in master data, making it easier to manage and providing a consistent vision of enterprise data. The use of graph databases has been demonstrated in the case study of Airbnb, where they used Neo4j to tie together their entire data ecosystem, make it searchable, relevant, and trustworthy, and empower employees to find resources with confidence. By using graph technology for master data management, companies can gain real-time insights and make better business decisions.

Graph databases are being used to solve tough problems in master data management, a critical component of business operations. Master data is often fragmented and requires adaptable foundational technology to function effectively. Graph technology allows companies to connect and evolve with the needs of their business, as seen in the case study of Airbnb. By using graph databases, businesses can model and store hierarchies, metadata, and connections in their master data, making it easier to query and providing a consistent view of enterprise data. This approach enables real-time access to relevant information, supporting data-driven decision-making and improving business agility.