June 2021 Summaries

Neo4j Bloom is a tool that enables users to navigate and visualize Neo4j graphs without needing to know Cypher query language. This allows for a more natural, visual exploration of the graph's connections, similar to taking an unknown path through a forest. While BI tools can provide subtotals, bar charts, etc., they were not designed for this functionality. To address this, Bloom enables custom Cypher queries executed in search phrases, which use a combination of text, parameters, and Cypher to guide users in building customized queries. These search phrases can be used to dynamically search the graph based on and/or logic, such as finding all :Person labeled nodes where the property born is between 1965 and 1992. The search phrase functionality allows for flexible searching with exact matching values, but can be problematic for other types of operators. A generic search phrase can be used to guide a user through this functionality, using parameters such as $label, $property, $condition, and $value. The search phrase also presents the user with conditional tests based on the data type of the property being tested. Additionally, Bloom allows users to find multi-label nodes by excluding invalid label combinations from the options presented to the user via a Cypher WHERE NOT clause. Finally, the blog discusses tips and techniques for working with Bloom, including using metadata commands, avoiding unwanted categories, and removing query parameters while editing search phrase definitions.

Knowledge graphs are driving industry disruption and business transformation, particularly during the COVID-19 pandemic, which has caused fundamental changes in consumer behavior, supply chains, and routes to markets. A combination of knowledge graphs and graph-based analytics can help companies bring complex products to market on schedule, proactively take action to remediate potential issues, and mitigate risks through greater end-to-end visibility. Knowledge graphs relate structured and unstructured data to identify information and reveal important but hidden facts, enabling the creation of semantic AI applications that thrive on contextual connections. The Connections virtual event series is featuring all things knowledge graphs, including webinars and thought leadership sessions from industry experts and organizations such as NASA, BMO Financial Group, and GraphAware.

The neo-forgery tool is a powerful stub that mocks a real Neo4j-driver session, allowing developers to create code and test it without making additional calls to their database. By utilizing this tool, Neo4j users can now enjoy the benefits of Test-Driven Development (TDD) and complete test coverage in their node applications. The tool provides an efficient way to stub out server calls, including queries that use a session object generated by the driver. It works well and allows developers to quickly create tests without worrying about the underlying database interactions. With neo-forgery, developers can focus on writing unit tests and ensuring their code is reliable and stable.

The Neo4j database's reactive drivers were introduced in version 4.0, enabling a gate between the client and server to control the flow of data and adjust it accordingly. This allows for efficient fetching of large amounts of data without overloading the client or server. The reactive drivers use operators such as bufferCount() and takeUntil(), which enable the client to define the size of results they can accept each time, and force the notifier to emit a value when the desired number of unique nodes is reached. This technique can be used for any data processing that requires size limitations on the client side.

Neo4j is still abuzz about the recent presentations at NODES 2021, including announcements of Neo4j 4.3, AuraDB Free Tier, and a massive trillion+ relationship graph. This week, Kacha Mukabe, the winner of the Leonhard Euler Idea Contest, is featured for creating an impressive submission using GraphQL despite having no prior Neo4j experience. Neo4j 4.3 has been released with new features such as improved devops support, security improvements, and performance enhancements like smart IO scheduling and finer-grained locking. A Graph App called SemSpect has been released by Thorsten Liebig from Derivo GmbH to explore graphs in a data-driven approach. Liz Michel presented on reconciling taxonomies using ontologies at NODES 2021 and wrote blogs on the topic, while Andreas Schreiber released prov2neo, a Python library for importing PROV documents into Neo4j. The Leonhard Euler Idea Contest winners have been announced, with Developers.Zed, Help Me, and SeedFundMe taking top prizes. Rik Van Bruggen showcased the benefits of relationship indexes in his blog, while Andreas Schreiber's prov2neo enables importing W3C PROV formats into Neo4j. The NODES 2021 recordings are now available, including pre-conference hands-on training sessions and conference sessions.

Allianz Benelux, the largest insurer in the world, is leveraging Neo4j to create customer profiles and detect fraud in their insurance industry. The company uses graph technology to understand how individual pieces of information are linked to each other, allowing for a 360-degree view of customers and improved detection of fraudulent claims. Before adopting Neo4j, they relied on traditional relational databases, which were difficult to manage and analyze. With Neo4j, they can now generate networks quickly and efficiently, reducing manual steps and saving time. The team is exploring the potential of graph data science libraries, including network analytics, to further optimize their use of Neo4j. They believe that graph technology has a bright future in the market, offering a key differentiator for companies looking to innovate and improve customer insights.

Creating indexes on relationship properties or types can significantly improve query performance in Neo4j by reducing scanning across the entire database. These indexes enable quick lookup of relationships and their properties, enabling more complex queries to be executed in less time. Indexes are created using the `CREATE INDEX` command and can be used with compound indices for multiple properties. To troubleshoot the effectiveness of an index, EXPLAIN and PROFILE commands can be used, which provide execution plans and runtime performance metrics. By creating indexes on relationship properties or types, users can reduce database storage demands and improve query performance.

Thank you for your inspiring submissions, congratulations to the winners of the Neo4j Leonhard Euler Idea Contest, which has drawn to a close and the winners were announced at NODES 2021 by Emil Eifrem, CEO and Co-Founder of Neo4j. A record number of innovative ideas were submitted from around the world, with over 300 developers, data scientists, and professionals from 58 countries participating. The primary goal of the contest was to excite and inspire developers to see the innovations, applicability, and feasibility possible – with graph database and GraphQL Library across different use cases – and present their work to a thriving global community of graph enthusiasts and developers. Eight winning teams received a total of $10,000 in prizes, including three prize winners and five most creative ideas selected by a stellar panel of judges from across the Neo4j Graph Database industry.

We at DTCP believe in the ongoing evolution of technology and software, enabling the development of more services with usability, personalization, and automation. The database market is witnessing technological evolution, with relational and NoSQL databases failing to process complex real-world datasets that can be handled by native graph databases like Neo4j. Graph databases are becoming mainstream, with Neo4j being a category leader, expected to grow to over $10 billion by 2027. Neo4j offers a new era of personalization and real-time services, handling connected data and enabling Fortune 500 companies to blend knowledge stored across data silos.

The latest version of NeoDash, a graph app for building Neo4j dashboards, has been released with several new features. These include interactive multi-page dashboards, map visualizations, and the ability to embed web apps directly into reports using iFrames. The new version also includes improved stability, better markdown support, and extended Cypher parameter capabilities. A demo dashboard is provided to showcase these features, including a brewery tour planner that uses selection boxes, maps, and embedded Neo4j Bloom visualizations. Users can try out NeoDash from the Graph App Gallery or by running an online demo.

The Neo4j Community has been active and productive, with many users contributing to the platform. The community featured Andrew Bowman as their most valued member, who helps resolve complex questions and presents at conferences. Atul Shukla created a movie recommendation system using Neo4j, while Rik Van Bruggen analyzed Shakespeare's plays using graph algorithms. Thomas Larsen released Alteryx Connectors for Neo4j, allowing users to import Cypher queries into workflows. Neo4j has announced the Early Access of Neo4j AuraDB Free, a free cloud instance that does not expire like a Neo4j Sandbox. The community is also exploring Graph Embeddings using GraphSAGE and analyzing data with Bloom. The company hosted its third annual online Neo4j developer conference, which included pre-conference training sessions and recorded sessions will be posted soon.

At Neo4j, the company aims to help users make sense of their data by unlocking connections and relationships using graph technology. Since the launch of its managed graph database service, AuraDB, customers have used it to build innovative applications across various use cases such as fraud detection, knowledge graphs, customer 360, recommendations, and more. The new Early Access for Neo4j AuraDB Free offers a completely free tier allowing users to learn, test, prototype, and explore their ideas without barriers or costs. This free tier provides access to graph technology for everyone, regardless of means or stage of development, enabling users to focus on building things and making sense of data. The service includes features such as easy integration with other native cloud services, a generous size limit for learning and prototyping, and continuous improvement through user feedback.

Neo4j Graph Database 4.3 has been released, marking a significant update to the leading graph database for deployment in any production environment. This latest version delivers enhancements to performance, scalability, security, operability, cloud scale, and developer experience, positioning it as a high-performance, distributed, multi-database, multi-use system that meets the demands of enterprises on security and scalability while delighting developers with a reactive API and close integration with developer ecosystems. Key features include improved performance through Smart IO scheduling, faster startup times, and reduced Total Cost of Ownership (TCO), relationship and relationship property indexes for fast query performance, graph-native scale with ultra-high parallelized throughput, increased operational flexibility with parallelized backup and restore, cloud scalability with Kubernetes integration and Helm Charts, and enhanced security features such as extended role-based access controls, security logs, and sensitive data obfuscation. Additionally, Neo4j 4.3 empowers developers with a better developer experience, including robust data pipelines, streaming data, machine learning, and improved query performance for complex queries. With its release, Neo4j is taking the emergence of the graph database one-notch further into the mainstream of enterprise technology.

We successfully created the world's biggest graph database, with a trillion relationships, using Neo4j Fabric. This was achieved by generating data from a known model, specifically the LDBC social network dataset, and distributing it across 1129 machines. The system was scaled to handle 280 TB of data and showed impressive performance, with latency measurements taken during the process. Despite facing some challenges, such as DNS query limiting, the team overcame these obstacles and achieved their goal in a relatively short period of time, demonstrating Neo4j's ability to scale beyond traditional limits.

Neo4j, a leader in graph database technology, recently made history by securing the largest investment round in database history, a $325 million Series F led by Eurazeo and GV, signaling a pivotal moment in the database industry. This investment highlights the growing importance of graph databases, which have evolved from niche tools to essential components in enterprise data management, thanks to their ability to handle complex data relationships. The database market, traditionally dominated by relational databases, is undergoing a significant transformation driven by advancements like cloud computing, big data, and AI, leading to the rise of non-relational databases. Neo4j's success is marked by widespread adoption across various industries, including major banks, retailers, and telecommunications companies, as well as by data scientists who leverage graph databases for machine learning. The company's recent demonstration of handling a trillion-relationship graph at scale underscores its potential to support large-scale applications, reinforcing its position as a key player in the evolving database landscape.

This week at Neo4j, the company is hosting its annual online developer expo and summit, NODES 2021. The event features a keynote by CEO Emil Eifrem on June 17th, as well as multiple presentations by community members and experts in the field of graph databases. A variety of tools and frameworks for working with Neo4j are being showcased, including deps.dev, neo-forgery, Motherlode, tei2neo, RBAC with Azure Active Directory, and Prometheus for monitoring Neo4j. Meanwhile, a podcast series called GraphStuff.FM is also exploring topics related to graphs and graph databases, featuring interviews with speakers from the upcoming NODES 2021 event.

Welcome to GraphCast – our series featuring what you might have missed in Neo4j media from the past fortnight. Neo4j has released a video titled "Why Neo4j?" which provides an overview of Johannes' work with AuraDB Self-Serve and insight into the team structure at Neo4j. The video serves as a kind of informational interview for potential cloud engineers looking to join the company.

This individual has a personal story of coming out as gay, which was a transformative experience for them. They initially hid their true self due to fear of judgment and rejection in the workplace, particularly in sales roles where being openly LGBTQ+ might be seen as a liability. However, after joining a company like Neo4j that values diversity and openness, they were able to be themselves without fear of retribution or judgment. As a result, they feel empowered to share their experiences and support others who may be struggling with similar issues. They now proudly celebrate Pride and strive to create an inclusive environment in the workplace, believing that unity and diversity are key to strength and power.

The author of the text has been with Neo4j for six months, a graph database company, and reflects on what they have learned. They highlight that positioning a graph platform effectively requires understanding whether it is a good fit for a customer's specific business challenges, and being able to articulate this in a way that resonates with prospects. The author notes that graphs are not just about the technology itself, but also about solving real-world problems related to relationships and connections in data. They share examples of use cases where graph can be applied, including financial services, government, manufacturing, transportation, and others. Additionally, they emphasize the importance of understanding how to model and pipeline data into a graph database like Neo4j, which is not necessarily rocket science but requires careful consideration. The author also looks forward to building on a strong foundation with their team in Southeast Asia, forming a community of practitioners, getting certified as a Neo4j Certified Professional, and navigating the growing competition in the market.

The Neo4j community is actively sharing knowledge and resources, including a free hands-on training session from June 7-11 for those who register for the NODES conference. Featured community member Alberto De Lazzari has contributed to Neo4j projects and will present on Graph AI to Combat Fraud in Fintech and Insurtech at NODES 2021. Clair Sullivan published a tutorial on getting started with graph embeddings using Neo4j's Graph Data Science Library, while Alex Wolford explained how Confluent Cloud can be used with Neo4j AuraDB. Liquibase has enhanced its products to work with Neo4j databases for DevOps management. The Data Stack Show podcast featured an hour-long discussion about Neo4j and data governance, and a video from NODES 2020 showcases scalable reactive product development frameworks for the automotive industry. A free hands-on training session is also available before the conference, led by Developer Relations team members.

Neo4j is a graph technology that helps Nur Aini Rakhmawati, a Ph.D. researcher and Graph Community MVP Asia, in her projects with students, particularly in analyzing data related to the halal portal and legislative candidates in Indonesia. She discovered Neo4j two years ago and was drawn to its algorithms, visualization capabilities, and ease of use. Nur is involved with the Neo4j community through teaching courses on graph databases and has participated in YouTube and social media platforms to promote her work. Her favorite part of working with Neo4j is its ability to support various algorithms, including graph embedding, and its user-friendly interface. The future holds great potential for graph technology, with applications in search engines, big data processing, and artificial intelligence, which are already being explored by the Neo4j community.

NODES 2021 is a live, fully immersive experience that will be different from traditional virtual conferences. The event aims to combat "zoom fatigue" and provide an engaging, interactive experience for attendees. Over 50 talks across five tracks will be delivered live, with scheduled breaks and an interactive Q&A panel. Pre-NODES will also take place before the main event, allowing people to join in remotely. Attendees are encouraged to register early, plan their day, join the Discord server, and prepare their environment for a comfortable viewing experience. The event is expected to be unique and enjoyable, with something special planned for the main break.

We've released new features in Neo4j, including the ability to train and store up to 3 ML models in the community edition, making it more useful for users just getting started. This release also introduced a ScaleProperties procedure to transform and scale node properties, Graph Filtering/Subgraph Projections to create subgraphs from an in-memory graph by filtering on node or relationship properties, improved Graph Embeddings with Node2Vec and graphSAGE, better Supervised ML Pipelines with support for saving, publishing, and restoring trained models, and Administrative Capabilities for enterprise users to view, use, and delete graphs and models. Additionally, we've added two new centrality algorithms for Influence Maximization contributed by a community member, @xkitsios.

IP asset management can be significantly enhanced by utilizing advanced database systems like Neo4j, which automate the evaluation and connection of relevant data in real-time, eliminating the need for manual analysis by IP professionals. Neo4j's graph data science algorithms facilitate three key types of information queries: examining single items, understanding the connections between multiple items, and identifying causes and effects in IP asset setups. These capabilities improve IP filing strategies by considering global market coverages and evaluating patent claims, enhance licensing with better distinction of terms and competitive fees, and aid in identifying patterns with high infringement potential for litigation purposes. The visualization of IP assets using Neo4j's tools, such as Neo4j Bloom, allows for intuitive understanding and can complement existing IP Asset Management systems by providing comprehensive insights into the relationships and dependencies within IP portfolios.