May 2021 Summaries

The Neo4j community has published several tutorials, guides, and articles on various topics such as setting up the environment for analyzing Twitch data, using Quarkus, Helidon, or Micronaut with Java drivers, encrypting intra-cluster communications, importing graph data into Power BI, and applying machine learning to analyze carbohydrate genomes. These resources are designed to help developers get started with Neo4j, improve their skills, and explore its capabilities. The community is also promoting hands-on training sessions before the NODES 2021 conference, where attendees can learn from experienced developers and get practical experience with Neo4j. Additionally, a featured community member, Frederik Byl, has shared his expertise on test automation and will be presenting at the conference, while Vladimir Bacvanski and Deepak Chandramoul's recording from last year's NODES 2020 is available to watch again.

The Neo4j digital events team has launched a new monthly blog to highlight past digital events, with the first issue featuring "Fan Favorites" - the top two attended and/or watched webinars of 2021. The inaugural post highlights Graph Databases For Dummies and Graph Data Science For Dummies, which were broadcast on February 2 and 21, 2021 respectively. These webinars discuss the advantages of graph database technology, the foundations of graph data science, and provide tips for using graph databases. They are structured as discussions between the authors and moderators, aiming to show how easy it is to get started with graph databases and explore the fundamentals of graph data science. The blog aims to engage attendees by sharing relevant content throughout the year and encourages readers to reach out to [email protected] with any questions.

I built a knowledge graph from scratch without any source code, leveraging my interest in oncology and the desire to learn about cancer research. I initially thought of using a relational database but realized that it wouldn't be suitable for capturing knowledge, which requires labeling, giving context, and connecting dots - exactly what a graph represents. I represented my initial idea in a competitive intelligence graph, showing how companies design therapeutic molecules interacting with molecular targets. The graph model is basic but not the end of it. I wanted to get answers to simple and complex questions, such as looking up a molecule by name or identifying interactions between molecules and their target families. To capture knowledge, I created input screens for entering company information and potential link-outs to other data sources. I defined relationships with properties like "developed by company" or "has location in city." The graph grew over time, adding bioprocess, biological structure, and financial data. I used ClinicalTrials.gov to extract data on companies developing drugs and semi-automate the process of loading it into my knowledge graph. The technology stack used was FileMaker Pro Advanced, KNIME for ETL, and Neo4j Desktop for storing the knowledge graph. Cypher allows me to answer questions through APOC procedures and visualize data in Tableau or Neo4j Bloom. I demonstrate navigating my knowledge graph with an example of a molecular target that has been resistant to being targeted by a drug. Building a knowledge graph opens doors to capturing complex data landscapes, gaining additional insights, and exploring new opportunities.

To run Django-Neomodel within a Docker container, one needs an updated version of Docker and Django Neomodel installed. The process involves creating a Dockerfile that collects the commands to create the environment, a docker-compose.yml file that configures the ports for communication with the Neo4j database, and a docker-entrypoint.sh script that configures the python/Django portion of the app. Once these are in place, running `docker-compose up` starts the container and allows access to the Django admin and Neo4j browser at specific URLs. The environment variables and commands in the Dockerfile and docker-entrypoint.sh script configure the installation of dependencies and creation of superusers for the Django application. This setup is useful for empowering app development for developers, especially those working with Neo4j and Django.

If you missed our APAC Quarterly Customer Update webinar, you can still watch the recording of the conversation between Emil Eifrem, Neo4j's Co-Founder and CEO, and Nik Vora, Vice President of Neo4j APAC. During the interview, Emil shared his path as a pioneer in graph databases and the origins of Neo4j, while also discussing the rapid expansion of graph data platforms in the Asia Pacific region. The event aimed to provide insight into Neo4j's work in this area and offer customers an update on the company's growth.

The APOC (Neo4j Extension Pack) Hidden Gems presentation showcases several lesser-known features and improvements in Neo4j's APOC extension. These include the ability to transform MongoDB collections into graphs, efficient Neo4j data import using Cypher scripts, and automated file ingestion with apoc.load.directory and apoc.load.directory.async procedures. Additionally, apoc.periodic.truncate allows for easy database cleanup during prototyping, while apoc.util.decompress enables compression and decompression of large string values between Node and Relationship properties. Minor updates include support for the latest Couchbase driver version, statistics for apod.periodic.iterate, and UUID and TTL features that work per database. Future plans include adding support for Apache Arrow file format and Redis data storage and retrieval.

Neo4j is continually improving its user experience, and users are encouraged to provide feedback. The company has shared various resources, including a blog post on setting up a Docker environment for Neo4j with Jupyter Lab, an example of moving data from Kafka to Neo4j, and a presentation on Lightning Fast Zero to Production with Spring, Neo4j, and jHipster. A featured community member, Camilla Dal Rio, will be presenting at NODES 2021 on using machine learning and MLOps with graph databases. The company is also seeking user input to improve its UX for developers and data scientists, offering compensation for participants. Additionally, users can watch a recording of the NODES 2020 presentation on Lightning Fast Zero to Production with Spring, Neo4j, and jHipster.

Reshama Shaikh, a statistician and data scientist based in NYC, has been involved with the Neo4j community for three years. She got introduced to graph databases through an event organized by Neo4j's developer advocate group and was impressed by their power in capturing complex relationships between data points. After attending GraphConnect 2018, she wrote an article on the benefits of graph databases and won a gold medal blog award. Shaikh has used Neo4j hands-on and learned about its importance in defining data relationships. She currently organizes two meetup groups, Data Umbrella for underrepresented persons in data science and PyLadies for advancing women in Python. Shaikh sees graph technology as an increasingly important tool in the future of data science, with Neo4j working on a new user interface to make it more accessible to beginners.

At financial firms large and small, graph technology plays a key role in cybersecurity, product development, risk management, and product recommendations. The financial services industry utilizes graph technology for various purposes including its importance in the industry.

Tomaz Bratanic has published a tutorial on visualizing graph embedding algorithm results in Neo4j's Graph Data Science Playground application, NEuler. Tomaz teaches you how to execute and visualize graph embedding results with the t-SNE scatter plot. In addition, Ümit Kaan Usta has developed a Python application called Reddit Detective that can be used to discover potentially problematic data on Reddit. Dirk Mahler has created the jQAssistant application where Neo4j helps identify problems in software architecture. Paul Parkinson from Oracle published a blog showing how to use Neo4j as a data source in a Helidon Microservice. Rebecca Rabb posted a blog about the power of storing corporate data in a graph database. The Developer Relations team also included a link to an Advanced Query Tuning training session presented by Mark Quinsland last year, where he shared tips and tricks for Cypher query tuning. Chris Hay is this week's featured community member, who has developed and architected applications for IBM and created a 30-minute video on using Neo4j.

The Neo4j Online Developer Expo and Summit is an upcoming event that will bring together more than 40 graph experts from around the world to showcase innovative projects built with Neo4j. The conference aims to take participants' graph skills to the next level, offering a mix of technical talks and hands-on practice workshops. Attendees can reserve their spot now and also catch up on videos from previous events by subscribing to the Neo4j YouTube channel.

This individual was born and raised in Kona, Hawaii, where limited resources and career paths are common. Their parents made a significant decision to leave the island and pursue careers in the tech industry in Silicon Valley, which had a profound impact on their life. Adjusting to mainland life as a Polynesian kid presented challenges such as learning English and fitting into a predominantly white community. The individual struggled with depression and found it difficult to connect with peers, but eventually turned their insecurities into strengths by excelling in tech sales and becoming a top performer. They now work at a leading database company, leveraging their Asian Pacific Islander heritage to enhance their professional life and well-being. Through their experiences, they've learned the importance of growth, adaptation, and self-awareness.

The Neo4j Graph Data Platform is a comprehensive solution for managing and analyzing complex data relationships. It was initially created as an embedded Java database, but has since evolved into a more generalized system with rapid uptake in various industries such as personalized recommendations, logistics, and fraud detection. The platform consists of the Neo4j graph database, which uses a property graph data model, Cypher query language, and index-free adjacency to optimize performance. It also includes tools like Neo4j Desktop, Neo4j Browser, and Graph Native, which enable developers to manage and interact with the database. Additionally, the platform offers various connectors for integrating with other technologies such as Apache Spark, Kafka, and business intelligence tools, as well as experimental integrations through Neo4j Labs, which includes projects like APOC library, Neosemantics library, and GRANDstack. The platform is designed to address emerging trends in developer productivity and data science, providing a flexible and scalable solution for managing complex data relationships.

Graph analytics emerged as a key trend at Gartner’s virtual Data & Analytics Summit, with predictions that it will be one of the top four enabling technologies for data and analytics by 2021. Graphs are seen as filling in missing pieces in analytics efforts, providing context lacking in traditional big data analytics. They enable organizations to understand human and machine behavior, improve customer understanding, service, logistics, and risk mitigation. Graph technology is also critical for AI/ML success, making it more explainable and providing metadata about models. Additionally, graphs can make data science project management easier and more effective by accelerating data preparation, insightful data exploration, superior feature engineering, and improved communication between data scientists and subject matter experts. Finally, graphs will enable the next big thing in AI – composite AI – by providing a common ground for combining multiple AI techniques to achieve the best result. The summit highlighted the importance of graph technology for organizations worldwide, with Gartner announcing the event for March 14-17, 2022.

This blog concludes a five-part series highlighting the reasons Neo4j AuraDB Enterprise should be chosen as the graph database solution for enterprises building mission-critical applications in the cloud. The database is cost-effective, with simple pricing and predictable costs, allowing companies to budget accordingly without waste from excess capacity. Additionally, it grows with companies, ensuring their applications are future-proofed against unforeseen changes. With flat hourly rates, costs are transparent and aligned with usage, making procurement and management easy. The latest features are delivered to the cloud first, providing immediate access to new capabilities, while staying backward compatible. This ensures that companies can benefit from the very latest graph data platform features immediately and seamlessly.

Neo4j Streams has been released with several new features, including dynamic configuration that allows for on-the-fly changes to Kafka config without restarting the database. Dirk Fahland is working on a project to transform event logs into a knowledge graph using Neo4j Streams and has published templates for translating CSV log files into events. Rick Van Bruggen used Neo4j with APOC to aggregate data from news articles as a knowledge graph, while Anurag Tandon presented a demo of Neo4j Bloom at NODES 2020. Py2Neo has been released with several highlights, including full support for routing and stability improvements for multithreaded usage. Madison Gipson shares how to pass parameters into Cypher code using Py2Neo, and Matthias Sieber is the featured community member who has made significant contributions to Neo4j development and beyond.

In this interview, Terry Franklin of Tourism Media discusses their use of Neo4j for intelligent content curation, a project that involves building and scaling content at scale using large datasets. Before adopting Neo4j, the team used traditional relational databases but faced issues with data storage and querying complexity. Terry chose Neo4j due to its market leadership, technical content, and developer relations team, which enabled them to quickly start using the product and bring it into their business. With Neo4j, they can now ask more complex questions of their data and build better content, particularly in handling round-the-world travel routes. The team chose AuraDB for scalability, allowing them to scale up or down as needed. Terry enjoys collaborating with non-technical people using the graph data model's intuitive concepts, and looks forward to adding more layers of data to their knowledge graph and exploring new use cases for graph technology.

NEuler is a graph data science playground application designed to help users execute and understand graph algorithms in Neo4j, allowing them to quickly import example data, execute various graph algorithms, and visualize their results with tools like t-SNE scatter plots. Users can set up the Neo4j sandbox environment by following a link or selecting an existing one, then choose to run a single algorithm, such as node embedding algorithms like Node2vec or FastRP, which calculate fixed-length vector representations for each node in the graph while preserving properties like graph structure and information. By configuring parameters and running the algorithm, users can observe the results in table form, click on the Scatter Plot option to visualize the results, and export code needed to execute the algorithms outside the NEuler application. The Neo4j Sandbox projects offer a range of datasets for users to experiment with, including Twitter, Open Street Map, and contact tracing projects.

Neo4j's Graphs 4 COVID-19 initiative has received two honorable mentions in Fast Company's 2021 World Changing Ideas Awards, recognizing its impact on the global pandemic. The initiative, launched during the pandemic, brought together developers and data scientists worldwide to collaborate and develop projects related to disease research, vaccine development, contact tracing, and more. Neo4j's Graphs4Good Program provided access to their open-source tool and resources to support developers in making a positive impact. Various projects, such as Contact Tracing, Covid Graph, Project Domino, and others, were developed through this initiative, addressing critical issues like misinformation and public health. The recognition from Fast Company highlights the success of Neo4j's efforts in uniting people to fight against COVID-19 and promoting data-driven solutions for the betterment of society.

Gartner’s Data & Analytics Summit is a significant event for IT leaders to learn about major trends in data and analytics. The summit highlights graph technology as a critical area of focus, with numerous sessions dedicated to the topic. As the CMO of a graph database company, Neo4j, sponsoring the summit was an obvious choice due to the surge in inquiries about graphs from various industries. Graph techniques and thinking are becoming increasingly important for businesses to inform strategy, drive smart decisions, and execute with precision. The summit will cover topics such as accelerating operationalization of AI, forming a strong data fabric, driving smart decisions, using graph technology to improve explainability in AI and machine learning, and deepening the impact of data analytics as a core business function. Key sessions include those led by experts on graph 101, intelligence in context, building knowledge graphs, and the future of data science and machine learning, among others. The event aims to help IT leaders navigate the pace of change and formulate an analytics strategy that addresses today's challenges while preparing for future-proofing in an uncertain world.

Neo4j AuraDB Enterprise is a reliable graph database designed for mission-critical applications, ensuring companies can depend on its reliability without fear of service interruptions or maintenance windows. Its fault-tolerant design and 99.5% service availability guarantee make it a secure choice for enterprises. Additionally, the database's infrastructure automatically recovers from component failures, replicates data to multiple systems, and ensures data sovereignty. Neo4j AuraDB Enterprise also bolsters productivity by providing flexibility and ease of use for developers, allowing them to build apps faster with built-in drivers, tools, and integrations for popular languages and frameworks.

The Neo4j GraphQL library has been officially released with new features, documentation, and a migration guide. The GraphStuff.FM podcast discussed identifying good fits for graph databases, while a video from NODES 2020 presented CrossCode's successful migration to a graph solution. King's Digital Lab published an art history dataset in Neo4j and explored it with 3D visualization and VR. Esther detailed the new features in GraphAware's Hume 2.8, including Snapshots and Webhooks, while GraphXR visualized dream report data using NLP and deep learning. Meanwhile, Mika shared her experience developing a graph database for Dayvelop, and Tony announced the beta release of his book on Elixir and graph databases.