May 2018 Summaries

The new release of the Neo4j ETL tool brings several enhancements and improvements, including backend optimizations, UI changes, and bug fixes. The tool is now fully integrated with Neo4j Desktop from version 1.1.3, and it supports multiple schema parsing, additional driver support, and improved data import features. The tool also includes new features such as multi-schema support, fetch size, and CSV file writing options. Additionally, the UI has been updated to reflect these changes, and the documentation has been revised to include setup instructions for Docker containers with MS-SQL sample datasets.

This week's Neo4j round-up covers various topics including West Africa Leaks, an online meetup about GQL, and a Deep Dive into the Geospatial Data Type. Featured community member Jérôme Baton was interviewed on the Graphistania podcast, discussing his work on Learning Neo4j 3.x and other projects. The week also saw new features from Neo4j Bloom, graph grouping using APOC, and explorations of how graphs can be used to analyze machine learning models. Additionally, there were blog posts on building a smart GIS system with Neo4j, analyzing CoinMarketCap data, and integrating Neo4j via Python in the R environment.

Neo4j 3.4 offers significant improvements across various aspects, including scalability and performance enhancements, new features for the Cypher query language such as spatial querying support and date/time types, Multi-Clustering support for horizontally partitioning graphs by domain, improved read performance with a new runtime that improves performance by roughly 70%, faster write performance due to native indexes optimized for graphs, rolling upgrades for operations and administration, enhanced spatial and date/time data capabilities including geospatial graph queries, and a new security feature focusing on property-level security.

Neo4j has released version 3.4, which includes geospatial and temporal data types, allowing developers to easily update their applications that require these features. Neo4j is now also available on Google Cloud Launcher, enabling users to leverage the power of graph databases in the cloud. Additionally, a new query language called GQL (Graph Query Language) has been proposed, which aims to unify the best features of existing graph query languages like Cypher and PGQL. The Neo4j community has welcomed Nicolle Cysneiros as its featured member, who has made significant contributions to the community through her talks on graph databases and Python. Various other updates and resources have been shared by the community, including tutorials, blog posts, and online meetups, showcasing the versatility and potential of Neo4j in various applications.

The Neo4j APOC team has released two versions of APOC, 3.3.0.3 and 3.4.0.1, which can be downloaded from GitHub or Neo4j Desktop with a single click. The new release includes significant updates to the internal kernel API, allowing for more efficient interaction with the Cypher runtime. Two notable features introduced in this release are `apoc.load.xls` for loading data from Excel files and graph grouping, which allows summarizing graphs by grouping nodes by one or more properties. Additionally, there are bugfixes, improvements, and documentation updates across various procedures and functions. The team thanks contributors and reporters who helped shape the release.

The Neo4j graph database has released version 3.4, which advances its mission of helping the world make sense of data. The native graph database is the foundation for the rest of the Neo4j Graph Platform and features include multi-clustering, new data types such as date/time and 3D geospatial data, performance improvements, native string indexes for faster writes, fast backups, enterprise Cypher runtime up to 70% faster, and rolling upgrades. These updates enable optimized Cypher queries for searches across time or space, expand the scale, uses, and performance of the graph platform, and provide improved security features such as user- and role-based security, LDAP and AD directory integration, Kerberos authentication, HTTPS access to all user-facing interfaces, and encrypted data at rest. The release is aimed at both longstanding community developers and enterprise DBAs and is available for download.

The graph community has expressed a desire for a common query language that would benefit developers with transferable expertise, provide portable queries, and reduce vendor lock-in. A unified graph query language, GQL (Graph Query Language), is proposed to fuse the best of three property graph languages: Cypher, PGQL, and G-CORE. The new language aims to be an industry standard, working with SQL but not being confined by it, offering better choices for developers, data engineers, and data scientists alike. The Neo4j team is advocating for collaboration among vendors and users to define and standardize one language, which has a common heritage of ASCII art patterns to match, merge, and create graph models. The community is invited to vote on whether to unite to create a standard Graph Query Language (GQL).

The Neo4j community has been actively engaged in exploring the capabilities of various products and tools, including Neo4j Bloom, a graph communication and data visualization product. The community has also seen releases such as Popoto 2.0, a visual query builder for Neo4j, and new tutorials and blog posts on topics like building personalized recommenders and parsing Neo4j's query log. Thomas Frisendal, a prominent member of the Neo4j community, was featured this week, sharing his expertise on data modeling with graphs and knowledge graphs. The community has also been actively engaged in graph visualization, with various projects and blog posts showcasing different approaches to visualizing data using tools like vis.js and SigmaJS.

Neo4j has announced the release of Neo4j 3.4 with native support for Spatial and DateTime data types, as well as a new product called Bloom which aims to make graph communication and data visualization easier for non-developers. The company has also released GraphAware NLP Libraries with improved performance and new features. Additionally, there are various tutorials and examples on how to use Neo4j with Azure Functions, plot Wifi traffic, and perform graph analytics and data visualization in mixed reality.

Neo4j Bloom is a breakthrough graph communication and data visualization product that enables non-technical users to share their work with peers, managers, and executives. It features a codeless search-to-storyboard design, GPU-accelerated rendering, and the ability to visualize related node clusters. Neo4j Bloom aims to accelerate "graph epiphanies" by revealing connections between people, data, devices, systems, and activities throughout an enterprise. The product is fully connected to the Neo4j Graph Platform, allows for navigation and editing of graph datasets, and offers various features such as panning, zooming, snapshotting, and querying. To run Neo4j Bloom, users need access to a running instance of Neo4j Enterprise Edition and a licensed Neo4j Desktop instance.