August 2015 Summaries

Felienne Hermans, an Assistant Professor at Delft University of Technology, emphasizes the importance of spreadsheets in decision-making processes within companies. She argues that people often overlook the power of spreadsheets due to their widespread use and lack of visibility. Hermans shares her research on spreadsheets as code, highlighting three key reasons why they should be considered programming languages: (1) spreadsheet formulas are used for similar problems as source code; (2) spreadsheets can be just as powerful as other programming languages; and (3) they suffer from typical software engineering problems such as feature envy smells. Hermans proposes applying software engineering methods to improve spreadsheets, including modeling spreadsheet data for a database, detecting code smells, and graphing spreadsheet data using Neo4j. She concludes that spreadsheets are not just data but actual pieces of programming that run the world, and her research aims to build an IDE for spreadsheets.

The Project Management Body of Knowledge (PMBOK) Standard, published by the Project Management Institute (PMI), is a well-known compilation of project management best practices. The standard has evolved over time and now includes interdependencies between processes and elements, which can be visualized as a graph database. This allows for more accurate and structured definitions of inputs and outputs, and enables practitioners to track the flow of concepts throughout the project lifecycle. By organizing the PMBOK Standard in this way, it becomes easier to understand how project management concepts relate to each other, and to identify potential areas for improvement. The author has successfully loaded the PMBOK data into Neo4j and demonstrated its potential for query and analysis, including identifying the quantity of inputs for time management processes and tracing the flow of elements through different processes. Future work includes exploring these opportunities further with developers and experts, and making the tools more accessible to a wider audience.

Spring Data Neo4j 4.0.0.RC2 has been released, featuring improved integration with Spring Data REST and ConversionService, performance enhancements for Cypher queries, and support for self-relations. The latest version includes fixes for persistence issues and enables execution of Cypher statements that modify the graph while returning results. A comprehensive reference documentation and example projects are available to help users get started with Spring Data Neo4j 4.0.0.RC2. The release is a result of contributions from users who reported issues via various channels, and the community's feedback is still welcome to ensure a great final release.

It's true that relational databases have their perfect use cases, but for highly interconnected data or applications with regularly changing database schemas, graph databases offer a more suitable alternative due to query performance and schema evolution benefits. Query performance in relational databases is impacted by data growth and the number of JOINs, whereas graph databases are scalable and show small increases in query times as data grows, making them ideal for large-scale applications. Additionally, graph databases are schema-optional, allowing for rapid addition of new data elements and relationships without significant development or operational overhead, making them a more future-proof solution for rapidly evolving schemas.

If you're interested in learning more about using Neo4j with Ruby, there's a new series of screencasts available that cover topics such as association chaining, ActiveRel, and deeper querying. These screencasts provide an even higher level of abstraction than Cypher, making it easier to traverse nodes and relationships with simple Ruby method call chains. The `ActiveNode` module is also introduced in the previous episodes, but these new ones dive deeper into more advanced topics like using relationships as another entity in your data model. Additionally, a webinar on metadata and asset control using Ruby on Rails and Neo4j is scheduled for September 10th, presented by Brian Underwood.

Several speakers at GraphConnect San Francisco on October 21st will describe how they use graph database technology to make the world a better place, citing examples such as transparency in the global food supply chain and catching international fraudsters. This trend has also appeared at non-Neo4j events, with notable mentions from Chief Data Officer Abhi Nemani of Los Angeles. The Neo Technology mission is to help the world make sense of data, aligning with the desire to make a positive difference on the planet. With multiple talks scheduled for GraphConnect San Francisco, attendees can expect to learn about how organizations use graph databases for greater global good, and Neo4j will share several big announcements from the stage during the conference.

Rapid change is a constant in today's business environment, making traditional tools for information and master data management (MDM) limitations exposed. Graph databases offer a solution to these challenges by providing a unique way to improve MDM practices. Master data management is crucial for any successful business, but current technologies often fall short when attempting to address its challenges. Relational databases struggle with highly connected data, requiring join tables that have significant limitations in complexity and query performance. In contrast, graph databases can model complex relationships between data entities, making them essential for MDM success. A graph database like Spectrum, developed by Pitney Bowes in partnership with Neo4j, provides intuitive modeling tools, dynamic visualization capabilities, and single-click functions to quickly develop web services, enabling agile MDM projects that deliver value quickly and engage business users throughout the process.

I've been an intern at Neo Technology, a small tech startup that makes graph databases, and my experience has been incredibly valuable. I initially sought out a prestigious internship to validate my successes as a student and launch a great career, but instead chose to join their demand generation team, which allowed me to gain hands-on experience within a growing company. Joining Neo Technology provided opportunities for growth, mentorship, and a unique work environment that values relationships and empowers employees to contribute value. This experience has been 100% worth it, and I would highly recommend it to others looking for valuable experience in tech.

The text appears to be a collection of articles and presentations related to Neo4j, a graph database. The topics covered range from introductory guides to more advanced subjects such as connecting popular frameworks like Meteor to Neo4j databases. Some articles also focus on specific use cases, like evaluating the success of Neo4j in real-world applications or visualizing complex networks using Cypher queries. Overall, the content seems to cater to developers and users interested in learning about Neo4j's capabilities and best practices for its implementation.

In relational databases, complex data models require multiple tables and intermediate JOINs, whereas graph databases can be easily visualized on a whiteboard, making it intuitive for both developers and business domain experts. Graph databases enable answers to questions about data connections, such as the strength of relationships and characteristics of those connections, which is crucial for building recommendation engines. Unlike SQL queries, graph database queries are straightforward to write and understand, with their own syntax like Cypher, allowing users to traverse data relationships efficiently. The equivalent Cypher query for a recommendation engine can encompass six JOINs across tables, reducing performance issues associated with relational databases, making graph databases the best choice for building scalable recommendation engines.

The Neo4j team has made significant progress in integrating the Spring Framework with their graph database, Neo4j. The latest releases of Spring Data Neo4j 3.3.2.RELEASE and 3.4.0.RC1 provide improved compatibility with various Neo4j versions, including 2.2.3 in the embedded integration. The main focus is now on developing Spring Data Neo4j 4, which will support Neo4j Server using remote Cypher APIs and integrate with the Neo4j-OGM library. Compatibility matrices are provided to help users understand the supported version ranges for different Spring Data Neo4j versions and Neo4j versions. New developer pages have been created on the Neo4j.com developer site to assist users in getting started with Spring and Neo4j, including example projects and tutorials. The team thanks everyone who contributed to and used the projects over the years, particularly Oliver Gierke's Spring Data Team at Pivotal and GraphAware for developing Spring Data Neo4j 4.

Graph data, also known as graph databases, have revolutionized the way we deal with complex data systems. The history of graph databases dates back to the 1970s when E.F. Codd developed the concept of relational databases, which led to the emergence of tabular databases that were later found to be limited in their ability to handle complex data models. In the 1990s, new data technologies like NoSQL emerged, but they had limitations such as denormalization and lack of expressiveness. Graph databases, on the other hand, offer a more expressive data model that allows for the storage and retrieval of graph-based relationships between entities. Today, graph database technology meets or exceeds ambitions, leading to a new era of innovation in data systems. The future of graph data holds promise with advancements in areas like eventual consistency, reliability, and scalability, as well as the development of new technologies such as peer-to-peer clusters and domain-specific partitioning. As ambition grows, so does the potential for innovation in graph databases, making them an exciting area to explore.

The Neo4j community is moving its support forum to a new location due to outgrowing the current Slack platform. The forum can now be found at community.neo4j.com, providing a better experience for users. A public Neo4j-users Slack group has been created as an alternative to IRC for quick questions and feedback, with over 600 members in its first six hours of operation. The group is intended to be a hub for discussion, help, and sharing of knowledge among the community, with many contributors and employees from Neo Technology participating to assist users with specific questions or ideas. Various channels have been created to cater to different interests, allowing users to join only those that are relevant to them. A slash command `/graph cypher [query]` has also been introduced to provide a convenient way for users to ask graph-related questions and receive answers in real-time.

Neo4j is a graph database that treats data relationships as first-class entities, providing connectedness to the data, which was not achievable with traditional relational databases. The author, who initially used RDBMS for their project, discovered Neo4j's capabilities and realized that data connectedness is key to unlocking real value in applications. This perspective shift has helped the author reconnect with building something tangible and worthwhile, leading them to create an online course, "NoSQL: Neo4j and Cypher", to share this knowledge with others. The course covers both beginner and intermediate levels, focusing on practical skills for working with connected data using Neo4j's Cypher language.

The proliferation of database technologies highlights the limitations of relational databases in handling complex, connected data. Relational databases excel with tabular data and consistent schema but struggle to store or express relationships between individual data elements. Graph databases offer a more flexible alternative by modeling data with high numbers of relationships, expanding models easily, and querying data relationships in real-time. This approach can lead to improved developer productivity, reduced project costs, and faster time-to-market, particularly for applications with rapidly evolving requirements or connected data. By structuring data around relationships rather than schema, graph databases can achieve better real-time query performance and make it easier to add new data or relationships without significant impact on the application.