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In this talk we’ll explore powerful analytic techniques for graph data. Firstly we’ll discover some of the innate properties of (social) graphs from fields like anthropology and sociology. By understanding the forces and tensions within the graph structure and applying some graph theory, we’ll be able to predict how the graph will evolve over time. To test just how powerful and accurate graph theory is, we’ll also be able to (retrospectively) predict World War 1 based on a social graph and a few simple mechanical rules.Then we’ll see how graph matching can be used to extract online business intelligence (for powerful retail recommendations). In turn we’ll apply these powerful techniques to modelling domains in Neo4j (a graph database) and show how Neo4j can be used to drive business intelligence. Don’t worry, there won’t be much maths.

In this talk we’ll explore powerful analytic techniques for graph data. Firstly we’ll discover some of the innate properties of (social) graphs from fields like anthropology and sociology. By understanding the forces and tensions within the graph structure and applying some graph theory, we’ll be able to predict how the graph will evolve over time. To test just how powerful and accurate graph theory is, we’ll also be able to (retrospectively) predict World War 1 based on a social graph and a few simple mechanical rules.Then we’ll see how graph matching can be used to extract online business intelligence (for powerful retail recommendations). In turn we’ll apply these powerful techniques to modelling domains in Neo4j (a graph database) and show how Neo4j can be used to drive business intelligence. Don’t worry, there won’t be much maths.

In this talk we’ll explore powerful analytic techniques for graph data. Firstly we’ll discover some of the innate properties of (social) graphs from fields like anthropology and sociology. By understanding the forces and tensions within the graph structure and applying some graph theory, we’ll be able to predict how the graph will evolve over time. To test just how powerful and accurate graph theory is, we’ll also be able to (retrospectively) predict World War 1 based on a social graph and a few simple mechanical rules.Then we’ll see how graph matching can be used to extract online business intelligence (for powerful retail recommendations). In turn we’ll apply these powerful techniques to modelling domains in Neo4j (a graph database) and show how Neo4j can be used to drive business intelligence. Don’t worry, there won’t be much maths.

Running an open source B2B business is a tough gig. Not only can anyone see where the bodies are buried but there’s a constant tension between various stakeholders: OSS enthusiasts, customers, and employees with respect to the software. At Neo4j we see architecture as one possible organising principle around which all stakeholders can gather. In this talk I’ll describe Neo4j and its architecture and discuss how we communicate and evolve the product to bring along all stakeholders. I’ll also talk about some of the hardships that architecture alone cannot solve and look to the audience to help us out with some of our thornier corner-cases.

This workshop is aimed at the people new to Neo4j but curious about what graphs can do for them and their business. In this highly practical session we'll explore what a graph database is and how data modelling works for graphs. We will also touch on a little graph theory and DBMS architecture for depth.

The workshop is split into multiple sessions, each with substantial practical elements - so bring your laptop!

RESERVE YOUR SEAT NOW

In this talk we’ll explore the new Causal clustering architecture for Neo4j. We’ll see how Neo4j uses the Raft protocol for a robust underlay for intensive write operations, and how the asynchronous new scale-out mechanism provides enormous capacity for very demanding graph workloads.

We’ll discuss the cluster architecture’s new causal consistency model. Causal consistency is a big leap forward compared to the commonplace eventual consistency which makes it convenient to write applications that use the full capacity of the cluster. In particular we’ll show how despite the mixture of concensus protocols and asynchronous replication, that Neo4j allows users to read their own writes straightforwardly and discuss why this is such a difficult achievement in distributed systems.

For the application developer, we’ll show how Neo4j’s Causal Clustering optimised drivers makes it easy to write applications that scale smoothly from a single server to a large, distributed cluster: a practical motivation for the distributed systems enthusiast.

In this talk we’ll explore the new Causal clustering architecture for Neo4j. We’ll see how Neo4j uses the Raft protocol for a robust underlay for intensive write operations, and how the asynchronous new scale-out mechanism provides enormous capacity for very demanding graph workloads.

We’ll discuss the cluster architecture’s new causal consistency model. Causal consistency is a big leap forward compared to the commonplace eventual consistency which makes it convenient to write applications that use the full capacity of the cluster. In particular we’ll show how despite the mixture of concensus protocols and asynchronous replication, that Neo4j allows users to read their own writes straightforwardly and discuss why this is such a difficult achievement in distributed systems.

For the application developer, we’ll show how Neo4j’s Causal Clustering optimised drivers makes it easy to write applications that scale smoothly from a single server to a large, distributed cluster: a practical motivation for the distributed systems enthusiast.

In this talk we’ll explore the new Causal clustering architecture for Neo4j. We’ll see how Neo4j uses the Raft protocol for a robust underlay for intensive write operations, and how the asynchronous new scale-out mechanism provides enormous capacity for very demanding graph workloads.

We’ll discuss the cluster architecture’s new causal consistency model. Causal consistency is a big leap forward compared to the commonplace eventual consistency which makes it convenient to write applications that use the full capacity of the cluster. In particular we’ll show how despite the mixture of concensus protocols and asynchronous replication, that Neo4j allows users to read their own writes straightforwardly and discuss why this is such a difficult achievement in distributed systems.

For the application developer, we’ll show how Neo4j’s Causal Clustering optimised drivers makes it easy to write applications that scale smoothly from a single server to a large, distributed cluster: a practical motivation for the distributed systems enthusiast.