Michael Hackstein

Graph databases allow users to analyze highly interconnected datasets and find patterns within these relationships. Social networks, corporate hierarchies, fraud detection, network analytics, or building whole knowledge graphs are great use cases for graph databases. However, these datasets of nodes and connecting edges change over time. Whether you are a developer, architect or data scientist, you may want to time travel for analyzing the past or even predict tomorrow.

While your graph database may be lacking built-in support for managing the revision history of graph data, this talk will show you how to manage it in a performant manner for general classes of graphs. Best of all, this won’t require any groundbreaking new ideas. We’ll simply borrow a few tools and tricks from existing persistent data structure literature and adapt them for good performance within the graph database software. This will help enable new ways to manipulate and exploit graph data and hopefully power new and exciting applications.

The complexity and amount of data rises. Modern graph databases are designed to handle the complexity but still not for the amount of data. When hitting a certain size of a graph, many dedicated graph databases reach their limits in vertical or, most common, horizontal scalability. In this talk I’ll provide a brief overview about current approaches and their limits towards scalability. Dealing with complex data in a complex system doesn’t make things easier… but more fun finding a solution. Join me on my journey to handle billions of edges in a graph database.

What we see in the modern data store world is a race between different approaches to achieve a distributed and resilient storage of data. Most applications need a stateful layer which holds the data. There are at least three necessary ingredients which are everything else than trivial to combine and of course even more challenging when heading for an acceptable performance.

Over the past years there has been significant progress in respect in both the science and practical implementations of such data stores. In his talk Max Neunhoeffer will introduce the audience to some of the needed ingredients, address the difficulties of their interplay and show four modern approaches of distributed open-source data stores.

Topics are:

• Challenges in developing a distributed, resilient data store
• Consensus, distributed transactions, distributed query optimization and execution
• The inner workings of ArangoDB, Cassandra, Cockroach and RethinkDB

The talk will touch complex and difficult computer science, but will at the same time be accessible to and enjoyable by a wide range of developers.