Presentation: Graph Algorithms on ACID: Combining OLTP+OLAP+Visualization
Abstract
When most data scientists think of Graph Algorithms, they think of batch analytical processes running computation on a graph in R/iGraph, Gephi, etc. These tools certainly provide great insight into your data, but they don't provide the ability for applications to make real-time decisions based on these algorithms. For that, you need to store your data in an OLTP.
Neo4j is a native graph database which combines the ACID data guarantees you expect from SQL, with the schema flexibility you expect from NoSQL, and the performance for traversing connected data that you expect from a native graph database. It excels at querying your data using graph patterns. More recently, we've added the ability to run graph analytics on top of the database, using a new library of procedures, to make real-time decisions.
Why settle for your classic data analysis tools and making decisions in offline processes? This session will be a fast-paced intro to the power of graphs for transactions, storage, traversal, and analysis: Graph Algorithms on ACID (or HTAP!).
Do you still want to do some analysis offline? We'll also review visualization tools, including the newly-announced Bloom. We'll discuss the different controls we have in these tools that make it a lot easier to interpret the results of the algorithms.
