Empower your investors with next-gen digital wealth solutions

Have you ever stopped to understand what graph databases are and what they can do for you? Graph databases and graph processing frameworks are all the hype in the NoSQL world at the moment. The ecosystem is constantly evolving and different datastores of processing frameworks are coming out what seems like weekly. The truth is that graph databases are a great way to solve certain application problems in areas such as personalization and recommendation, logistics, master data management, social networks, fraud or IoT but many people are completely lost when they step foot into the exosystem. In this session we will help you make sense of the graph ecosystem with an examination of a variety of graph datastores (e.g. Neo4j, DSE Graph, Titan, OrientDB, etc.) and graph processing frameworks (e.g. Giraph, GraphX, Elasticsearch Graph, GraphQL, Pregel, etc.). We will then discussing how you might use these technologies to augment or replace complex portions of your applications. In the end you will walk away with a better appreciation for the practical aspects of the graph ecosystem and you might even find out how to remove that complex recursive SQL CTE that gives you nightmares.

Building a data ingestion pipeline using Spark, Kafka, DataStax, Nifi, and Pentaho.

How do you handle customer #2? You delivered an MVP of some hosted software for customer #1. Your brother-in-law knows a guy who has a similar problem and after a lunch meeting, now you need to add customer #2 to your incubating SaaS tool. Of course customer #1 and customer #2 shouldn’t be able to see each other’s data, but you don’t necessarily want to install and configure everything all over again just because you added another customer.