Big data, both structured and unstructured, is flooding the digital world. It comes from networks that span the globe–social media, online portals, internal business processes–and connects people and systems like never before. The interconnections between those data points can reveal powerful insights to organizations that know how to unlock them.
Graph databases can play a vital role in analyzing data points and interconnections. Using graph databases, you can analyze data, find relationships, and make assumptions. Graph databases can help you leverage data to understand your business and make informed decisions.
To fully unlock this data value, it’s important to integrate analytics tools with graph databases to provide a full picture and create a complete solution to data science problems. By integrating IBM Data Science Experience with OrientDB database, which has a graph database engine, you can create an powerful platform to launch a complete analytics solution.
Our new code pattern, Store, graph, and derive insights from interconnected data gives you a head start on working with graphs in OrientDB, using IBM Data Science Experience (DSX) and the PyOrient module.
When you complete this code pattern, you’ll walk through a complete end-to-end flow:
- Downloading the data set
- Cleansing the data
- Extracting entities and relations from the data set
- Connecting with OrientDB
- Creating a new OrientDB database
- Populating database with node classes, edge classes, vertices, relations
- Executing queries to get more insights from the OrientDB database
We created this code pattern to give you a solid understanding of OrientDB with IBM Data Science Experience. You can extend the pattern to create your own domain-specific knowledge graph according to your own business requirements. Check out Store, graph, and derive insights from interconnected data today!