You’ve probably heard of a graph database – but what is it?
The term “graph database” was coined to describe a slightly different approach to data storage. Instead of focusing on putting data into delimited, normalized tables with rows and columns, in a graph database, the focus is on adjacency. Adjacency refers to how “close” data elements are to each other.
According to Wikipedia:
Graph databases employ nodes, properties, and edges.
- A node refers to an piece of data
- A property refers to something known about that data
- Edges are links that connect nodes
- And the graph is the representation that ties it all together!
So what can a graph database do?
Modern enterprises are trying to answer more questions by using data. But often it’s almost impossible to know what data is relevant to the question. By viewing all data in a graph, you can see how the data is related. As the saying goes, seeing is believing. When you see that a node is connected along a relevant edge, you can take that node into consideration.
We are drowning in information, but we are starved for knowledge. -John Naisbitt, Megatrends (1982)
The main benefit of this is to help you identify the data needed for any decision. If you’re starving for knowledge, being able to cut through the noise to find the right data point is critical!
How does flockdata fit into the equation?
flockdata leverages Neo4j, one of the world’s leading graph database technologies. flockdata can help you aggregate data from multiple sources via simple RESTful API call. Once the data is collected, you can use flockdata to search across your enterprise data. Again, the graph will help you find the right nodes, and edges to investigate. Once found, you can query the data to dig for deeper knowledge. flockdata will help satisfy your hunger for knowledge.