Overview
In today's evolving world of distributed computing, we need answers to complex questions quickly. Graph databases provide an efficient way to represent and query relationships between entities.
Problem Scenario
Consider these business questions:
- How many products were ordered last Friday between 10am to 12pm that are offered by each supplier?
- How many products are returned that are offered by each supplier during the last quarter?
Traditionally, relational database systems struggle with complex joins and scalability issues when managing such intricate relationship queries.
Solution: Graph Databases
Graph databases allow representing relationships as equally important as entities. They present data as a graph where entities are nodes and relationships are edges. You can traverse such a graph to answer complex queries most efficiently, even if you have hundreds of millions of relationships.
These distributed graph computing frameworks, such as Titan, rely on external persistence systems for data storage. The architecture matters not just the set of tools involved.
Use Cases
Key applications of graph databases include:
- Supply chain management: Managing details of retail outlets, airports, warehouses, and their interconnections
- Product recommendation engines: Tracking customers, products purchased, and product relationships
- Page rank calculations: Computing importance scores for web pages
- Finding shortest paths: Determining optimal routes and connections
Data Partitioning
Depending on business needs, you can partition data between graph databases and other systems to optimize performance and cost.
Related Concepts
Understanding that insight is today's currency is crucial for business readiness in implementing advanced data technologies.