Graph Databases

Graph Databases

Social media platforms such as Facebook, Instagram, and Twitter all use graph databases and analytics to understand how users relate to each other and connect them with the right content. Google search is blindingly fast due to Google’s Knowledge Graph, and Amazon can recommend you an item to purchase by analysing billions of transactions in just a split second due to the use of advanced graph analytics.

First-generation graph databases were built with native graph storage but were not made to handle large data or query volumes or perform beyond three levels or connections — known as hops — inside the graph. With every hop in a graph, the scope of the search expands dramatically and the insights gleaned become deeper.

Second-generation graph databases were built on top of NoSQL storage, which allowed them to load large amounts of data. However, they still do not scale for queries involving three or more hops. Older graph databases also typically do not support “database sharding” — partitioning of data across a number of servers to increase scalability-which means, a large graph with terabytes of data can’t be distributed. These legacy graph databases are ill-equipped to scale up to today’s real-world requirements, which call for a system that can perform many hops efficiently and in parallel to deliver sub-second query performance on big data.

Relational Database

COMPLEX, SLOW, TABLE JOINS REQUIRED
  • Rigid schema
  • High performance for transactions
  • Poor performance for deep analytics

 

Key-Value Database

MULTIPLE SCANS OF MASSIVE TABLE REQUIRED

  • High fluid schema/no schema
  • High performance for simple transactions
  • Poor performance for deep analytics

 

Graph Database

PRE-CONNECTED BUSINESS ENTITIES — NO JOINS NEEDED

  • Flexible schema
  • High performance for complex transactions
  • High performance for deep analytics

TigerGraph is a new kind of graph database, a native parallel graph database purpose-built for loading massive amounts of data (terabytes) in hours and analyzing as many as 10 or more hops deep in to relationships in real-time. TigerGraph supports transaction as well as analytical workloads, is ACID compliant, scales up and out with database sharding. TigerGraph’s proven technology supports applications such as fraud detection, customer360, IoT, AI and machine learning to make sense of ever-changing big data, and is used by customers including Intuit, China Mobile, Wish and Zillow.