Closing the Analytics Loop: How Graph Databases Complement your Data Warehouse?
Traditional data warehouses and analytic tools are useful for producing the baseline insights that allow organizations to work more intelligently. But consider the following questions:
Graph databases build on the analytics foundation of data warehouses and data lakes and answer these questions with deeper insights into relationships.
Tony Baer, the Data Warehousing Guru, shares the best practices and real-world case studies for complementing your data warehouse with a graph database in this white paper.
TigerGraph is the only scalable graph database for the enterprise. Based on the industry’s first Native and Parallel Graph technology, TigerGraph unleashes the power of interconnected data, offering organizations deeper insights and better outcomes. TigerGraph fulfills the true promise and benefits of the graph platform by tackling the toughest data challenges in real time, no matter how large or complex the dataset. TigerGraph’s proven technology supports applications such as fraud detection, customer 360, MDM, IoT, AI and machine learning to make sense of ever-changing big data, and is used by customers including Amgen, China Mobile, Intuit, Wish and Zillow, along with some of the world’s largest healthcare, entertainment and financial institutions.