The world we live in isn't just relational, so why should your data be stored that way?
Today’s data management requires reliable handling of master data at Big Data volume, variety, and velocity. There are two broad categories of databases in the market, Relational and NoSQL. And then there are variations of NoSQL such as a graph, key-value, and document. The challenge is comparing, contrasting, and figuring out what database is the right one for your business.
There are dozens of databases to choose from. Data architects have been exploring multiple options for various business needs, with a realization that a single type of database is not optimum for all application use cases. This thought led to the notion ofpolyglot persistence. The concept explores the idea that a single application should be able to talk to different database types to achieve the business objective.
When you build a data foundation for enterprise applications requiring information from internal, external, third-party, and social data sources, you need a data store that is flexible to handle all disparate data types with big data scalability. You also need the agility to update the data model quickly without any impact on the performance.
In this article, I am going to discuss the idea of hybrid data stores designed to handle the most complex multi-domain master data management (MDM) challenges and at the same time effortlessly bringing together transactional, interaction, social and machine-generated data at scale. Inspired by consumer applications like LinkedIn (Economic Graph) and Facebook (Social Graph), hybrid data foundation helps create data-driven applications that are infinitely scalable, flexible, and extensible.
An example of such data-driven application is Customer 360 for consolidating customer information by bringing together data from multiple sources like CRM, ERP, support, marketing automation, social, channel, and others. For a true Customer 360 view, you may also want to augment customer profile by adding data from third-party providers like data.com.
Business users want access to accurate and complete customer data and analytical insights (customer value, churn propensity, and next-best-offer) to help increase revenue and improve customer experience. Customer-facing teams also want to uncover the relationships that customers have with various organizations, places, and products.
A single columnar or Graph database cannot easily support such use cases. Columnar databases do not manage relationships that well. Graph databases while suited for uncovering and handling relationships, don’t have the horizontal scalability to meet enterprise requirements. When a Fortune 500 company wants to manage 100 million or above customer profiles, across the globe, across thousands of product offering, the graph databases alone is not up to the task. A modern data management platform with a polyglot approach, built on columnar-graph hybrid stores are more suited for applications with varying data storage needs.
