Consider using ACID-compliant graph databases like Neo4j instead of other NoSQL or RDBMS databases

A graph database is purpose-built to handle highly connected data, and the increase in the volume and connectedness of today’s data presents a tremendous opportunity for sustainable competitive advantage.When it comes to applying a graph database to a real-world problem, with real-world technical and business constraints, enterprise organizations choose graph databases for the following reasons:
  • Minutes-to-Milliseconds Performance
    Query performance and responsiveness are at the top of many organizations’ concerns with regard to their data platforms. Online transactional systems – large web applications in particular – must respond to end users in milliseconds if they are to be successful. 

    In the relational world, as an application’s dataset size grows, JOIN pains begin to manifest themselves, and performance deteriorates. Using index-free adjacency, a graph database turns complex JOINs into fast graph traversals – which are constant time operations – thereby maintaining millisecond performance irrespective of the overall size of the dataset.

  • Drastically Accelerated Development Cycles 

    The graph data model reduces the impedance mismatch that has plagued software development for decades, thereby reducing the development overhead of translating back and forth between an object model and a tabular relational model. 

    More importantly, the graph model reduces the impedance mismatch between the technical and business domains. Subject matter experts, architects and developers can talk about and picture the core domain using a shared model that is then incorporated into the application itself.

  • Extreme Business Responsiveness

    Successful applications rarely stay still. Changes in business conditions, user behaviors, and technical and operational infrastructures drive new requirements. In the past, this has required organizations to undertake careful and lengthy data migrations that involve modifying schemas, transforming data and maintaining redundant data to serve old and new features. 

    Developing with graph databases aligns perfectly with today’s agile, test-driven development practices, allowing your graph database to evolve in step with the rest of the application and any changing business requirements. Rather than exhaustively modeling a domain ahead of time, data teams can add to the existing graph structure without endangering current functionality.

  • Enterprise ready 
    When employed in a mission-critical application, a data technology must be robust, scalable and – more often than not – transactional. Although some graph databases are fairly new and not yet fully mature, there are graph databases on the market that provide all the -ilities needed by large enterprises today:

  • ACID transactionality
  • High availability
  • Horizontal read scalability
  • Storage of billions of entities
These characteristics have been an important factor leading to the adoption of graph databases by large organizations, not merely in modest offline or departmental capacities, but in ways that truly transform the business.


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