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Kundera: An Easy Transition to NoSQL

Are you planning to transition from traditional data stores to NoSQL, but are wary of the complexity of NoSQL data stores? Or maybe you need a combination of RDBMS and NoSQL, or NoSQL and NewSQL data stores? It can be a little intimidating. The truth is that Big Data technologies are new and complex. Any Big Data initiative will present your organization with a number of challenges and steep learning curves.

Kundera, an open source project backed by Impetus Technologies, is a JPA-based object-relational mapping (ORM) tool that facilitates the transition from RDMS to NoSQL or NewSQL data stores while reducing the core challenges facing developers of Big Data applications.

Using well-known JPA constructs, Kundera hides the complexities of working with diverse data stores to provide developers with unified experience for caching, schema generation, transitive persistence, lazy fetching, and REST-based access. It facilitates the transition to NoSQL or NewSQL by enabling you to redirect the storage, reading, and querying with a simple switch of an annotation.

Kundera is the only JPA-based solution for reading, writing, querying, and persistence across RDMS, NoSQL, and NewSQL data stores. Currently, Kundera supports Cassandra, MongoDB, HBase, Oracle NoSQL Database, and most RDBMs. Plugins for additional data stores can be created with very little effort.

Kundera helps you to:
  • Reduce the learning curve by using well-known JPA constructs to read, write, query, transact, and index NoSQL data stores
  • Seamlessly work with any polyglot combination of RDBMS, NoSQL, and NewSQL data stores
  • Map your existing object model to the NoSQL data store-specific data structure
  • Write neater-and-cleaner code with reduced complexity and quality improvements
Key Features and Benefits
Mapping
  • Entities
  • Relationships
  • Intelligent data store-specific storage options
Polyglot
  • Seamless CRUD across NoSQL, NewSQL, or RDBMS data stores
  • Relationship definitions across data stores
  • Atomic transactions across data stores
Strengths
  • Flexibility to set up or switch vendors, bundles, and versions
  • Big Data ecosystem certification capability
  • Interceptors/events
  • Object caching
  • Connection pooling
  • Batch CRUD
  • Custom indexing
  • Schema generation
  • Data store-specific extensions
Consumption Interfaces
  • JPA
  • REST
Extensibility
  • Pluggable architecture for adding new NoSQL data stores