HBase:

Key characteristics:

· Distributed and scalable big data store

· Strong consistency

· Built on top of Hadoop HDFS

· CP on CAP

Good for:

· Optimized for read

· Well suited for range based scan

· Strict consistency

· Fast read and write with scalability

Not good for:

· Classic transactional applications or even relational analytics

· Applications need full table scan

· Data to be aggregated, rolled up, analyzed cross rows

Usage Case: Facebook message

Cassandra:

Key characteristics:

· High availability

· Incremental scalability

· Eventually consistent

· Trade-offs between consistency and latency

· Minimal administration

· No SPF (Single point of failure) – all nodes are the same in Cassandra

· AP on CAP

Good for:

· Simple setup, maintenance code

· Fast random read/write

· Flexible parsing/wide column requirement

· No multiple secondary index needed

Not good for:

· Secondary index

· Relational data

· Transactional operations (Rollback, Commit)

· Primary & Financial record

· Stringent and authorization needed on data

· Dynamic queries/searching on column data

· Low latency

Usage Case: Twitter, Travel portal

MongoDB:

Key characteristics:

· Schemas to change as applications evolve (Schema-free)

· Full index support for high performance

· Replication and failover for high availability

· Auto Sharding for easy Scalability

· Rich document based queries for easy readability

· Master-slave model

· CP on CAP

Good for:

· RDBMS replacement for web applications

· Semi-structured content management

· Real-time analytics and high-speed logging, caching and high scalability

· Web 2.0, Media, SAAS, Gaming