CAP Theorem

CAP Theorem for a distributed architecture:   Consistency: The data in database remains consistent after the execution of an operation. Example: After an insert operation all clients will see the same data Availability:The System is always On/Available. Partition Tolerance:The system continues to function even if the servers are not able to communicate with each other […]

HBase Basics

Difference between HBase and RDBMS? Hbase RDBMS Column Oriented Row Oriented Flexible schema, add columns on the fly Fixed Schema Good with sparse table Not optimized for sparse table Join using MapReduce not optimized Not Applicable Horizontal Scalability (Add hardware) Hard to shard and scale Good for structured and semi structured data Good for structured […]

HBase vs Cassandra

Hbase Cassandra Distributed and scalable big data store High availability and Incremental scalability Strong consistency Eventually consistent Built on top of Hadoop Trade – offs between consistency and latency Distributed file system No single point of failure and Minimal administration CP on CAP theorem AP on CAP Theorem Note: CAP Theorem is discussed in MongoDB […]