Compared to their relational counterparts, NoSQL databases generally have a flexible data storage format. Whereas most databases require tables to be defined up front, most NoSQL data stores often accept free form records on the fly, with few formatting constraints. Often called schemaless, this is useful when the exact information needed may vary from record to record.
Applications typically access NoSQL data in the form of single records or batches of records in a collection. This simple access pattern has the advantage of allowing a very large data set to be spread across multiple servers, scaling horizontally through the use of both replication and sharding. As a result, very large data sets can be accessed by applications.
The landscape is rich with options. There are several NoSQL database solutions, including Apache Cassandra, CouchDB, MongoDB, Hadoop, and HBase. Each of these has different capabilities, strengths, and weaknesses.
If you find your data access patterns are simple but your data set is large, then NoSQL may be right for you. Whether you have 1 server or 30, let End Point’s expertise help you design and maintain your NoSQL data store.
Some NoSQL techonlogies we use:
- Updated NoSQL benchmark: Cassandra, MongoDB, HBase, Couchbase
- New NoSQL benchmark: Cassandra, MongoDB, HBase, Couchbase
- Web Development, Big Data and DevOps - OSI Days 2014, India
- Riding the Elasticsearch River on a CouchDB: Part 1
- MongoDB and OpenStack - OSI Days 2014, India
- CouchDB pagination with couchdb-python
- PostgreSQL as NoSQL with Data Validation
- NoSQL benchmark of Cassandra, HBase, MongoDB
- MongoDB replication from Postgres using Bucardo
- NoSQL at RailsConf 2010: An Ecommerce Example
- NoSQL Live: The Dynamo Derivatives (Cassandra, Voldemort, Riak)
- Quick Thoughts on NoSQL Live Boston Conference
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