Row-Based Vs Columnar Vs NoSQL
There are various Database players in the market. Here is one quick comparsion on Row-Based Vs Vs Columnar Vs NoSQL.
Row-basedDescription: Data structured or stored in Rows.
Common Use Case: Used in transaction processing, interactive transaction applications.
Strength: Robust, proven technology to capture intermediate transactions.
Weakness: Scalability and query processing time for huge data.
Size of DB: Several GB to TB.
Key Players: Sybase, Oracle, My SQL, DB2
ColumnarDescription: Data is vertically partitioned and stored in Columns.
Common Use Case: Historical data analysis, data warehousing and business Intelligence.
Strength: Faster query (specially ad-hoc queries) on large data.
Weakness: not suitable for transaction, import export seep & heavy computing resource utilization.
Size of DB: Several GB to 50 TB.
Key Players: Info Bright, Asterdata, Vertica, Sybase IQ, Paraccel
NoSQL Key Value StoredDescription: Data stored in memory with some persistent backup.
Common Use Case: Used in cache for storing frequently requested data in applications.
Strength: Scalable, faster retrieval of data , supports Unstructured and partial structured data.
Weakness: All data should fit to memory, does not support complex query.
Size of DB: Several GBs to several TBs.
Key Players: Amazon S3, MemCached, Redis, Voldemort
NoSQL- Document StoreDescription: Persistent storage of unstructured or semi-structured data along with some SQL Querying functionality.
Common Use Case: Web applications or any application which needs better performance and scalability without defining columns in RDBMS.
Strength: Persistent store with scalability and better query support than key-value store.
Weakness: Lack of sophisticated query capabilities.
Size of DB: Several TBs to PBs.
Key Players: MongoDB, CouchDB, SimpleDb
NoSQL- Column StoreDescription: Very large data store and supports Map-Reduce.
Common Use Case: Real time data logging in Finance and web analytics.
Strength: Very high throughput for Big Data, Strong Partitioning Support, random read-write access.
Weakness: Complex query, availability of APIs, response time.
Size of DB: Several TBs to PBs.Key Players: HBase, Big Table, Cassandra
Posted on October 2, 2012, in amazon s3, Big Table, business intelligence, Cassandra, columnar, CouchDB, data, database, gartner, Hbase, key value, MemCached, MongoDB, NoSQl, partition, query, Redis, row based, SimpleDB. Bookmark the permalink. Leave a comment.