Thursday, January 26, 2012

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.
Description: 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
Description: 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 Stored
Description: 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 Store
Description: 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 Store
Description: 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

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