DocumentCode :
1667184
Title :
No SQL in Practice: A Write-Heavy Enterprise Application
Author :
Lourenco, Joao Ricardo ; Abramova, Veronika ; Cabral, Bruno ; Bernardino, Jorge ; Carreiro, Paulo ; Vieira, Marco
Author_Institution :
CISUC, Coimbra, Portugal
fYear :
2015
Firstpage :
584
Lastpage :
591
Abstract :
The continuous information growth in current organizations has created a need for adaptation and innovation in the field of data storage. Alternative technologies such as NoSQL have been heralded as the solution to the ever-growing data requirements of the corporate world, but these claims have not been backed by many real world studies. Current benchmarks evaluate database performance by executing specific queries over mostly synthetic data. These artificial scenarios, then, prevent us from easily drawing conclusions for the real world and appropriately characterize the performance of databases in a real system. To counter this, we used a real world enterprise system with real corporate data to evaluate the performance characteristics of popular NoSQL databases and compare them to SQL counterparts. In particular, we present one of the first write-heavy evaluations using enterprise software and big data. We tested Cassandra, Mongo DB, Couchbase Server and MS SQL Server and compared their performance while handling demanding and large write requests from a real company with an electrical measurement enterprise system.
Keywords :
Big Data; business data processing; database management systems; Cassandra; Couchbase server; MS SQL Server; Mongo DB; NoSQL databases; big data; data storage; database performance evaluation; electrical measurement enterprise system; enterprise software; write-heavy enterprise application; write-heavy evaluations; Benchmark testing; Big data; Databases; Scalability; Servers; Throughput; Big Data; Cassandra; Couchbase; Enterprise; MongoDB; NoSQL; SQL Server; Write-Heavy;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Big Data (BigData Congress), 2015 IEEE International Congress on
Conference_Location :
New York, NY
Print_ISBN :
978-1-4673-7277-0
Type :
conf
DOI :
10.1109/BigDataCongress.2015.90
Filename :
7207274
Link To Document :
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