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Comparison analysis of DBMSs and development of data store module. Сoncept for financial analysis needs

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«Comparison analysis of DBMSs and
development of data store module
concept for financial analysis needs»
Made by the Р9-204 group student:
Grinenko A. M.
Research advisor:
Pakhomov A. P.
January, 18 2016г

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Aims and goals
Research actuality:
New data technologies give instruments for data storage
improvements but they are not actually used.
Aims and goals:
• Compare different DBMS and data storage approaches
• Measure and explane better performance
• Develop a data store concept for financial analysis
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Case study highlight
Improved performance = better decision-making:
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Huge amount of information from different organizations
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Different data types.
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Unstructured data parsing.
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Detailed, accurate search.
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Quick indexes update.

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DBMS
Relational DBMS
Object-oriented DMBS
DataBase
Management
Systems
NoSQL Search Engines
In-memory databases
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Relational vs NoSQL
Compare productivity using two metricks:
Queries per second;
Queries per second per thread;
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6.

Estimations
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MongoDB could handle
more complex queries faster
than Oracle;
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Oracle performs better when
deleting data when
MongoDB performed better
during insertions;
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7.

ElasticSearch vs Mongodb
Indexing
MongoDb
14.6 sec
ElasticSearch
0,7 sec
Aggregation
MongoDb
42,116 sec(first try) - 3.779 sec(second
try)
ElasticSearch
0,902 sec
Insertion
MongoDb
522 sec -> ~12,159 documents/second
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ElasticSearch
600 sec -> ~10,597 documents/second

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Data storage prototype
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Results
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Different DBMS and data storage
approaches were compared
Better performance was measured and
explained
The data store concept for financial
analysis workspace was developed

10.

Thank you for attention!
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