Nowadays software performance is evaluated not only by specialized review companies but it is more and more starting to be a common practice for the software developers themselves. Companies are often forced to develop and maintain their own tools for measuring performance of the developed applications. On the Faculty of Mathematics and Physics there has been created a toolkit for automation of software performance evaluation called BEEN. This toolkit should significantly ease the management of individual performance measurements but it is not possible to test it thoroughly in the environment where it was created. The goal of this thesis is to deploy BEEN in a real environment of commercially oriented company and evaluate the usability of this toolkit for the developers. We will focus on evaluating both objective and subjective positives and drawbacks of this toolkit as observed by unbiased users.

Today, when businesses and organizations are generating huge volumes of data; the applications like Web 2.0 or social networking requires processing of petabytes of data. Stock Exchange Systems are among the ones that process large amount of quotes and trades on a daily basis. The limited database storage ability is a major bottleneck in meeting up the challenge of providing efficient access to information. Further to this, varying data are the major source of information for the financial industry. This data needs to be read and written efficiently in the database; this is quite costly when it comes to traditional Relational Database Management System. RDBMS is good for different scenarios and can handle certain types of data very well, but it isn’t always the perfect choice. The existence of innovative architectures allows the storage of large data in an efficient manner. “Not only SQL” brings an effective solution through the provision of an efficient information storage capability. NOSQL is an umbrella term for various new data store. The NOSQL databases have gained popularity due to different factors that include their open source nature, existence of non-relational data store, high-performance, fault-tolerance, and scalability to name a few. Nowadays, NOSQL databases are rapidly gaining popularity because of the advantages that they offer compared to RDBMS. The major aim of this research is to find an efficient solution for storing and processing the huge volume of data for certain variants. The study is based on choosing a reliable, distributed, and efficient NOSQL database at Cinnober Financial Technology AB. The research majorly explores NOSQL databases and discusses issues with RDBMS; eventually selecting a database, which is best suited for financial data management. It is an attempt to contribute the current research in the field of NOSQL databases which compares one such NOSQL database Apache Cassandra with Apache Lucene and the traditional relational database MySQL for financial management. The main focus is to find out which database is the preferred choice for different variants. In this regard, the performance test framework for a selected set of candidates has also been taken into consideration.

This thesis deals with NoSQL database performance issue. The aim of the paper is to compare most common prototypes of distributed database systems with emphasis on performance and scalability. Yahoo! Cloud Serving Benchmark (YCSB) is used to accomplish the aforementioned aim. The YCSB tool allows performance testing through performance indicators like throughput or response time. It is followed by a thorough explanation of how to work with this tool, which gives readers an opportunity to test performance or do a performance comparison of other distributed database systems than of those described in this thesis. It also helps readers to be able to create testing environment and apply the testing method which has been listed in this thesis should they need it. This paper can be used as a help when making an arduous choice for a specific system from a wide variety of NoSQL database systems for intended solution.

Aufgrund der in vielen Bereichen stets wachsenden Menge an zu verarbeitenden Daten haben sich Big-Data-Anwendungen in den letzten Jahren zunehmend verbreitet. Twitter gab bereits im Jahr 2011 an, täglich 15 Millionen URLs in Echtzeit zu untersuchen, um die Verbreitung von Spamlinks zu unterbinden [1]. Facebook verarbeitet pro Minute über vier Millionen „Gefällt mir“-Klicks und verwaltet über 300 Petabyte Daten [2]. Über das Businessportal LinkedIn wurden 2011 rund eine Milliarde Nachrichten pro Tag zugestellt, 2015 waren es laut Angaben des Unternehmens bereits 1,1 Billionen täglich versendete Nachrichten [3]. Diesem starken Anstieg liegt ein exponentielles Wachstum zugrunde, das für Big Data typisch ist. Gartner definiert den Begriff „Big Data“ auf Basis seiner spezifischen Eigenschaften, die in englischer Sprache auch als die „drei V´s“ bezeichnet werden: „Volume“, „Variety“ und „Velocity“ [4]. Neben der enormen Menge an zu verarbeitenden Daten („Volume“) und ihrer Vielfalt und Unstrukturiertheit („Variety“), ist demnach auch die Geschwindigkeit („Velocity“), in der die Daten generiert werden, ein wesentliches Merkmal von Big Data [5, 6]. Soll trotz der ständigen und immer schneller werdenden Generierung neuer Daten ein Verarbeitungsrückstau vermieden werden, so folgt daraus auch die Notwendigkeit, die kontinuierlich wachsenden Datenmengen immer schneller zu verarbeiten.