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Big Data Processing from Large IoT Networks
Benkő, Krisztián ; Podivínský, Jakub (referee) ; Krčma, Martin (advisor)
The goal of this diploma thesis is to design and develop a system for collecting, processing and storing data from large IoT networks. The developed system introduces a complex solution able to process data from various IoT networks using Apache Hadoop ecosystem. The data are real-time processed and stored in a NoSQL database, but the data are also stored in the file system for a potential later processing. The system is optimized and tested using data from IQRF network. The data stored in the NoSQL database are visualized and the system periodically generates derived predictions. Users are connected to this system via an information system, which is able to automatically generate notifications when monitored values are out of range.
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Distributed Big Data Processing on the Java Platform
Tutko, Jakub ; Rychlý, Marek (referee) ; Burget, Radek (advisor)
This thesis is focused on the distributed Big Data processing on the Java platform, together with graph databases. It analyses several graph database distributions and the possibilities to connect them to the Apache Hadoop system for distributed data processing. For the purpose of testing database solutions effectiveness, the thesis outcome is an application, which is downloading data from social networks Twitter and Facebook. It is able to write and analyse data with two different database frameworks which are Halyard and HGraphDB.
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Optimization of data reading from a distributed database
Kozlovský, Jiří ; Holek, Radovan (referee) ; Macho, Tomáš (advisor)
This thesis is focused on optimization of data reading from distributed NoSQL database Apache HBase with regards to the desired data granularity. The assignment was created as a product request from Seznam.cz, a.s. the Reklama division, Sklik.cz cost center to improve user experience by making filtering of aggregated statistical data available to advertiser web application users for the purpose of viewing entity performance history.
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Big Data Processing from Large IoT Networks
Benkő, Krisztián ; Podivínský, Jakub (referee) ; Krčma, Martin (advisor)
The goal of this diploma thesis is to design and develop a system for collecting, processing and storing data from large IoT networks. The developed system introduces a complex solution able to process data from various IoT networks using Apache Hadoop ecosystem. The data are real-time processed and stored in a NoSQL database, but the data are also stored in the file system for a potential later processing. The system is optimized and tested using data from IQRF network. The data stored in the NoSQL database are visualized and the system periodically generates derived predictions. Users are connected to this system via an information system, which is able to automatically generate notifications when monitored values are out of range.
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Optimization of data reading from a distributed database
Kozlovský, Jiří ; Holek, Radovan (referee) ; Macho, Tomáš (advisor)
This thesis is focused on optimization of data reading from distributed NoSQL database Apache HBase with regards to the desired data granularity. The assignment was created as a product request from Seznam.cz, a.s. the Reklama division, Sklik.cz cost center to improve user experience by making filtering of aggregated statistical data available to advertiser web application users for the purpose of viewing entity performance history.
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Distributed Big Data Processing on the Java Platform
Tutko, Jakub ; Rychlý, Marek (referee) ; Burget, Radek (advisor)
This thesis is focused on the distributed Big Data processing on the Java platform, together with graph databases. It analyses several graph database distributions and the possibilities to connect them to the Apache Hadoop system for distributed data processing. For the purpose of testing database solutions effectiveness, the thesis outcome is an application, which is downloading data from social networks Twitter and Facebook. It is able to write and analyse data with two different database frameworks which are Halyard and HGraphDB.
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Hadoop NoSQL database
Švagr, Lukáš ; Palovská, Helena (advisor) ; Tomášková, Barbora (referee)
The theme of this work is database storage Hadoop Hbase. The main goal is to demonstrate the principles of its function and show the main usage. The entire text assumes that the reader is already familiar with the basic principles of NoSQL databases. The theoretical part briefly describes the basic concepts of databases then mostly covers Hadoop and its properties. This work also includes the practical part which describes how to install a database repository and illustrates basic database operations in two simple programs. The components of the practical part are case studies that report current use of Hadoop in the world-famous companies.
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Comparison of distributed "NoSQL" databases with focus on performance and scalability
Vrbík, Tomáš ; Šlajchrt, Zbyněk (advisor) ; Pavlíček, Luboš (referee)
This paper focuses on NoSQL database systems. These systems currently serve rather as supplement than replacement of relational database systems. The aim of this paper is to compare 4 selected NoSQL database systems (MongoDB, Apache Cassandra, Apache HBase and Redis) with a main focus on performance and scalability. Performance comparison is done using simulated workload in a 4 nodes cluster environment. One relational SQL database is also benchmarked to provide comparison between classic and modern way of maintaining structured data. As the result of comparison I found out that none of these database systems can be labeled as "the best" as each of the compared systems is suitable for different production deployment.
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