The goal of this work is to analyze systems Maven and Gradle and to compare one against the other. Whilst I was analyzing both the systems user guides in Czech language were created. To achieve the set of goals of the thesis I had to use user guides and books that were written in English as a source of information. The user guides describes the system basics, dependency management, testing and multi-project builds. Comparisons between the two systems were created from information from the user guides and personal experi-ence of the author. The practical part of the thesis contains files for build application in Maven and Gradle.

Software rebuilding is the process of deriving a deployable software system from its primitive source objects. A build tool helps maintain consistency between the derived objects and source objects by ensuring that all necessary build steps are re-executed in the correct order after a set of changes is made to the source objects. It is imperative that derived objects accurately represent the source objects from which they were supposedly constructed; otherwise, subsequent testing and quality assurance is invalidated. This thesis aims to advance the state-of-the-art in tool support for automated software rebuilding. It surveys the body of background work, lays out a set of design considerations for build tools, and examines areas where current tools are limited. It examines the properties of a next-generation tool concept, redo, conceived by D. J. Bernstein; redo is novel because it employs a purely top-down approach to software rebuilding that promises to be simpler, more flexible, and more reliable than current approaches. The details of a redo prototype written by the author of this thesis are explained including the central algorithms and data structures. Lastly, the redo prototype is evaluated on some sample software systems with respect to migration effort between build tools as well as size, complexity, and performances aspects of the resulting build systems.

Software rebuilding is the process of deriving a deployable software system from its primitive source objects. A build tool helps maintain consistency between the derived objects and source objects by ensuring that all necessary build steps are re-executed in the correct order after a set of changes is made to the source objects. It is imperative that derived objects accurately represent the source objects from which they were supposedly constructed; otherwise, subsequent testing and quality assurance is invalidated. This thesis aims to advance the state-of-the-art in tool support for automated software rebuilding. It surveys the body of background work, lays out a set of design considerations for build tools, and examines areas where current tools are limited. It examines the properties of a next-generation tool concept, redo, conceived by D. J. Bernstein; redo is novel because it employs a purely top-down approach to software rebuilding that promises to be simpler, more flexible, and more reliable than current approaches. The details of a redo prototype written by the author of this thesis are explained including the central algorithms and data structures. Lastly, the redo prototype is evaluated on some sample software systems with respect to migration effort between build tools as well as size, complexity, and performances aspects of the resulting build systems.

I detta examensarbete utförs en jämförande analys av metoder och verktyg för hantering och analys av data inom datalager. Med den snabbt ökande mängden data och utvecklingen av molnteknologier står företag inför utmaningen att navigera bland olika metoder för att välja den mest lämpliga för sin specifika datahantering och analysbehov. Rapporten belyser metoden One Big Table (OBT) samt verktyget Data Build Tool (dbt) och undersöker deras för- och nackdelar i datalagermiljöer. För att få en djupare förståelse för deras funktion och effektivitet jämförs de i olika användarfall genom prestandatester på latens och samtidighet med hjälp av verktyget Hyperfine. OBT implementeras med hjälp av Google BigQuery såväl som Google Cloud SQL för PostgreSQL där latens och samtidighet för analytiska målsättningar utvärderas genom användning av Python-skript med SQL-frågor respektive med dbt-modeller. Skripten och dbt-modellerna körs mot BigQuery samt PostgreSQL och de båda implementerar OBT. Resultatet visar att SQL-skripten uppvisade lägre latens än dbt-modeller när de exekverades mot både BigQuery och PostgreSQL. Ett annat fynd är att latensen för SQL-skripten var lägre i PostgreSQL jämfört med BigQuery, medan dbt-modellerna istället uppvisade högre latens i PostgreSQL jämfört med BigQuery. I båda datalagermiljöer visas det även att SQL-skripten presterar bättre än dbt-modeller vid samtidiga körningar. / This bachelor’s thesis presents a comparative analysis of methods and tools for data management and analysis within data warehouses. With the rapidly increasing volume of data and the development of cloud technologies, companies face the challenge of navigating various methods to choose the most suitable one for their specific data management and analysis needs. The report highlights the One Big Table (OBT) method and the Data Build Tool (dbt), examining their advantages and disadvantages in data warehouse environments. To gain a deeper understanding of their functionality and efficiency, they are compared in different use cases through performance tests on latency and concurrency using the Hyperfine tool. OBT is implemented using Google BigQuery as well as Google Cloud SQL for PostgreSQL, where latency and concurrency for analytical purposes are evaluated using Python scripts with SQL queries and dbt models. The scripts and dbt models are run against BigQuery and PostgreSQL, both implementing OBT. The results show that the SQL scripts exhibited lower latency than the dbt models when executed against both BigQuery and PostgreSQL. Another finding is that the latency for SQL scripts was lower when run against PostgreSQL compared to BigQuery, while dbt models showed higher latency when run against PostgreSQL compared to BigQuery. The SQL scripts also performed better than the dbt models in concurrent executions in both BigQuery and PostgreSQL.

This diploma thesis is focused on the automatization of digitization workflow in the National Library of Technology, Prague, Czech Republic. This thesis examines possibilities of digitization processes automatization by means of scripts written in Perl programming language and Apache Ant build tool. The advantages and disadvantages of both solutions are analyzed as well as their suitability for automatization of digitization workflow. Based on the comparison of both solutions, the scripts in Perl programming language are selected as the most suitable solution for automatization of digitization workflow. The question whether Ant build tool could be used for the purpose of automatization of digitization workflow is also answered in this thesis. The Ant build tool could be used for the above-mentioned purpose. However, once the activities in the given process divert from the general scope of tasks provided by the Ant build tool, the complexity of the whole solution increases rapidly. This complexity is given by the necessity to use predefined tasks -- sets of functions which have to be combined to create a functional automatization script. Even though Ant is an extendable tool, it is necessary to understand the Java programming language in order to create a new Ant task successfully. On the other hand, the Perl programming language allows easier customization of the script for the purposes of automatization of digitization workflow. Also, the modularity of the Perl programming language makes it easier to create those scripts and modify, correct or develop them even further.