Structured data stores have been the dominating technologies for the past few decades. Although dominating, structured data stores lack the functionality to handle the ‘Big Data’ phenomenon. A new technology has recently emerged which stores unstructured data and can handle the ‘Big Data’ phenomenon. This study describes the development of an artefact to aid in the analysis of NoSQL document data stores in terms of relational database model constructs. Design science research (DSR) is the methodology implemented in the study and it is used to assist in the understanding, design and development of the problem, artefact and solution. This study explores the existing literature on DSR, in addition to structured and unstructured data stores. The literature review formulates the descriptive and prescriptive knowledge used in the development of the artefact. The artefact is developed using a series of six activities derived from two DSR approaches. The problem domain is derived from the existing literature and a real application environment (RAE). The reviewed literature provided a general problem statement. A representative from NFM (the RAE) is interviewed for a situation analysis providing a specific problem statement. An objective is formulated for the development of the artefact and suggestions are made to address the problem domain, assisting the artefact’s objective. The artefact is designed and developed using the descriptive knowledge of structured and unstructured data stores, combined with prescriptive knowledge of algorithms, pseudo code, continuous design and object-oriented design. The artefact evolves through multiple design cycles into a final product that analyses document data stores in terms of relational database model constructs. The artefact is evaluated for acceptability and utility. This provides credibility and rigour to the research in the DSR paradigm. Acceptability is demonstrated through simulation and the utility is evaluated using a real application environment (RAE). A representative from NFM is interviewed for the evaluation of the artefact. Finally, the study is communicated by describing its findings, summarising the artefact and looking into future possibilities for research and application. / MSc (Computer Science), North-West University, Vaal Triangle Campus, 2014

Structured data stores have been the dominating technologies for the past few decades. Although dominating, structured data stores lack the functionality to handle the ‘Big Data’ phenomenon. A new technology has recently emerged which stores unstructured data and can handle the ‘Big Data’ phenomenon. This study describes the development of an artefact to aid in the analysis of NoSQL document data stores in terms of relational database model constructs. Design science research (DSR) is the methodology implemented in the study and it is used to assist in the understanding, design and development of the problem, artefact and solution. This study explores the existing literature on DSR, in addition to structured and unstructured data stores. The literature review formulates the descriptive and prescriptive knowledge used in the development of the artefact. The artefact is developed using a series of six activities derived from two DSR approaches. The problem domain is derived from the existing literature and a real application environment (RAE). The reviewed literature provided a general problem statement. A representative from NFM (the RAE) is interviewed for a situation analysis providing a specific problem statement. An objective is formulated for the development of the artefact and suggestions are made to address the problem domain, assisting the artefact’s objective. The artefact is designed and developed using the descriptive knowledge of structured and unstructured data stores, combined with prescriptive knowledge of algorithms, pseudo code, continuous design and object-oriented design. The artefact evolves through multiple design cycles into a final product that analyses document data stores in terms of relational database model constructs. The artefact is evaluated for acceptability and utility. This provides credibility and rigour to the research in the DSR paradigm. Acceptability is demonstrated through simulation and the utility is evaluated using a real application environment (RAE). A representative from NFM is interviewed for the evaluation of the artefact. Finally, the study is communicated by describing its findings, summarising the artefact and looking into future possibilities for research and application. / MSc (Computer Science), North-West University, Vaal Triangle Campus, 2014

NoSQL databases are becoming increasingly more popular due to their undeniable advantages in the context of storing and processing big data, mainly horizontal scala- bility and the lack of a requirement to define a data schema upfront. In the absence of explicit schema, however, an implicit schema inherent to the stored data still exists and can be inferred. Once inferred, a schema is of great value to the stakeholders and database maintainers. Nevertheless, the problem of schema inference is non-trivial and is still the subject of ongoing research. We explore the many aspects of NoSQL schema inference and data modeling, analyze a number of existing schema inference solutions in terms of their inner workings and capabilities, point out their shortcomings, and devise (1) a novel horizontally scalable approach based on the Apache Spark platform and (2) a new NoSQL Schema metamodel capable of modeling i.a. inter-entity referential relation- ships and deeply nested JSON constructs. We then experimentally evaluate the newly designed approach along with the preexisting solutions with respect to their functional and performance capabilities. 1

Le cloud computing a eu un impact majeur sur la gestion des données, conduisant à une prolifération de nouvelles solutions évolutives de gestion des données telles que le stockage distribué de fichiers et d’objets, les bases de données NoSQL et les frameworks de traitement de données. Cela a conduit également à une grande diversification des interfaces aux SGBD et à la perte d’un paradigme de programmation commun, ce qui rend très difficile pour un utilisateur d’intégrer ses données lorsqu’elles se trouvent dans des sources de données spécialisées, par exemple, relationnelle, document et graphe.Dans cette thèse, nous abordons le problème du traitement de requêtes avec plusieurs sources de données dans le cloud, où ces sources ont des modèles, des langages et des API différents. Cette thèse a été préparée dans le cadre du projet européen CoherentPaaS et, en particulier, du système multistore CloudMdsQL. CloudMdsQL est un langage de requête fonctionnel capable d’exploiter toute la puissance des sources de données locales, en permettant simplement à certaines requêtes natives portant sur les systèmes locauxd’être appelées comme des fonctions et en même temps optimisées, par exemple, en exploitant les prédicats de sélection, en utilisant le bindjoin, en réalisant l’ordonnancement des jointures ou en réduisant les transferts de données intermédiaires.Dans cette thèse, nous proposons une extension de CloudMdsQL pour tirer pleinement parti des fonctionnalités des frameworks de traitement de données sous-jacents tels que Spark en permettant l’utilisation ad hoc des opérateurs de map/filter/reduce (MFR) définis par l’utilisateur en combinaison avec les ordres SQL traditionnels. Cela permet d’effectuer des jointures entre données relationnelles et HDFS. Notre solution permet l’optimisation en permettant la réécriture de sous-requêtes afin de réaliser des optimisations majeures comme le bindjoin ou le filtrage des données le plus tôt possible.Nous avons validé notre solution en implémentant l’extension MFR dans le moteur de requête CloudMdsQL. Sur la base de ce prototype, nous proposons une validation expérimentale du traitement des requêtes multistore dans un cluster pour évaluer l’impact sur les performances de l’optimisation. Plus précisément, nous explorons les avantages de l’utilisation du bindjoin et du filtrage de données dans des conditions différentes. Dans l’ensemble, notre évaluation des performances illustre la capacité du moteur de requête CloudMdsQL à optimiser une requête et à choisir la stratégie d’exécution la plus efficace. / Cloud computing is having a major impact on data management, with a proliferation of new, scalable data management solutions such as distributed file and object storage, NoSQL databases and big data processing frameworks. This also leads to a wide diversification of DBMS interfaces and the loss of a common programming paradigm, making it very hard for a user to integrate its data sitting in specialized data stores, e.g. relational, documents and graph data stores.In this thesis, we address the problem of query processing with multiple cloud data stores, where the data stores have different models, languages and APIs. This thesis has been prepared in the context of the CoherentPaaS European project and, in particular, the CloudMdsQL multistore system. CloudMdsQL is a functional query language able to exploit the full power of local data stores, by simply allowing some local data store native queries to be called as functions, and at the same time be optimized, e.g. by pushing down select predicates, using bind join, performing join ordering, or planning intermediate data shipping.In this thesis, we propose an extension of CloudMdsQL to take full advantage of the functionality of the underlying data processing frameworks such as Spark by allowing the ad-hoc usage of user defined map/filter/reduce (MFR) operators in combination with traditional SQL statements. This allows performing joins between relational and HDFS big data. Our solution allows for optimization by enabling subquery rewriting so that bind join can be used and filter conditions can be pushed down and applied by the data processing framework as early as possible.We validated our solution by implementing the MFR extension as part of the CloudMdsQL query engine. Based on this prototype, we provide an experimental validation of multistore query processing in a cluster to evaluate the impact on performance of optimization. More specifically, we explore the performance benefit of using bind join and select pushdown under different conditions. Overall, our performance evaluation illustrates the CloudMdsQL query engine’s ability to optimize a query and choose the most efficient execution strategy.

This thesis aimed to investigate what Data Stores would fit to be implemented as an Online Feature Store. This is a component in the Machine Learning infrastructure that needs to be able to handle low latency Reads at high throughput with high availability. The thesis evaluated the Data Stores with real feature workloads from Spotify’s Search system. First an investigation was made to find suitable storage systems. NDB and Aerospike were selected because of their state-of-the-art performance together with their suitable functionality. These were then implemented as the Online Feature Store by batch Reading the feature data through a Java program and by using Google Dataflow to input data to the Data Stores. For 1 client NDB achieved about 35% higher batch Read throughput with around 30% lower P99 latency than Aerospike. For 8 clients NDB got 20% higher batch Read throughput, with a varying P99 latency different compared to Aerospike. But in a 8 node setup NDB achieved on average 35% lower latency. Aerospike achieved 50% fasterWrite speeds when writing feature data to the Data Stores. Both Data Stores’ Read performance was found to suffer upon Writing to the data store at the same time as Reading, with the P99 Read latency increasing around 30% for both Data Stores. It was concluded that both Data Stores would work as an Online Feature Store. But NDB achieved better Read performance, which is one of the most important factors for this type of Feature Store. / Den här uppsatsen undersökte vilka datalagringssystem som passar för att implementeras som en Online Feature Store. Detta är en komponent i maskininlärningsinfrastrukturen som måste hantera snabba läsningar med hög genomströmning och hög tillgänglighet. Uppsatsen studerade detta genom att evaluera datalagringssystem med riktig feature data från Spotifys söksystem. En utredning gjordes först för att hitta lovande datalagringssystem för denna uppgift. NDB och Aerospike blev valda på grund av deras topp prestanda och passande funktionalitet. Dessa implementerades sedan som en Online Feature Store genom att batch-läsa feature datan med hjälp av ett Java program samt genom att använda Google Dataflow för att lägga in feature datan i datalagringssystemen. För 1 klient fick NDB runt 35% bättre genomströmning av feature data jämfört med Aerospike för batch läsningar, med ungefär 30% lägre P99 latens. För 8 klienter fick runt 20% högre genomströmning av feature data med en P99 latens som var mer varierande. Men klustren med 8 noder fick NDB i genomsnitt 35% lägre latens. Aerospike var 50% snabbare på att skriva feature datan till datalagringssystemet. Båda systemen led dock av sämre läsprestanda när skrivningar skedde till dem samtidigt. P99 läs-latensen gick då upp runt 30% för båda datalagringssystemen. Sammanfattningsvis funkade båda av de undersökta datalagringssystem som en Online Feature Store. Men NDB hade bättre läsprestanda, vilket är en av de mest viktigaste faktorerna för den här typen av Feature Store.