The goal of this report and study is to at a theoretical level determine the possi- bilities for Försäkringskassan IT to change platform for storage of data used in their daily activities. Försäkringskassan collects immense amounts of data ev- eryday containing personal information, lines of programming code, payments and customer service tickets. Today, everything is stored in large relationship databases which leads to problems with scalability and performance. The new platform studied in this report is built on a storage technology named Hadoop. Hadoop is developed to store and process data distributed in what is called clus- ters. Clusters that consists of commodity server hardware. The platform promises near linear scalability, possibility to store all data with a high fault tolerance and that it can handle massive amounts of data. The study is done through theo- retical studies as well as a proof of concept. The theory studies focus on the background of Hadoop, it’s structure and what to expect in the future. The plat- form being used at Försäkringskassan today is to be specified and compared to the new platform. A proof of concept will be conducted in a test environment at Försäkringskassan running a Hadoop platform from Hortonworks. Its purpose is to show how storing data is done as well as to show that unstructured data can be stored. The study shows that no theoretical problems have been found and that a move to the new platform should be possible. It does however move handling of the data from before storage to after. This is because todays platform is reliant on relationship databases that require data to be structured neatly to be stored. Hadoop however stores all data but require more work and knowledge to retrieve the data. / Målet med rapporten och undersökningen är att på en teoretisk nivå undersöka möjligheterna för Försäkringskassan IT att byta plattform för lagring av data och information som används i deras dagliga arbete. Försäkringskassan samlar på sig oerhörda mängder data på daglig basis innehållandes allt från personupp- gifter, programkod, utbetalningar och kundtjänstärenden. Idag lagrar man allt detta i stora relationsdatabaser vilket leder till problem med skalbarhet och prestanda. Den nya plattformen som undersöks bygger på en lagringsteknik vid namn Hadoop. Hadoop är utvecklat för att både lagra och processerna data distribuerat över så kallade kluster bestående av billigare serverhårdvara. Plattformen utlovar näst intill linjär skalbarhet, möjlighet att lagra all data med hög feltolerans samt att hantera enorma datamängder. Undersökningen genomförs genom teoristudier och ett proof of concept. Teoristudierna fokuserar på bakgrunden på Hadoop, dess uppbyggnad och struktur samt hur framtiden ser ut. Dagens upplägg för lagring hos Försäkringskassan specificeras och jämförs med den nya plattformen. Ett proof of concept genomförs på en testmiljö hos För- säkringskassan där en Hadoop plattform från Hortonworks används för att påvi- sa hur lagring kan fungera samt att så kallad ostrukturerad data kan lagras. Undersökningen påvisar inga teoretiska problem i att byta till den nya plattformen. Dock identifieras ett behov av att flytta hanteringen av data från inläsning till utläsning. Detta beror på att dagens lösning med relationsdatabaser kräver väl strukturerad data för att kunna lagra den medan Hadoop kan lagra allt utan någon struktur. Däremot kräver Hadoop mer handpåläggning när det kommer till att hämta data och arbeta med den.

Diploma Thesis focuses on integrating Hadoop platform into current data warehouse architecture. In theoretical part, properties of Big Data are described together with their methods and processing models. Hadoop framework, its components and distributions are discussed. Moreover, compoments which enables end users, developers and analytics to access Hadoop cluster are described. Case study of batch data extraction from current data warehouse on Oracle platform with aid of Sqoop tool, their transformation in relational structures of Hive component and uploading them back to the original source is being discussed at practical part of thesis. Compression of data and efficiency of queries depending on various storage formats is also discussed. Quality and consistency of manipulated data is checked during all phases of the process. Fraction of practical part discusses ways of storing and capturing stream data. For this purposes tool Flume is used to capture stream data. Further this data are transformed in Pig tool. Purpose of implementing the process is to move part of data and its processing from current data warehouse to Hadoop cluster. Therefore process of integration of current data warehouse and Hortonworks Data Platform and its components, was designed

Artificial neural networks are fascinating machine learning algorithms. They used to be considered unreliable and computationally very expensive. Now it is known that modern neural networks can be quite useful, but their computational expensiveness unfortunately remains. Statistical boosting is considered to be one of the most important machine learning ideas. It is based on an ensemble of weak models that together create a powerful learning system. The goal of this thesis is the comparison of these machine learning models on three use cases. The first use case deals with modeling the probability of burglary in the city of Chicago. The second use case is the typical example of customer churn prediction in telecommunication industry and the last use case is related to the problematic of the computer vision. The second goal of this thesis is to introduce an open-source machine learning platform called H2O. It includes, among other things, an interface for R and it is designed to run in standalone mode or on Hadoop. The thesis also includes the introduction into an open-source software library Apache Hadoop that allows for distributed processing of big data. Concretely into its open-source distribution Hortonworks Data Platform.