Mediator Framework for Inserting Data into Hadoop

Citation: Micael Capitão, Óscar Mortágua Pereira, "Mediator Framework for Inserting Data into Hadoop", University of Aveiro, MSc Dissertation, Dec 2014

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Abstract

Data has always been one of the most valuable resources for organizations. With it we can extract information and, with enough information on a subject, we can build knowledge. However, it is first needed to store that data for later processing. On the last decades we have been assisting what was called “information explosion”. With the advent of the new technologies, the volume, velocity and variety of data has increased exponentially, becoming what is known today as big data. Telecommunications operators gather, using network monitoring equipment, millions of network event records, the Call Detail Records (CDRs) and the Event Detail Records (EDRs), commonly known as xDRs. These records are stored and later processed to compute network performance and quality of service metrics. With the ever increasing number of telecommunications subscribers, the volume of generated xDRs needing to be stored and processed has increased exponentially, making the current solutions based on relational databases not suited any more and so, they are facing a big data problem. To handle that problem, many contributions have been made on the last years that have resulted in solid and innovative solutions. Among them, Hadoop and its vast ecosystem stands out. Hadoop integrates new methods of storing and process high volumes of data in a robust and cost-effective way, using commodity hardware.
This dissertation presents a platform that enables the current systems inserting data into relational databases, to keep doing it transparently when migrating those to Hadoop. The platform has to, like in the relational databases, give delivery guarantees, support unique constraints and, be fault tolerant. As proof of concept, the developed platform was integrated with a system specifically designed to the computation of performance and quality of service metrics from xDRs, the Altaia. The performance tests have shown the platform fulfils and exceeds the requirements for the insertion rate of records. During the tests the behaviour of the platform when trying to insert duplicated records and when in failure scenarios have also been evaluated. The results for both situations were as expected.

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Publisher: University of Aveiro