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Author: search.filters.author.Numnonda, ThanisaSubject: search.filters.subject.Lambda architecture

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    A comparative performance of real-time big data analytic architectures
    (2019-07-01)
    Sanla, Apisit
    ;
    Numnonda, Thanisa
    Nowadays, many organizations pay attention to the relevant technologies of Big Data to analyze more accurately, quickly, and efficiently. Real-time Big Data analytics is challenging due to the massive volume of complex data needed to distribute in processing. Therefore, in this research, we investigate two state-of-the-art architectures: Lambda and Kappa. The Kappa architecture is simply the Lambda architecture without the batch layer. To help businesses decide on the right architecture, their processing time, and resource utilization in the same environment need to be found out. Experiments had been carried out with the data size 3 MB, 30 MB, and 300 MB. The results showed that Lambda architecture outperforms Kappa architecture around 9% for the accuracy test when using processing time approximately 2.2 times more than Kappa architecture. Lambda architecture also used more 10-20% of CPU usage and 0.5 GB of RAM usage than Kappa architecture.
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    A real-time recommendation engine using lambda architecture
    (2018-06-01)
    Numnonda, Thanisa
    In a data science theory, the recommended methodology is one of the most popular theories and has been deployed in many real industries. However, one of the most challenging problems these days is how to recommend items with massively streaming data. Therefore, this paper aims to do a real-time recommendation engine using the Lambda architecture. The Apache Hadoop and Apache Spark frameworks were used in this research to process the MovieLens dataset comprised 100 K and 20 M ratings from the GroupLens research. Using alternating least squares (ALS) and k-means algorithms, the top K recommendation movies and the top K trending movies for each user were shown as results. Additionally, the mean squared error (MSE) and within cluster sum of squared error (WCSS) had been computed to evaluate the performance of the ALS and k-means algorithms, sequentially. The results showed that they are acceptable since the MSE and WCSS values are low when comparing to the size of data. However, they can still be improved by tuning some parameters.