Real-Time Data Processing With Lambda Architecture

Data has evolved immensely in recent years, in type, volume and velocity. There are several frameworks to handle the big data applications. The project focuses on the Lambda Architecture proposed by Marz and its application to obtain real-time data processing. The architecture is a solution that unites the benefits of the batch and stream processing techniques. Data can be historically processed with high precision and involved algorithms without loss of short-term information, alerts and insights. Lambda Architecture has an ability to serve a wide range of use cases and workloads that withstands hardware and human mistakes. The layered architecture enhances loose coupling and flexibility in the system. This a huge benefit that allows understanding the trade-offs and application of various tools and technologies across the layers. There has been an advancement in the approach of building the LA due to improvements in the underlying tools. The project demonstrates a simplified architecture for the LA that is maintainable

Scalable Architecture for Integrated Batch and Streaming Analysis of Big Data

Thesis (Ph.D.) - Indiana University, Computer Sciences, 2015As Big Data processing problems evolve, many modern applications demonstrate special characteristics. Data exists in the form of both large historical datasets and high-speed real-time streams, and many analysis pipelines require integrated parallel batch processing and stream processing. Despite the large size of the whole dataset, most analyses focus on specific subsets according to certain criteria. Correspondingly, integrated support for efficient queries and post- query analysis is required. To address the system-level requirements brought by such characteristics, this dissertation proposes a scalable architecture for integrated queries, batch analysis, and streaming analysis of Big Data in the cloud. We verify its effectiveness using a representative application domain - social media data analysis - and tackle related research challenges emerging from each module of the architecture by integrating and extending multiple state-of-the-art Big Data storage and processing systems. In the storage layer, we reveal that existing text indexing techniques do not work well for the unique queries of social data, which put constraints on both textual content and social context. To address this issue, we propose a flexible indexing framework over NoSQL databases to support fully customizable index structures, which can embed necessary social context information for efficient queries. The batch analysis module demonstrates that analysis workflows consist of multiple algorithms with different computation and communication patterns, which are suitable for different processing frameworks. To achieve efficient workflows, we build an integrated analysis stack based on YARN, and make novel use of customized indices in developing sophisticated analysis algorithms. In the streaming analysis module, the high-dimensional data representation of social media streams poses special challenges to the problem of parallel stream clustering. Due to the sparsity of the high-dimensional data, traditional synchronization method becomes expensive and severely impacts the scalability of the algorithm. Therefore, we design a novel strategy that broadcasts the incremental changes rather than the whole centroids of the clusters to achieve scalable parallel stream clustering algorithms. Performance tests using real applications show that our solutions for parallel data loading/indexing, queries, analysis tasks, and stream clustering all significantly outperform implementations using current state-of-the-art technologies

A Business Intelligence Solution, based on a Big Data Architecture, for processing and analyzing the World Bank data

The rapid growth in data volume and complexity has needed the adoption of advanced technologies to extract valuable insights for decision-making. This project aims to address this need by developing a comprehensive framework that combines Big Data processing, analytics, and visualization techniques to enable effective analysis of World Bank data. The problem addressed in this study is the need for a scalable and efficient Business Intelligence solution that can handle the vast amounts of data generated by the World Bank. Therefore, a Big Data architecture is implemented on a real use case for the International Bank of Reconstruction and Development. The findings of this project demonstrate the effectiveness of the proposed solution. Through the integration of Apache Spark and Apache Hive, data is processed using Extract, Transform and Load techniques, allowing for efficient data preparation. The use of Apache Kylin enables the construction of a multidimensional model, facilitating fast and interactive queries on the data. Moreover, data visualization techniques are employed to create intuitive and informative visual representations of the analysed data. The key conclusions drawn from this project highlight the advantages of a Big Data-driven Business Intelligence solution in processing and analysing World Bank data. The implemented framework showcases improved scalability, performance, and flexibility compared to traditional approaches. In conclusion, this bachelor thesis presents a Business Intelligence solution based on a Big Data architecture for processing and analysing the World Bank data. The project findings emphasize the importance of scalable and efficient data processing techniques, multidimensional modelling, and data visualization for deriving valuable insights. The application of these techniques contributes to the field by demonstrating the potential of Big Data Business Intelligence solutions in addressing the challenges associated with large-scale data analysis

BDEv 3.0: energy efficiency and microarchitectural characterization of Big Data processing frameworks

This is a post-peer-review, pre-copyedit version of an article published in Future Generation Computer Systems. The final authenticated version is available online at: https://doi.org/10.1016/j.future.2018.04.030[Abstract] As the size of Big Data workloads keeps increasing, the evaluation of distributed frameworks becomes a crucial task in order to identify potential performance bottlenecks that may delay the processing of large datasets. While most of the existing works generally focus only on execution time and resource utilization, analyzing other important metrics is key to fully understanding the behavior of these frameworks. For example, microarchitecture-level events can bring meaningful insights to characterize the interaction between frameworks and hardware. Moreover, energy consumption is also gaining increasing attention as systems scale to thousands of cores. This work discusses the current state of the art in evaluating distributed processing frameworks, while extending our Big Data Evaluator tool (BDEv) to extract energy efficiency and microarchitecture-level metrics from the execution of representative Big Data workloads. An experimental evaluation using BDEv demonstrates its usefulness to bring meaningful information from popular frameworks such as Hadoop, Spark and Flink.Ministerio de Economía, Industria y Competitividad; TIN2016-75845-PMinisterio de Educación; FPU14/02805Ministerio de Educación; FPU15/0338