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Hadoop is an open source implementation of the MapReduce Framework in the realm of distributed processing. A Hadoop cluster is a unique type of computational cluster designed for storing and analyzing large data sets across cluster of…
The growing complexity and variety of Big Data platforms makes it both difficult and time consuming for all system users to properly setup and operate the systems. Another challenge is to compare the platforms in order to choose the most…
Apache Hadoop and Spark are gaining prominence in Big Data processing and analytics. Both of them are widely deployed on Internet companies. On the other hand, high-performance data analysis requirements are causing academical and…
The objective of this work was to utilize BigBench [1] as a Big Data benchmark and evaluate and compare two processing engines: MapReduce [2] and Spark [3]. MapReduce is the established engine for processing data on Hadoop. Spark is a…
In this report we investigate the performance of Hadoop clusters, deployed with separated storage and compute layers, on top of a hypervisor managing a single physical host. We have analyzed and evaluated the different Hadoop cluster…
As architecture, systems, and data management communities pay greater attention to innovative big data systems and architectures, the pressure of benchmarking and evaluating these systems rises. Considering the broad use of big data…
Existing benchmarks for analytical database systems such as TPC-DS and TPC-H are designed for static reporting scenarios. The main metric of these benchmarks is the performance of running individual SQL queries over a synthetic database. In…
As the amount of data explodes rapidly, more and more corporations are using data centers to make effective decisions and gain a competitive edge. Data analysis applications play a significant role in data centers, and hence it has became…
Hadoop is emerging as the primary data hub in enterprises, and SQL represents the de facto language for data analysis. This combination has led to the development of a variety of SQL-on-Hadoop systems in use today. While the various…
In April 2023, HEPScore23, the new benchmark based on HEP specific applications, was adopted by WLCG, replacing HEP-SPEC06. As part of the transition to the new benchmark, the CPU corepower published by the sites needed to be compared with…
Nowadays distributed computing environments, large amounts of data are generated from different resources with a high velocity, rendering the data difficult to capture, manage, and process within existing relational databases. Hadoop is a…
Huge amounts of data being generated continuously by digitally interconnected systems of humans, organizations and machines. Data comes in variety of formats including structured, unstructured and semi-structured, what makes it impossible…
As dataset sizes increase, data analysis tasks in high performance computing (HPC) are increasingly dependent on sophisticated dataflows and out-of-core methods for efficient system utilization. In addition, as HPC systems grow, memory…
The advancement in HPC and BDA ecosystem demands a better understanding of the storage systems to plan effective solutions. To make applications access data more efficiently for computation, HPC and BDA ecosystems adopt different storage…
Transitioning cloud-based Hadoop from IaaS to PaaS, which are commercially conceptualized as pay-as-you-go or pay-per-use, often reduces the associated system costs. However, managed Hadoop systems do present a black-box behavior to the…
Data-intensive computing has become one of the major workloads on traditional high-performance computing (HPC) clusters. Currently, deploying data-intensive computing software framework on HPC clusters still faces performance and…
Apache HBase, a mainstay of the emerging Hadoop ecosystem, is a NoSQL key-value and column family hybrid database which, unlike a traditional RDBMS, is intentionally designed to scalably host large, semistructured, and heterogeneous data.…
Different from the traditional benchmarking methodology that creates a new benchmark or proxy for every possible workload, this paper presents a scalable big data benchmarking methodology. Among a wide variety of big data analytics…
Data warehouse architectural choices and optimization techniques are critical to decision support query performance. To facilitate these choices, the performance of the designed data warehouse must be assessed, usually with benchmarks.…
We present in this paper a new benchmark for evaluating the performances of data warehouses. Benchmarking is useful either to system users for comparing the performances of different systems, or to system engineers for testing the effect of…