Related papers: Does Big Data Require Complex Systems? A Performan…
Analyzing and working with big data could be very diffi cult using classical means like relational database management systems or desktop software packages for statistics and visualization. Instead, big data requires large clusters with…
Big Data has become prominent throughout many scientific fields and, as a result, scientific communities have sought out Big Data frameworks to accelerate the processing of their increasingly data-intensive pipelines. However, while…
Storage and memory systems for modern data analytics are heavily layered, managing shared persistent data, cached data, and non-shared execution data in separate systems such as distributed file system like HDFS, in-memory file system like…
We are living in the era of Big Data and witnessing the explosion of data. Given that the limitation of CPU and I/O in a single computer, the mainstream approach to scalability is to distribute computations among a large number of…
Objective: To (1) demonstrate the implementation of a data science platform built on open-source technology within a large, academic healthcare system and (2) describe two computational healthcare applications built on such a platform.…
Modern distributed data processing systems struggle to balance performance, maintainability, and developer productivity when integrating machine learning at scale. These challenges intensify in large collaborative environments due to high…
This chapter introduces the state-of-the-art in the emerging area of combining High Performance Computing (HPC) with Big Data Analysis. To understand the new area, the chapter first surveys the existing approaches to integrating HPC with…
Modern big data systems run on cloud environments where resources are shared amongst several users and applications. As a result, declarative user queries in these environments need to be optimized and executed over resources that…
Context: The efficient processing of Big Data is a challenging task for SQL and NoSQL Databases, where competent software architecture plays a vital role. The SQL Databases are designed for structuring data and supporting vertical…
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…
The Big Data management is a problem right now. The Big Data growth is very high. It is very difficult to manage due to various characteristics. This manuscript focuses on Big Data analytics in cloud environment using Hadoop. We have…
Data lakes have emerged as a flexible and scalable solution for storing and analyzing large volumes of heterogeneous data, including structured, semi-structured, and unstructured formats. Despite their growing adoption in both industry and…
Enterprises operate large data lakes using Hadoop and Spark frameworks that (1) run a plethora of tools to automate powerful data preparation/transformation pipelines, (2) run on shared, large clusters to (3) perform many different…
Now we live in an era of big data, and big data applications are becoming more and more pervasive. How to benchmark data center computer systems running big data applications (in short big data systems) is a hot topic. In this paper, we…
Big data analytics frameworks, such as Spark and Giraph, need to process and cache massive amounts of data that do not always fit on the heap. Therefore, frameworks temporarily move long-lived objects outside the managed heap (off-heap) on…
In the past few years, neuroimaging has entered the Big Data era due to the joint increase in image resolution, data sharing, and study sizes. However, no particular Big Data engines have emerged in this field, and several alternatives…
Data frames in scripting languages are essential abstractions for processing structured data. However, existing data frame solutions are either not distributed (e.g., Pandas in Python) and therefore have limited scalability, or they are not…
Big Data is considered proprietary asset of companies, organizations, and even nations. Turning big data into real treasure requires the support of big data systems. A variety of commercial and open source products have been unleashed for…
English. This document is designed to study the data structures that can be used in the Apache Spark framework and to evaluate the best performing ones to implement solutions, in particular we will evaluate advantages / disadvantages…
Configuration space complexity makes the big-data software systems hard to configure well. Consider Hadoop, with over nine hundred parameters, developers often just use the default configurations provided with Hadoop distributions. The…