MaRe: Processing Big Data with application containers on Apache Spark
نویسندگان
چکیده
منابع مشابه
A comparison on scalability for batch big data processing on Apache Spark and Apache Flink
*Correspondence: [email protected] 1Department of Computer Science and Artificial Intelligence, CITIC-UGR (Research Center on Information and Communications Technology), University of Granada, Calle Periodista Daniel Saucedo Aranda, 18071 Granada, Spain Full list of author information is available at the end of the article Abstract The large amounts of data have created a need for new fram...
متن کاملStatic and Dynamic Big Data Partitioning on Apache Spark
Many of today’s large datasets are organized as a graph. Due to their size it is often infeasible to process these graphs using a single machine. Therefore, many software frameworks and tools have been proposed to process graph on top of distributed infrastructures. This software is often bundled with generic data decomposition strategies that are not optimised for specific algorithms. In this ...
متن کاملTowards Large Scale Environmental Data Processing with Apache Spark
Currently available environmental datasets are either manually constructed by professionals or automatically generated from the observations provided by sensing devices. Usually, the former are modelled and recorded with traditional general-purpose relational technologies, whereas the latter require more specific scientific array formats and tools. Declarative data processing technologies are a...
متن کاملSPARQL query processing with Apache Spark
The number and the size of linked open data graphs keep growing at a fast pace and confronts semantic RDF services with problems characterized as Big data. Distributed query processing is one of them and needs to be efficiently addressed with execution guaranteeing scalability, high availability and fault tolerance. RDF data management systems requiring these properties are rarely built from sc...
متن کاملConquering Big Data with Spark
Today, big and small organizations alike collect huge amounts of data, and they do so with one goal in mind: extract "value" through sophisticated exploratory analysis, and use it as the basis to make decisions as varied as personalized treatment and ad targeting. To address this challenge, we have developed Berkeley Data Analytics Stack (BDAS), an open source data analytics stack for big data ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: GigaScience
سال: 2020
ISSN: 2047-217X
DOI: 10.1093/gigascience/giaa042