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Spark Core is the foundation of the overall project. It provides distributed task dispatching, scheduling, and basic I/O functionalities, exposed through an application programming interface (for Java, Python, Scala, .NET [16] and R) centered on the RDD abstraction (the Java API is available for other JVM languages, but is also usable for some other non-JVM languages that can connect to the ...
In the past, many of the implementations use the Apache Hadoop platform, however today it is primarily focused on Apache Spark. [3] [4] Mahout also provides Java/Scala libraries for common math operations (focused on linear algebra and statistics) and primitive Java collections. Mahout is a work in progress; a number of algorithms have been ...
Cascading is a software abstraction layer for Apache Hadoop and Apache Flink. Cascading is used to create and execute complex data processing workflows on a Hadoop cluster using any JVM-based language (Java, JRuby, Clojure, etc.), hiding the underlying complexity of MapReduce jobs. It is open source and available under the Apache License.
It runs on a single machine, as well as the distributed processing frameworks Apache Hadoop, Apache Spark, Apache Flink, and Dask. [ 9 ] [ 10 ] XGBoost gained much popularity and attention in the mid-2010s as the algorithm of choice for many winning teams of machine learning competitions .
It using the hadoop file system as distributed storage. Tiles: templating framework built to simplify the development of web application user interfaces. Trafodion: Webscale SQL-on-Hadoop solution enabling transactional or operational workloads on Apache Hadoop [11] [12] [13] Tuscany: SCA implementation, also providing other SOA implementations
DataVec vectorizes various file formats and data types using an input/output format system similar to Hadoop's use of MapReduce; that is, it turns various data types into columns of scalars termed vectors. DataVec is designed to vectorize CSVs, images, sound, text, video, and time series.
The company employed contributors to the open source software project Apache Hadoop. [5] The Hortonworks Data Platform (HDP) product, first released in June 2012, [6] included Apache Hadoop and was used for storing, processing, and analyzing large volumes of data. The platform was designed to deal with data from many sources and formats.
A fourth version of the SPARK language, SPARK 2014, based on Ada 2012, was released on April 30, 2014. SPARK 2014 is a complete re-design of the language and supporting verification tools. The SPARK language consists of a well-defined subset of the Ada language that uses contracts to describe the specification of components in a form that is ...