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Apache Spark is an open-source unified analytics engine for large-scale data processing. Spark provides an interface for programming clusters with implicit data parallelism and fault tolerance . Originally developed at the University of California, Berkeley 's AMPLab , the Spark codebase was later donated to the Apache Software Foundation ...
The core of Apache Flink is a distributed streaming data-flow engine written in Java and Scala. [3] [4] Flink executes arbitrary dataflow programs in a data-parallel and pipelined (hence task parallel) manner. [5] Flink's pipelined runtime system enables the execution of bulk/batch and stream processing programs.
sbt, a widely used build tool for Scala projects; Spark Framework is designed to handle, and process big-data and it solely supports Scala; Neo4j is a java spring framework supported by Scala with domain-specific functionality, analytical capabilities, graph algorithms, and many more; Play!, an open-source Web application framework that ...
Apache Kafka is a distributed event store and stream-processing platform. It is an open-source system developed by the Apache Software Foundation written in Java and Scala.The project aims to provide a unified, high-throughput, low-latency platform for handling real-time data feeds.
Databricks, Inc. is a global data, analytics, and artificial intelligence (AI) company, founded in 2013 by the original creators of Apache Spark. [1] [4] The company provides a cloud-based platform to help enterprises build, scale, and govern data and AI, including generative AI and other machine learning models.
Scala has been widely used in Data science, [125] while ClojureScript, [126] Elm [127] or PureScript [128] are some of the functional frontend programming languages used in production. Elixir 's Phoenix framework is also used by some relatively popular commercial projects, such as Font Awesome or Allegro (one of the biggest e-commerce platforms ...
R, a language often used in data mining and statistical data analysis, is now also sometimes used for data wrangling. [6] Data wranglers typically have skills sets within: R or Python, SQL, PHP, Scala, and more languages typically used for analyzing data.
Clustering: Grouping data into unlabeled groups. This is a typical technique used in “unsupervised learning” where there is not established model to rely on. Intel DAAL provides 2 algorithms for clustering: K-Means and “EM for GMM.” Principal Component Analysis (PCA): the most popular algorithm for dimensionality reduction.