Apache Spark: The Next Gen toolset for Big Data Processing

Boulder/Denver Spark Meetup, 2014-10-02 @ Datalogix http://www.meetup.com/Boulder-Denver-Spark-Meetup/events/207581832/ Apache Spark is intended as a general purpose engine that supports combinations of Batch, Streaming, SQL, ML, Graph, etc., for apps written in Scala, Java, Python, Clojure, R, etc. This talk provides an introduction to Spark — how it provides so much better performance, and why — and then explores how Spark fits into the Big Data landscape — e.g., other systems with which Spark pairs nicely — and why Spark is needed for the work ahead.

http://bit.ly/1BTaXZP – This presentation was given by Marco Vasquez, Data Scientist at MapR, at the Houston Hadoop Meetup

In this one day workshop, we will introduce Spark at a high level context. Spark is fundamentally different than writing MapReduce jobs so no prior Hadoop experience is needed. You will learn how to interact with Spark on the command line and conduct rapid in-memory data analyses. We will then work on writing Spark applications to perform large cluster-based analyses including SQL-like aggregations, machine learning applications, and graph algorithms. The course will be conducted in Python using PySpark.

This document provides an introduction and overview of Apache Spark. It discusses what Spark is, its performance advantages over Hadoop MapReduce, its core abstraction of resilient distributed datasets (RDDs), and how Spark programs are executed. Key features of Spark like its interactive shell, transformations and actions on RDDs, and Spark SQL are explained. Recent new features in Spark like DataFrames, external data sources, and the Tungsten performance optimizer are also covered. The document aims to give attendees an understanding of Spark's capabilities and how it can provide faster performance than Hadoop for certain applications.

Spark was introduced by Apache Software Foundation for speeding up the Hadoop computational computing software process.

This document introduces Apache Spark, an open-source cluster computing system that provides fast, general execution engines for large-scale data processing. It summarizes key Spark concepts including resilient distributed datasets (RDDs) that let users spread data across a cluster, transformations that operate on RDDs, and actions that return values to the driver program. Examples demonstrate how to load data from files, filter and transform it using RDDs, and run Spark programs on a local or cluster environment.

The document discusses Apache Spark, an open source cluster computing framework for real-time data processing. It notes that Spark is up to 100 times faster than Hadoop for in-memory processing and 10 times faster on disk. The main feature of Spark is its in-memory cluster computing capability, which increases processing speeds. Spark runs on a driver-executor model and uses resilient distributed datasets and directed acyclic graphs to process data in parallel across a cluster.

Apache Spark presentation at HasGeek FifthElelephant https://fifthelephant.talkfunnel.com/2015/15-processing-large-data-with-apache-spark Covering Big Data Overview, Spark Overview, Spark Internals and its supported libraries

Introduction to Apache Spark. With an emphasis on the RDD API, Spark SQL (DataFrame and Dataset API) and Spark Streaming. Presented at the Desert Code Camp: http://oct2016.desertcodecamp.com/sessions/all

A presentation cum workshop on Real time Analytics with Apache Kafka and Apache Spark. Apache Kafka is a distributed publish-subscribe messaging while other side Spark Streaming brings Spark's language-integrated API to stream processing, allows to write streaming applications very quickly and easily. It supports both Java and Scala. In this workshop we are going to explore Apache Kafka, Zookeeper and Spark with a Web click streaming example using Spark Streaming. A clickstream is the recording of the parts of the screen a computer user clicks on while web browsing.

Your data is getting bigger while your boss is getting anxious to have insights! This tutorial covers Apache Spark that makes data analytics fast to write and fast to run. Tackle big datasets quickly through a simple API in Python, and learn one programming paradigm in order to deploy interactive, batch, and streaming applications while connecting to data sources incl. HDFS, Hive, JSON, and S3.

Spark Streaming allows processing of live data streams at scale. Recent improvements include: 1) Enhanced fault tolerance through a write-ahead log and replay of unprocessed data on failure. 2) Dynamic backpressure to automatically adjust ingestion rates and ensure stability. 3) Visualization tools for debugging and monitoring streaming jobs. 4) Support for streaming machine learning algorithms and integration with other Spark components.

This document provides a history and market overview of Apache Spark. It discusses the motivation for distributed data processing due to increasing data volumes, velocities and varieties. It then covers brief histories of Google File System, MapReduce, BigTable, and other technologies. Hadoop and MapReduce are explained. Apache Spark is introduced as a faster alternative to MapReduce that keeps data in memory. Competitors like Flink, Tez and Storm are also mentioned.

This document discusses Apache Spark, a fast and general cluster computing system. It summarizes Spark's capabilities for machine learning workflows, including feature preparation, model training, evaluation, and production use. It also outlines new high-level APIs for data science in Spark, including DataFrames, machine learning pipelines, and an R interface, with the goal of making Spark more similar to single-machine libraries like SciKit-Learn. These new APIs are designed to make Spark easier to use for machine learning and interactive data analysis.

http://bit.ly/1BTaXZP – Apache Spark is currently one of the most active projects in the Hadoop ecosystem, and as such, there’s been plenty of hype about it in recent months, but how much of the discussion is marketing spin? And what are the facts? MapR and Databricks, the company that created and led the development of the Spark stack, will cut through the noise to uncover practical advantages for having the full set of Spark technologies at your disposal and reveal the benefits for running Spark on Hadoop This presentation was given at a webinar hosted by Data Science Central and co-presented by MapR + Databricks. To see the webinar, please go to: http://www.datasciencecentral.com/video/let-spark-fly-advantages-and-use-cases-for-spark-on-hadoop