1.1 What is Spark Shell? The Spark Shell offers interactive command-line environments for Scala and Python users.  SparkR Shell has only been thoroughly tested to work with Spark standalone so far and not all Hadoop distros available, and therefore is not covered here. The Spark Shell is often referred to as REPL (Read/Eval/Print Loop).The Spark Shell session

1.1 What is Apache Spark? Apache Spark (Spark) is a general-purpose processing system for large- scale data. Spark is effective for data processing of up to 100s of terabytes on a cluster of machines. It runs on both Unix-like systems and Windows. Main players behind Spark are Apache Software, Databricks (founded by the creators of Spark), UC

This tutorial is adapted from Web Age course  Applied Data Science with Python. This tutorial provides  quick overview of Python modules and high-power features, NumPy library, pandas library, SciPy library, scikit-learn library, Jupyter notebooks and Anaconda distribution. 1.1 Importing Modules Command Comments from lib import x from lib import x as y Importing a single object

  This tutorial is adapted from Web Age course  Applied Data Science with Python. This tutorial aims at helping you refresh your knowledge of Python and show how Python integrates with NumPy and pandas libraries. Part 1 – Set up the Environment 1. Download and install the latest Google Chrome browser from: https://www.google.com/intl/en/chrome/browser 2. Install Anaconda

In this post, I will demonstrate the usage of the k-means clustering algorithm in R and in Apache Spark.Apache Spark (hereinafter Spark) offers two implementations of k-means algorithm: one is packaged with its MLlib library; the other one exists in Spark’s spark.ml package. While both implementations are currently more or less functionally equivalent, the Spark

In this post we will review the more important aspects related to RDD checkpointing. We will continue working on the over500 RDD we created in the previous post on caching. You will remember that checkpointing is a process of truncating an RDD’s lineage graph and saving its materialized version on a persistence store.

Spark offers developers two simple and quite efficient techniques to improve RDD performance and operations against them: caching and checkpointing. Caching allows you to save a materialized RDD in memory, which greatly improves iterative or multi-pass operations that need to traverse the same data set over and over again (e.g. in machine learning algorithms.)

Last week I completed development of our 2 day class teaching Apache Spark which will be integrated in our Big Data and Data Science classes after the QA cycle. I will be feeding some fragments of the material with additional comments and notes that would help you get a taste of what the new content is all

Spark added support for R back in version 1.4.1. and you can use it in Spark Standalone mode. Big Hadoop distros, like Cloudera’s CDH and Hortonworks’ HDP that bundle Spark, have varying degree of support for R. For the time being, CDH decided to opt out of supporting R (their latest CDH 5.8.x version does