Introduction to SparkR | Big Data Hadoop Spark Tutorial | CloudxLab

1. Introduction to SparkR 2. Demo Starting to use SparkR DataFrames: dplyr style, SQL style RDD v.s. DataFrames SparkR on MLlib: GLM, K-means 3. User Case Median: approxQuantile() ID Match: dplyr style, SQL style, SparkR function SparkR + Shiny 4. The Future of SparkR

This document provides an overview of Apache Spark, including why it was created, how it works, and how to get started with it. Some key points: - Spark was initially developed at UC Berkeley as a class project in 2009 to test cluster management systems like Mesos, and was later open sourced in 2010. It became an Apache project in 2014. - Spark is faster than Hadoop for machine learning tasks because it keeps data in-memory between jobs rather than writing to disk, and has a smaller codebase. - The basic unit of data in Spark is the resilient distributed dataset (RDD), which allows immutable, distributed collections across a cluster. RDDs support transformations and actions. -

Watch video at: http://youtu.be/Wg2boMqLjCg Want to learn how to write faster and more efficient programs for Apache Spark? Two Spark experts from Databricks, Vida Ha and Holden Karau, provide some performance tuning and testing tips for your Spark applications

Big Data with Hadoop & Spark Training: http://bit.ly/2kyRTuW This CloudxLab Advanced Spark Programming tutorial helps you to understand Advanced Spark Programming in detail. Below are the topics covered in this slide: 1) Shared Variables - Accumulators & Broadcast Variables 2) Accumulators and Fault Tolerance 3) Custom Accumulators - Version 1.x & Version 2.x 4) Examples of Broadcast Variables 5) Key Performance Considerations - Level of Parallelism 6) Serialization Format - Kryo 7) Memory Management 8) Hardware Provisioning

This document demonstrates how to use Scala and Spark to analyze text data from the Bible. It shows how to install Scala and Spark, load a text file of the Bible into a Spark RDD, perform searches to count verses containing words like "God" and "Love", and calculate statistics on the data like the total number of words and unique words used in the Bible. Example commands and outputs are provided.

This document provides an introduction to big data and Hadoop. It defines big data as massive amounts of structured and unstructured data that is too large for traditional databases to handle. Hadoop is an open-source framework for storing and processing big data across clusters of commodity hardware. Key components of Hadoop include HDFS for storage, MapReduce for parallel processing, and an ecosystem of tools like Hive, Pig, and Spark. The document outlines the architecture of Hadoop, including the roles of the master node, slave nodes, and clients. It also explains concepts like rack awareness, MapReduce jobs, and how files are stored in HDFS in blocks across nodes.

This document discusses Scala and big data technologies. It provides an overview of Scala libraries for working with Hadoop and MapReduce, including Scalding which provides a Scala DSL for Cascading. It also covers Spark, a cluster computing framework that operates on distributed datasets in memory for faster performance. Additional Scala projects for data analysis using functional programming approaches on Hadoop are also mentioned.

This document discusses adding complex data to the Spark stack using Apache Drill. It provides an overview of Drill, how to integrate it with Spark, the current status and next steps. Drill allows SQL-based querying of structured, semi-structured and unstructured data across various data sources. It can be used as an input to Spark jobs and to query Spark RDDs. The integration provides benefits like flexibility, rich storage support and efficient distributed processing while combining Drill and Spark's capabilities.

Spark Streaming provides fault-tolerant stream processing capabilities to Spark. To achieve fault-tolerance and exactly-once processing semantics in production, Spark Streaming uses checkpointing to recover from driver failures and write-ahead logging to recover processed data from executor failures. The key aspects required are configuring automatic driver restart, periodically saving streaming application state to a fault-tolerant storage system using checkpointing, and synchronously writing received data batches to storage using write-ahead logging to allow recovery after failures.

The document discusses Spark's DataFrame API and the Tungsten project. DataFrames make Spark accessible to different users by providing a common API across languages like Python, R and Scala. Tungsten aims to improve Spark's performance for the next five years through techniques like runtime code generation and off-heap memory management. Initial results show Tungsten doubling performance. Together, DataFrames and Tungsten will help Spark scale to larger data and queries across different languages and execution backends.

This document discusses scalable machine learning techniques. It summarizes Spark MLlib, which provides machine learning algorithms that can run on large datasets in a distributed manner using Apache Spark. It also discusses H2O, which provides fast machine learning algorithms that can integrate with Spark via Sparkling Water to allow transparent use of H2O models and algorithms with the Spark API. Examples of using K-means clustering and logistic regression are provided to illustrate MLlib and H2O.

This document provides an overview of big data analytics with Scala, including common frameworks and techniques. It discusses Lambda architecture, MapReduce, word counting examples, Scalding for batch and streaming jobs, Apache Storm, Trident, SummingBird for unified batch and streaming, and Apache Spark for fast cluster computing with resilient distributed datasets. It also covers clustering with Mahout, streaming word counting, and analytics platforms that combine batch and stream processing.