R for hadoopers

Yahoo migrated most of its Pig workload from MapReduce to Tez to achieve significant performance improvements and resource utilization gains. Some key challenges in the migration included addressing misconfigurations, bad programming practices, and behavioral changes between the frameworks. Yahoo was able to run very large and complex Pig on Tez jobs involving hundreds of vertices and terabytes of data smoothly at scale. Further optimizations are still needed around speculative execution and container reuse to improve utilization even more. The migration to Tez resulted in up to 30% reduction in runtime, memory, and CPU usage for Yahoo's Pig workload.

Apache Drill is the next generation of SQL query engines. It builds on ANSI SQL 2003, and extends it to handle new formats like JSON, Parquet, ORC, and the usual CSV, TSV, XML and other Hadoop formats. Most importantly, it melts away the barriers that have caused databases to become silos of data. It does so by able to handle schema-changes on the fly, enabling a whole new world of self-service and data agility never seen before.

Hadoop became the most common systm to store big data. With Hadoop, many supporting systems emerged to complete the aspects that are missing in Hadoop itself. Together they form a big ecosystem. This presentation covers some of those systems. While not capable to cover too many in one presentation, I tried to focus on the most famous/popular ones and on the most interesting ones.

This document describes how to set up a single-node Hadoop installation to perform MapReduce operations. It discusses supported platforms, required software including Java and SSH, and preparing the Hadoop cluster in either local, pseudo-distributed, or fully-distributed mode. The main components of the MapReduce execution pipeline are explained, including the driver, mapper, reducer, and input/output formats. Finally, a simple word count example MapReduce job is described to demonstrate how it works.

This document provides an agenda for an advanced Spark class covering topics such as RDD fundamentals, Spark runtime architecture, memory and persistence, shuffle operations, and Spark Streaming. The class will be held in March 2015 and include lectures, labs, and Q&A sessions. It notes that some slides may be skipped and asks attendees to keep Q&A low during the class, with a dedicated Q&A period at the end.

R is a very popular platform for Data Science. Apache Spark is a highly scalable data platform. How could we have the best of both worlds? How could a Data Scientist leverage the rich 10000+ packages on CRAN, and integrate Spark into their existing Data Science toolset? SparkR is a new language binding for Apache Spark and it is designed to be familiar to native R users. In this talk we will walkthrough many examples how several new features in Apache Spark 2.x will enable scalable machine learning on Big Data. In addition to talking about the R interface to the ML Pipeline model, we will explore how SparkR support running user code on large scale data in a distributed manner, and give examples on how that could be used to work with your favorite R packages. We will also discuss best practices around using this new feature. We will also look at exciting changes in and coming next in Apache Spark 2.x releases.

Flink provides unified batch and stream processing. It natively supports streaming dataflows, long batch pipelines, machine learning algorithms, and graph analysis through its layered architecture and treatment of all computations as data streams. Flink's optimizer selects efficient execution plans such as shipping strategies and join algorithms. It also caches loop-invariant data to speed up iterative algorithms and graph processing.

PyCascading provides a Python API for the Cascading framework to process data flows on Hadoop. It allows defining data flows as Python functions and operations instead of Java code. The document discusses Hadoop concepts, shows how to define a WordCount workflow in PyCascading with fewer lines of code than Java, and walks through a full example of finding friends' most common interests. Key advantages are using Python instead of Java and leveraging any Python libraries, though performance-critical parts require Java.

Spark Streaming is an extension of the core Spark API that enables continuous data stream processing. It is particularly useful when data needs to be processed in real-time. Carol McDonald, HBase Hadoop Instructor at MapR, will cover: + What is Spark Streaming and what is it used for? + How does Spark Streaming work? + Example code to read, process, and write the processed data

Efficient data access is one of the key factors for having a high performance data processing pipeline. Determining the layout of data values in the filesystem often has fundamental impacts on the performance of data access. In this talk, we will show insights on how data layout affects the performance of data access. We will first explain how modern columnar file formats like Parquet and ORC work and explain how to use them efficiently to store data values. Then, we will present our best practice on how to store datasets, including guidelines on choosing partitioning columns and deciding how to bucket a table.

The NameNode was experiencing high load and instability after being restarted. Graphs showed unknown high load between checkpoints on the NameNode. DataNode logs showed repeated 60000 millisecond timeouts in communication with the NameNode. Thread dumps revealed NameNode server handlers waiting on the same lock, indicating a bottleneck. Source code analysis pointed to repeated block reports from DataNodes to the NameNode as the likely cause of the high load.

This document discusses loading data from Hadoop into Oracle databases using Oracle connectors. It describes how the Oracle Loader for Hadoop and Oracle SQL Connector for HDFS can load data from HDFS into Oracle tables much faster than traditional methods like Sqoop by leveraging parallel processing in Hadoop. The connectors optimize the loading process by automatically partitioning, sorting, and formatting the data into Oracle blocks to achieve high performance loads. Measuring the CPU time needed per gigabyte loaded allows estimating how long full loads will take based on available resources.

LLAP enables sub-second analytical queries in Hive by running query fragments directly in memory on compute nodes using a long-running daemon process. It provides high performance scans and execution through an in-memory columnar cache shared across queries. LLAP queries are coordinated independently by Tez while utilizing Hive operators for processing and Tez for data transfers. It improves upon traditional MapReduce and Tez by keeping intermediate query results in memory rather than writing to disk.

augmented my real-time hadoop talk to include a programming intro to mapreduce for google developer groups

The document discusses scaling HDFS to manage billions of files. It describes how HDFS usage has grown from millions of files in 2007 to potentially billions of files in the future. To address this, the speakers propose storing HDFS metadata in a key-value store like LevelDB instead of solely in memory. They evaluate this approach and find comparable performance to HDFS for most operations. Future work includes improving operations like compaction and failure recovery in the new architecture.

This document provides an overview of the Hadoop MapReduce Fundamentals course. It discusses what Hadoop is, why it is used, common business problems it can address, and companies that use Hadoop. It also outlines the core parts of Hadoop distributions and the Hadoop ecosystem. Additionally, it covers common MapReduce concepts like HDFS, the MapReduce programming model, and Hadoop distributions. The document includes several code examples and screenshots related to Hadoop and MapReduce.

Big Data with Hadoop & Spark Training: http://bit.ly/2L6bZbn This CloudxLab Introduction to Spark Streaming & Apache Kafka tutorial helps you to understand Spark Streaming and Kafka in detail. Below are the topics covered in this tutorial: 1) Spark Streaming - Workflow 2) Use Cases - E-commerce, Real-time Sentiment Analysis & Real-time Fraud Detection 3) Spark Streaming - DStream 4) Word Count Hands-on using Spark Streaming 5) Spark Streaming - Running Locally Vs Running on Cluster 6) Introduction to Apache Kafka 7) Apache Kafka Hands-on on CloudxLab 8) Integrating Spark Streaming & Kafka 9) Spark Streaming & Kafka Hands-on

The document outlines an introduction to analyzing and visualizing geo-data in R. It discusses exploring the structure of spatially distributed point data through point process statistics like the Complete Spatial Randomness test and Ripley's K-function. It also covers visualizing maps and point patterns with packages like maps, ggmap, rworldmap, and ggplot2. The document provides examples of mapping different regions, geocoding location data, and plotting point patterns on maps in R.

In KDD2011, Vijay Narayanan (Yahoo!) and Milind Bhandarkar (Greenplum Labs, EMC) conducted a tutorial on "Modeling with Hadoop". This is the first half of the tutorial.

Abstract: The presentation describes - What is the BigData problem - How Hadoop helps to solve BigData problems - The main principles of the Hadoop architecture as a distributed computational platform - History and definition of the MapReduce computational model - Practical examples of how to write MapReduce programs and run them on Hadoop clusters The talk is targeted to a wide audience of engineers who do not have experience using Hadoop.

This document provides an agenda for an R programming presentation. It includes an introduction to R, commonly used packages and datasets in R, basics of R like data structures and manipulation, looping concepts, data analysis techniques using dplyr and other packages, data visualization using ggplot2, and machine learning algorithms in R. Shortcuts for the R console and IDE are also listed.

Apache Hadoop is an open-source software framework for distributed storage and processing of large datasets across clusters of computers. It provides reliable storage through its distributed file system and scalable processing through its MapReduce programming model. Yahoo! uses Hadoop extensively for applications like log analysis, content optimization, and computational advertising, processing over 6 petabytes of data across 40,000 machines daily.

This document provides a step-by-step guide to learning R. It begins with the basics of R, including downloading and installing R and R Studio, understanding the R environment and basic operations. It then covers R packages, vectors, data frames, scripts, and functions. The second section discusses data handling in R, including importing data from external files like CSV and SAS files, working with datasets, creating new variables, data manipulations, sorting, removing duplicates, and exporting data. The document is intended to guide users through the essential skills needed to work with data in R.

This document summarizes machine learning concepts in Spark. It introduces Spark, its components including SparkContext, Resilient Distributed Datasets (RDDs), and common transformations and actions. Transformations like map, filter, join, and groupByKey are covered. Actions like collect, count, reduce are also discussed. A word count example in Spark using transformations and actions is provided to illustrate how to analyze text data in Spark.

R is a language and environment for statistical computing and graphics. It includes facilities for data manipulation, calculation, graphical display, and programming. Some key features of R include effective data handling, a suite of operators for calculations on arrays and matrices, graphical facilities, and a programming language with conditionals, loops, and functions. Common data structures in R include vectors, matrices, factors, lists, and data frames. Basic operations include arithmetic, logical operations, indexing, subsetting, applying functions, binding, and coercing between different structures.

This document provides an introduction to Apache Spark, a general purpose cluster computing framework. It discusses how Spark improves upon MapReduce by offering better performance, support for iterative algorithms, and an easier developer experience. Spark retains MapReduce's advantages like scalability, fault tolerance, and data locality, but offers more by leveraging distributed memory and supporting directed acyclic graphs of tasks. Examples demonstrate how Spark can run programs up to 100x faster than Hadoop MapReduce and how it supports machine learning algorithms and streaming data analysis.

Hadoop is an open-source software framework for distributed storage and processing of large datasets across clusters of commodity hardware. It uses a simple programming model called MapReduce that automatically parallelizes and distributes work across nodes. Hadoop consists of Hadoop Distributed File System (HDFS) for storage and MapReduce execution engine for processing. HDFS stores data as blocks replicated across nodes for fault tolerance. MapReduce jobs are split into map and reduce tasks that process key-value pairs in parallel. Hadoop is well-suited for large-scale data analytics as it scales to petabytes of data and thousands of machines with commodity hardware.

This document provides an introduction to Apache Spark, including its architecture and programming model. Spark is a cluster computing framework that provides fast, in-memory processing of large datasets across multiple cores and nodes. It improves upon Hadoop MapReduce by allowing iterative algorithms and interactive querying of datasets through its use of resilient distributed datasets (RDDs) that can be cached in memory. RDDs act as immutable distributed collections that can be manipulated using transformations and actions to implement parallel operations.

This document provides an introduction to Hadoop, including its motivation and key components. It discusses the scale of cloud computing that Hadoop addresses, and describes the core Hadoop technologies - the Hadoop Distributed File System (HDFS) and MapReduce framework. It also briefly introduces the Hadoop ecosystem, including other related projects like Pig, HBase, Hive and ZooKeeper. Sample code is walked through to illustrate MapReduce programming. Key aspects of HDFS like fault tolerance, scalability and data reliability are summarized.

Hadoop in Data Warehousing, done as a part of INFO-H-419: Data Warehouses course at the ULB. The report is available at http://goo.gl/gc9Krz

Apache Hadoop has emerged as the storage and processing platform of choice for Big Data. In this tutorial, I will give an overview of Apache Hadoop and its ecosystem, with specific use cases. I will explain the MapReduce programming framework in detail, and outline how it interacts with Hadoop Distributed File System (HDFS). While Hadoop is written in Java, MapReduce applications can be written using a variety of languages using a framework called Hadoop Streaming. I will give several examples of MapReduce applications using Hadoop Streaming.

This document discusses experiences using Hadoop and HBase in the Perf-Log project. It provides an overview of the Perf-Log data format and architecture, describes how Hadoop and HBase were configured, and gives examples of using MapReduce jobs and HBase APIs like Put and Scan to analyze log data. Key aspects covered include matching Hadoop and HBase versions, running MapReduce jobs, using column families in HBase, and filtering Scan results.

This document provides an overview and introduction to using R. It discusses why R is useful, outlines the R interface and workspace, describes how to get help and install packages, and provides tips on resolving conflicting object names. The document is intended to help new users get started with the basic functionality of R.

This document outlines the agenda for a two-day workshop on learning R and analytics. Day 1 will introduce R and cover data input, quality, and exploration. Day 2 will focus on data manipulation, visualization, regression models, and advanced topics. Sessions include lectures and demos in R. The goal is to help attendees learn R in 12 hours and gain an introduction to analytics skills for career opportunities.

Spark is a fast and general engine for large-scale data processing. It improves on MapReduce by allowing iterative algorithms through in-memory caching and by supporting interactive queries. Spark features include in-memory caching, general execution graphs, APIs in multiple languages, and integration with Hadoop. It is faster than MapReduce, supports iterative algorithms needed for machine learning, and enables interactive data analysis through its flexible execution model.

This talk covers the current parallel capabilities in MATLAB*. Learn about its parallel language and distributed and tall arrays. Interact with GPUs both on the desktop and in the cluster. Combine this information into an interesting algorithmic framework for data analysis and simulation.

Big Data with Hadoop & Spark Training: http://bit.ly/2skCodH This CloudxLab Understanding MapReduce tutorial helps you to understand MapReduce in detail. Below are the topics covered in this tutorial: 1) Thinking in Map / Reduce 2) Understanding Unix Pipeline 3) Examples to understand MapReduce 4) Merging 5) Mappers & Reducers 6) Mapper Example 7) Input Split 8) mapper() & reducer() Code 9) Example - Count number of words in a file using MapReduce 10) Example - Compute Max Temperature using MapReduce 11) Hands-on - Count number of words in a file using MapReduce on CloudxLab

In which disk-related failure 
scenarios of Apache Kafka are discussed in unprecedented level of detail

The document discusses lies that architects sometimes tell and truths they avoid. It provides examples of six common lies: 1) saying a system is real-time or has big data when it really has specific requirements, 2) claiming a microservices architecture exists when the goal is still to migrate, 3) saying hybrid/multi-cloud architectures don't exist when the architecture is just copy-pasted, 4) using "best of breed" when really using only one of everything, 5) claiming something can't be done at an organization due to its nature when other similar organizations succeeded, and 6) avoiding risk or change by safely interpreting things in a non-threatening way. The document advocates defining responsibilities clearly, embracing change, taking measured