background preloader


Facebook Twitter

Hadoop Internals. Big Data Benchmark. Click Here for the previous version of the benchmark Introduction Several analytic frameworks have been announced in the last year. Among them are inexpensive data-warehousing solutions based on traditional Massively Parallel Processor (MPP) architectures (Redshift), systems which impose MPP-like execution engines on top of Hadoop (Impala, HAWQ), and systems which optimize MapReduce to improve performance on analytical workloads (Shark, Stinger/Tez).

In order to provide an environment for comparing these systems, we draw workloads and queries from "A Comparison of Approaches to Large-Scale Data Analysis" by Pavlo et al. (SIGMOD 2009). We have used the software to provide quantitative and qualitative comparisons of five systems: Redshift - a hosted MPP database offered by based on the ParAccel data warehouse.

This remains a work in progress and will evolve to include additional frameworks and new capabilities. What this benchmark is not What is being evaluated? Dataset and Workload. Big Data Benchmark. Migrating to MapReduce 2 on YARN (For Operators) Cloudera Manager lets you add a YARN service in the same way you would add any other Cloudera Manager-managed service.

In Apache Hadoop 2, YARN and MapReduce 2 (MR2) are long-needed upgrades for scheduling, resource management, and execution in Hadoop. At their core, the improvements separate cluster resource management capabilities from MapReduce-specific logic. They enable Hadoop to share resources dynamically between MapReduce and other parallel processing frameworks, such as Cloudera Impala; allow more sensible and finer-grained resource configuration for better cluster utilization; and permit Hadoop to scale to accommodate more and larger jobs. In this post, operators of Cloudera’s distribution of Hadoop and related projects (CDH) who want to upgrade their existing setups to run MR2 on top of YARN will get a guide to the architectural and user-facing differences between MR1 and MR2. (MR2 is the default processing framework in CDH 5, although MR1 will continue to be supported.)

<! --?


Data warehouse augmentation, Part 1: Big data and data warehouse augmentation. Develop and deploy your nextapp on the IBM Bluemixcloud platform. Start building for free This article describes the big data technologies, which are based on Hadoop, that can be implemented to augment existing data warehouses. Traditional data warehouses are built primarily on relational databases that analyze data from the perspective of business processes. Part 1 of this series describes the current state of the data warehouse, its landscape, technology, and architecture. It identifies the technical and business drivers for moving to big data technologies and identifies use cases for augmenting existing data warehouses by incorporating big data technologies. As organizations look for the business value that is hidden within non-structured data, they encounter the challenge of how to analyze complex data. A traditional IT infrastructure is not able to capture, manage, and process big data within a reasonable time.

Traditional data warehouses Data management landscape Figure 1. Figure 2. Introduction to YARN. Develop and deploy your nextapp on the IBM Bluemixcloud platform. Start building for free Introduction Apache Hadoop 2.0 includes YARN, which separates the resource management and processing components. The YARN-based architecture is not constrained to MapReduce. This article describes YARN and its advantages over the previous distributed processing layer in Hadoop.

Learn how to enhance your clusters with YARN's scalability, efficiency, and flexibility. Back to top Apache Hadoop in a nutshell Apache Hadoop is an open source software framework that can be installed on a cluster of commodity machines so the machines can communicate and work together to store and process large amounts of data in a highly distributed manner. MapReduce, a simple programming model popularized by Google, is useful for processing large datasets in a highly parallel and scalable way. Hadoop also provides the software infrastructure for running MapReduce jobs as a series of map and reduce tasks. Hadoop's golden era. Big data architecture and patterns, Part 1: Introduction to big data classification and architecture. Overview Big data can be stored, acquired, processed, and analyzed in many ways. Every big data source has different characteristics, including the frequency, volume, velocity, type, and veracity of the data.

When big data is processed and stored, additional dimensions come into play, such as governance, security, and policies. Choosing an architecture and building an appropriate big data solution is challenging because so many factors have to be considered. This "Big data architecture and patterns" series presents a structured and pattern-based approach to simplify the task of defining an overall big data architecture. Back to top From classifying big data to choosing a big data solution If you've spent any time investigating big data solutions, you know it's no simple task. We begin by looking at types of data described by the term "big data. " Part 1 explains how to classify big data.

Classifying business problems according to big data type Table 1. Figure 1. Smart Data Access with HADOOP  HIVE  “SAP HANA smart data access enables remote data to be accessed as if they are local tables in SAP HANA, without copying the data into SAP HANA. Not only does this capability provide operational and cost benefits, but most importantly it supports the development and deployment of the next generation of analytical applications which require the ability to access, synthesize and integrate data from multiple systems in real-time regardless of where the data is located or what systems are generating it.” Reference: Section 2.4.2 Currently Supported databases by SAP HANA smart data access include: Teradata Database: version 13.0SAPSybase IQ: version 15.4 ESD#3 and 16.0SAP Sybase Adaptive Service Enterprise: version 15.7 ESD#4Intel Distribution for Apache Hadoop: version 2.3 (This includes Apache Hadoop version 1.0.3 and Apache Hive 0.9.0.)

Also Refer to: e.g. SAP HANA Academy | SAP HANA Remote Data Sources: Virtual Tables: e.g. Presto: Interacting with petabytes of data at Facebook. Parquet: Columnar Storage for Hadoop. Optimizing Hive Queries. OpenTSDB - A Distributed, Scalable Monitoring System. Innovations in Apache Hadoop MapReduce Pig Hive for Improving Query... Cloudera Impala: A Modern SQL Engine for Apache Hadoop. Phoenix - SQL over HBase. Presto | Distributed SQL Query Engine for Big Data. How Hadoop Works? HDFS case study. The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage.

Rather than rely on hardware to deliver high-availability, the library itself is designed to detect and handle failures at the application layer, so delivering a highly-available service on top of a cluster of computers, each of which may be prone to failures. The Hadoop library contains two major components HDFS and MapReduce, in this post we will go inside each HDFS part and discover how it works internally. HDFS has a master/slave architecture. HDFS exposes a file system namespace and allows user data to be stored in files. HDFS analysis After the analysis of the Hadoop with JArchitect, here’s the dependency graph of the hdfs project. HDFS use mostly rt, hadoop-common and protobuf libraries. The Hadoop ecosystem: the (welcome) elephant in the room (infographic)

To say Hadoop has become really big business would be to understate the case. At a broad level, it’s the focal point of a immense big data movement, but Hadoop itself is now a software and services market of its very own. In this graphic, we aim to map out the current ecosystem of Hadoop software and services — application and infrastructure software, as well as open source projects — and where those products fall in terms of use cases and delivery model. Click on a company name for more information about how they are using this technology. A couple of points about the methodology might be valuable: The first is that these are products and projects that are built with Hadoop in mind and that aim to either extend its utility in some way or expose its core functions in a new manner.

This is the second installment of our four-part series on the past, present and future of Hadoop. Part I is the history of Hadoop from the people who willed it into existence and took it mainstream. Hama - a general BSP framework on top of Hadoop. Apache Spark™ - Lightning-Fast Cluster Computing. Comparing Pattern Mining on a Billion Records with HP Vertica and Hadoop. Pattern mining can help analysts discover hidden structures in data. Pattern mining has many applications—from retail and marketing to security management. For example, from a supermarket data set, you may be able to predict whether customers who buy Lay’s potato chips are likely to buy a certain brand of beer.

Similarly, from network log data, you may determine groups of Web sites that are visited together or perform event analysis for security enforcement. In this blog post, we will show you how the HP Vertica Analytics Platform can efficiently find frequent patterns in very large data sets. A pattern mining algorithm Frequent patterns are items that occur often in a data set. Instead of describing FP-growth in detail, we list the main steps from a practitioner’s perspective. Create transactions of itemsCount occurrence of item setsSort item sets according to their occurrenceRemove infrequent itemsScan DB and build FP-treeRecursively grow frequent item sets.

Apache Hadoop 2.5.0 - Hadoop in Secure Mode. Common Configurations In order to turn on RPC authentication in hadoop, set the value of property to "kerberos", and set security related settings listed below appropriately. The following properties should be in the core-site.xml of all the nodes in the cluster. Configuration for WebAppProxy The WebAppProxy provides a proxy between the web applications exported by an application and an end user.

If security is enabled it will warn users before accessing a potentially unsafe web application. LinuxContainerExecutor A ContainerExecutor used by YARN framework which define how any container launched and controlled. The following are the available in Hadoop YARN: To build the LinuxContainerExecutor executable run: $ mvn package -Dcontainer-executor.conf.dir=/etc/hadoop/ The path passed in -Dcontainer-executor.conf.dir should be the path on the cluster nodes where a configuration file for the setuid executable should be located.

Conf/container-executor.cfg. Presto | Distributed SQL Query Engine for Big Data. Index - Apache ZooKeeper. Skip to end of metadataGo to start of metadata ZooKeeper: Because coordinating distributed systems is a Zoo ZooKeeper is a centralized service for maintaining configuration information, naming, providing distributed synchronization, and providing group services. All of these kinds of services are used in some form or another by distributed applications. Each time they are implemented there is a lot of work that goes into fixing the bugs and race conditions that are inevitable. Because of the difficulty of implementing these kinds of services, applications initially usually skimp on them ,which make them brittle in the presence of change and difficult to manage.

ZooKeeper aims at distilling the essence of these different services into a very simple interface to a centralized coordination service. We have Java and C interfaces to Zoo Keeper for the applications themselves. Talend | Home. Hadoop, MapReduce and processing large Twitter datasets for fun and profit | Vidal Quevedo. This fall I just enrolled back to complete my PhD at the School of Journalism and Mass Communications (SJMC) at the University of Wisconsin-Madison. As part of my activities, I’ve been attending the sessions of the Social Media and Democracy research group at SJMC, a great collaborative effort to further research in Social Media and how it’s used in political communications.

As part of a series of upcoming research projects on a HUGE Twitter dataset collected SMAD during the US 2012 presidential election, we’ve been brushing up on Python, Hadoop and MapReduce. I’m very excited about this opportunity, as big data analysis seems to be coming of age and gaining traction on in several areas of communication research. As part of our training, Alex Hanna, a sociology PhD student at UW-Madison, put together an excellent series of workshops on Twitter (or, as he’s aptly named them, “Tworkshops“) to get the whole SMAD team started in the art of big data analysis. Be Sociable, Share! RealTime Hadoop Example - Analyse Tweets using Flume, Hadoop and Hive.

Hadoop Tutorial. Apache Hadoop Yahoo! Hadoop Tutorial Table of Contents Welcome to the Yahoo! Hadoop Tutorial. This tutorial includes the following materials designed to teach you how to use the Hadoop distributed data processing environment: Hadoop 0.18.0 distribution (includes full source code) A virtual machine image running Ubuntu Linux and preconfigured with Hadoop VMware Player software to run the virtual machine image A tutorial which will guide you through many aspects of Hadoop's installation and operation. The tutorial is divided into seven modules, designed to be worked through in order.

You can also download this tutorial as a single .zip file and burn a CD for use, and easy distribution, offline. "Hadoop Tutorial from Yahoo! " Follow Yahoo Developer Network on. Think Big » Technologies. Presentations - Apache Hive. Skip to end of metadataGo to start of metadata A list of presentations mainly focused on Hive November 2011 NYC Hive Meetup Presentations June 2012 Hadoop Summit Hive Meetup Presentations February 2013 Hive User Group Meetup June 2013 Hadoop Summit Hive Meetup Presentations Hive Correlation Optimizer (Yin Huai) November 2013 Hive Contributors Meetup Presentations. Analyzing Twitter Data with Apache Hadoop. Social media has gained immense popularity with marketing teams, and Twitter is an effective tool for a company to get people excited about its products.

Twitter makes it easy to engage users and communicate directly with them, and in turn, users can provide word-of-mouth marketing for companies by discussing the products. Given limited resources, and knowing we may not be able to talk to everyone we want to target directly, marketing departments can be more efficient by being selective about whom we reach out to. In this post, we’ll learn how we can use Apache Flume, Apache HDFS, Apache Oozie, and Apache Hive to design an end-to-end data pipeline that will enable us to analyze Twitter data. This will be the first post in a series. The posts to follow to will describe, in more depth, how each component is involved and how the custom code operates. Who is Influential? To understand whom we should target, let’s take a step back and try to understand the mechanics of Twitter.

Some Results. Hadoop Connector — MongoDB Ecosystem 2.2.2.