Hadoop cluster setup by using cloudera manager

Dec 15, 20135 likes1,944 views

1. The document discusses setting up a Hadoop cluster using Cloudera Manager. It outlines the requirements for Cloudera Manager, including supported operating systems, browsers, databases, and Java versions. 2. The process of setting up the Hadoop cluster with Cloudera Manager is described. It involves installing the Cloudera Manager installer, logging into the admin console, specifying hosts, and configuring services. 3. Flume is introduced as a data collection tool that can run independently or on Hadoop clusters. Its important settings - sources, channels, and sinks - are defined along with example types for each.

Hadoop Cluster Setup
A Simple Way by Cloudera Manager
Peng-Yi Lai
Co-graph confidential

Outline
▪ Cloudera Manager – Set Up Your Hadoop

▪ Flume – Data Collection Tool

Co-graph confidential

Before Starting
▪ Ask yourself what do you want!

An expert to make
Hadoop itself better

Provide Service by
Using Hadoop

Co-graph confidential

As a Hadoop Expert

Better to know Hadoop as detail as possible
Companies like Cloudera and MapR
Co-graph confidential

Other Usages on Hadoop
1. Learn how to use
Hadoop to solve
problems more
effectively and
efficiently
2. Find an easiest
way to make sure
your Hadoop can
work properly

Co-graph confidential

Desired Skills
▪ Network knowledge is imperative
▪ Every node in a cluster communicates with each
other through network
▪ Even with cloudera manager, you still need to
handle it on your own

▪ Linux administration
▪ Everyone knows that!!

Co-graph confidential

Requirement for Cloudera
Manager (1)
▪ Prepare Your Machines
▪ Supported OS version
▪ Only 64bit Linux-based

▪ Supported Browsers
▪ For admin console

▪ Supported Database
▪ If you need to use custom database other than embedded PostgreSQL database

▪ Supported JDK version
▪ Cloudera Manager would install it for you if there is no JDK installed

▪ Repositories
▪ All hosts must have to access standard packages repositories and Cloudera
Hadoop repositories

Co-graph confidential

Requirement for Cloudera
Manager (2)
▪ Networking and Security
▪ Properly configuring DNS or /etc/hosts
▪ Everyone should know who’s who

▪ Using root account ro password-less sudo permision ssh
access to all cluster machines
▪ No blocking by iptables or firewalls
▪ 7180 port is used to access Cloudera Manager

▪ No blocking by Security-Enhanced Linux (SELinux)
▪ disabled

▪ There are more details on cloudera.com
▪ If there is a problem, don’t feel ashamed to google!
Co-graph confidential

Set Up a Hadoop Cluster
▪ After everything is done, install clouderamanager-installer.bin from the Cloudera
Downlaods page
▪ Change the permission and install
▪ Login to admin console on http://<Server
host>:7180
▪ Follow the steps by Cloudera Manager
▪ Done!

Co-graph confidential

Cloudera Manager Login

Co-graph confidential

Waiting for Installation

Co-graph confidential

Actions of Services

Co-graph confidential

Configuration of HDFS

Co-graph confidential

Selected Services

Co-graph confidential

Information of a Host

Co-graph confidential

More about Cloudera Manager
▪ Easy to upgrade your CHD version

▪ Easy to add/delete a host and a cluster
▪ Easy to configure High Availability (HA)
▪ Support Hadoop security by using
Kerberos
▪ Support backup and disaster recovery

Co-graph confidential

For Developer
▪ Use Hue (another topic)

Co-graph confidential

Flume
A Data Collection Tool
Co-graph confidential

Two Ways to Use Flume
Independent of Hadoop
cluster
• Flume can totally run by
itself
• Configure flume.conf in
/etc/flume-ng/conf

On cluster of Hadoop
Or a node managed by
Cloudera Manager
• Easy to keep the agent
nodes under control
• Start, Stop, Restart
service on admin console
• Configure flume on admin
console
• Convenient to check log
file

Co-graph confidential

3 Important Settings
Source
• Define what kind of events sent by external source
to accept
Channel
• Define which way to keep the event until it’s
consumed by a Flume sink
Sink
• Define which repository like HDFS or Flume agent
to put/forward the event kept in Channel

Co-graph confidential

Type Example
▪ Source
▪
▪
▪
▪
▪
▪
▪
▪
▪

Avro Source
Exec Source
JMS Source
NetCat Source
Syslog TCP
Source
Syslog UDP
Source
HTTP Source
Thrift Legacy
Source
…etc

▪ Channel
▪ Memory
Channel
▪ JDBC
Channel

▪ File Channel
▪ Pseudo
Transaction
Channel
▪ Custom
Channel

Co-graph confidential

▪ Sink
▪ HDFS Sink
▪ Logger Sink

▪ Avro Sink
▪ Thrift Sink
▪ IRC Sink
▪ File Roll Sink

▪ HBaseSink
▪ …etc

Example of Setting

Co-graph confidential

Use Cloudera Manager

Co-graph confidential

This document discusses the APIs and extensibility features of Cloudera Manager. It provides an overview of the Cloudera Manager API introduced in version 4.0, which allows programmatic access to cluster operations and monitoring data. It also discusses how the API has been used by various customers and partners for tasks like installation/deployment, monitoring, and alerting integration. The document outlines Cloudera Manager's monitoring capabilities using the tsquery language and provides examples. Finally, it covers new service extensibility features introduced in Cloudera Manager 5.

Cloudera cluster setup and configurationSudheer Kondla

The document provides steps for setting up a Hadoop cluster using Cloudera Manager, including downloading and running the Cloudera Manager installer, logging into the Cloudera Manager Admin Console, using Cloudera Manager to automate the installation and configuration of CDH, specifying cluster node and repository information, installing software components on cluster nodes, reviewing installation logs, installing parcels, setting up the cluster and roles, configuring databases and clients, and completing the Cloudera cluster installation process.

Cluster management and automation with cloudera managerChris Westin

Cloudera User Group SF - Cloudera Manager: APIs & ExtensibilityClouderaUserGroups

Configuring Your First Hadoop Cluster On EC2benjaminwootton

YARNAlex Moundalexis

Hadoop Operations for Production Systems (Strata NYC)Kathleen Ting

Hadoop is emerging as the standard for big data processing and analytics. However, as usage of the Hadoop clusters grow, so do the demands of managing and monitoring these systems. In this full-day Strata Hadoop World tutorial, attendees will get an overview of all phases for successfully managing Hadoop clusters, with an emphasis on production systems — from installation, to configuration management, service monitoring, troubleshooting and support integration. We will review tooling capabilities and highlight the ones that have been most helpful to users, and share some of the lessons learned and best practices from users who depend on Hadoop as a business-critical system.

Docker based Hadoop provisioning - Hadoop Summit 2014 Janos Matyas

Big Data in Container; Hadoop Spark in Docker and MesosHeiko Loewe

Multi-tenant, Multi-cluster and Multi-container Apache HBase DeploymentsDataWorks Summit

Why Your Apache Spark Job is FailingCloudera, Inc.

The document discusses Spark job failures and Spark/YARN architecture. It describes a Spark job failure due to a task failing 4 times with a NumberFormatException when parsing a string. It then explains that Spark jobs are divided into stages made up of tasks, and the entire job fails if a stage fails. The document also provides an overview of the Spark and YARN architectures, showing how Spark jobs are submitted to and run via the YARN resource manager.

January 2015 HUG: Using HBase Co-Processors to Build a Distributed, Transacti...Yahoo Developer Network

Monte Zweben Co-Founder and CEO of Splice Machine, will discuss how to use HBase co-processors to build an ANSI-99 SQL database with 1) parallelization of SQL execution plans, 2) ACID transactions with snapshot isolation and 3) consistent secondary indexing. Transactions are critical in traditional RDBMSs because they ensure reliable updates across multiple rows and tables. Most operational applications require transactions, but even analytics systems use transactions to reliably update secondary indexes after a record insert or update. In the Hadoop ecosystem, HBase is a key-value store with real-time updates, but it does not have multi-row, multi-table transactions, secondary indexes or a robust query language like SQL. Combining SQL with a full transactional model over HBase opens a whole new set of OLTP and OLAP use cases for Hadoop that was traditionally reserved for RDBMSs like MySQL or Oracle. However, a transactional HBase system has the advantage of scaling out with commodity servers, leading to a 5x-10x cost savings over traditional databases like MySQL or Oracle. HBase co-processors, introduced in release 0.92, provide a flexible and high-performance framework to extend HBase. In this talk, we show how we used HBase co-processors to support a full ANSI SQL RDBMS without modifying the core HBase source. We will discuss how endpoint transactions are used to serialize SQL execution plans over to regions so that computation is local to where the data is stored. Additionally, we will show how observer co-processors simultaneously support both transactions and secondary indexing. The talk will also discuss how Splice Machine extended the work of Google Percolator, Yahoo Labs’ OMID, and the University of Waterloo on distributed snapshot isolation for transactions. Lastly, performance benchmarks will be provided, including full TPC-C and TPC-H results that show how Hadoop/HBase can be a replacement of traditional RDBMS solutions.

Structor - Automated Building of Virtual Hadoop ClustersOwen O'Malley

This document describes Structor, a tool that automates the creation of virtual Hadoop clusters using Vagrant and Puppet. It allows users to quickly set up development, testing, and demo environments for Hadoop without manual configuration. Structor addresses the difficulties of manually setting up Hadoop clusters, particularly around configuration, security testing, and experimentation. It provides pre-defined profiles that stand up clusters of different sizes on various operating systems with or without security enabled. Puppet modules configure and provision the Hadoop services while Vagrant manages the underlying virtual machines.

MariaDB on DockerMariaDB plc

Using Galera Cluster to Power Geo-distributed Applications on the WANphilip_stoev

This document discusses using Galera Cluster to power geo-distributed applications across wide area networks (WANs). Some key points: - Galera Cluster allows MySQL databases to be replicated across multiple data centers separated by thousands of kilometers to achieve high availability and disaster recovery. - Replicating data closer to applications improves performance by answering most read queries locally with up-to-date data cached in the InnoDB buffer pool. - Galera is optimized for WAN replication with features like encrypting traffic, automatic detection and removal of unreliable nodes, and minimizing cross-data center traffic. - Basic configuration involves specifying network locations, opening required ports, and configuring IPs for each node. Security must

OpenStack London Meetup, 18 Nov 2015Jesse Pretorius

MHA: Getting started & moving past quirks percona live santa clara 2013Colin Charles

MHA (Master High Availability) is a tool for automating MySQL master failover and slave promotion with minimal downtime. It consists of Perl scripts that monitor replication, detect master failures, and promote a slave to become the new master. When a failure occurs, it saves binlogs from the crashed master, identifies the most up-to-date slave, applies differential relay logs to other slaves, promotes a slave to master, and changes other slaves to replicate from the new master - typically in under 30 seconds. MHA is used by many companies to provide highly available MySQL master servers.

Managing Enterprise Hadoop Clusters with Apache AmbariJayush Luniya

The document discusses features of the Apache Ambari platform for managing Hadoop clusters, including: - Ambari allows provisioning, managing, and monitoring Hadoop clusters at scale through features like stacks, blueprints, views, and smart configurations. - Stacks define Hadoop services and components and their lifecycles. Blueprints allow automated deployment of clusters. Views extend the Ambari UI. - Other features discussed include rolling upgrades between stack versions, metrics collection and monitoring, and an alerts framework to notify users of cluster issues.

Cloudera ImpalaAlex Moundalexis

Cloudera Impala provides a fast, ad hoc query capability to Apache Hadoop, complementing traditional MapReduce batch processing. Learn the design choices and architecture behind Impala, and how to use near-ubiquitous SQL to explore your own data at scale. As presented to Portland Big Data User Group on July 23rd 2014. http://www.meetup.com/Hadoop-Portland/events/194930422/

Mesosphere and Contentteam: A New Way to Run CassandraDataStax Academy

Leveraging Docker and CoreOS to provide always available Cassandra at Instacl...DataStax

Instaclustr provides managed Apache Cassandra and DataStax Enterprise clusters in the cloud. They initially ran Cassandra on custom Ubuntu images but moved to CoreOS for its immutable and self-updating capabilities. Using Docker and CoreOS together allows Cassandra to run in immutable Docker containers while CoreOS handles OS-level updates. Integrating Cassandra containers with the CoreOS and systemd init system provides reliable automatic restarts and the ability to notify when Cassandra is ready using dbus inter-process communication. This architecture provides a robust solution for running and updating Cassandra in production clusters.

Do more with Galera Cluster in your OpenStack cloudphilip_stoev

This document discusses Galera, an open-source synchronous multi-master replication plugin for MySQL. It can be used to create highly available, fault-tolerant MySQL clusters. Galera replicates all data changes across nodes simultaneously and supports both single-master and multi-master configurations. It has been used successfully in OpenStack deployments and allows for geo-distributed databases across data centers with minimal latency. The document also outlines the benefits of deploying and managing Galera clusters using tools like Juju.

The State of HBase ReplicationHBaseCon

Speaker: Jean-Daniel Cryans (Cloudera) HBase Replication has come a long way since its inception in HBase 0.89 almost four years ago. Today, master-master and cyclic replication setups are supported; many bug fixes and new features like log compression, per-family peers configuration, and throttling have been added; and a major refactoring has been done. This presentation will recap the work done during the past four years, present a few use cases that are currently in production, and take a look at the roadmap.

Lessons Learned From Running Spark On DockerSpark Summit

Running Spark on Docker containers provides flexibility for data scientists and control for IT. Some key lessons learned include optimizing CPU and memory resources to avoid noisy neighbor problems, managing Docker images efficiently, using network plugins for multi-host connectivity, and addressing storage and security considerations. Performance testing showed Spark on Docker containers can achieve comparable performance to bare metal deployments for large-scale data processing workloads.

MHA (MySQL High Availability): Getting started & moving past quirksColin Charles

MHA (Master High Availability Manager) is a tool for automating MySQL master failover and slave promotion with minimal downtime. It consists of Perl scripts that monitor a replication topology and automatically switch a slave to a master if the current master fails. When a failure is detected, MHA identifies the most up-to-date slave and applies any missing binary logs to make it the new master. It then changes the slaves to replicate from the new master. MHA requires installing node packages on all servers and a manager package to coordinate monitoring and failover.

Building Effective Near-Real-Time Analytics with Spark Streaming and KuduJeremy Beard

This document discusses building near-real-time analytics pipelines using Apache Spark Streaming and Apache Kudu on the Cloudera platform. It defines near-real-time analytics, describes the relevant components of the Cloudera stack (Kafka, Spark, Kudu, Impala), and how they can work together. The document then outlines the typical stages involved in implementing a Spark Streaming to Kudu pipeline, including sourcing from a queue, translating data, deriving storage records, planning mutations, and storing the data. It provides performance considerations and introduces Envelope, a Spark Streaming application on Cloudera Labs that implements these stages through configurable pipelines.

Zero-downtime Hadoop/HBase Cross-datacenter MigrationScott Miao

This document discusses zero-downtime migration of a Hadoop/HBase cluster across datacenters. It begins by outlining the problems with the original IDC, including insufficient network bandwidth and data storage capacity. It then presents the new IDC layout with improved 10Gb networking and 2-3x increased storage. The document details considerations for migrating Hadoop and HBase data separately, including tools to synchronize hot/cold data and pre-split HBase regions. It proposes a process for zero-downtime migration involving building the new cluster, migrating data, and phasing out the old one while services run unchanged on both clusters during the transition. Cooperation from services to port to the new Hadoop version

Productionizing Spark and the Spark Job ServerEvan Chan

You won't find this in many places - an overview of deploying, configuring, and running Apache Spark, including Mesos vs YARN vs Standalone clustering modes, useful config tuning parameters, and other tips from years of using Spark in production. Also, learn about the Spark Job Server and how it can help your organization deploy Spark as a RESTful service, track Spark jobs, and enable fast queries (including SQL!) of cached RDDs.

บทความ Big Data School ใน IMC e-MagazineIMC Institute

Installation and setup hadoop publishedDipendra Kusi

The document provides steps to install Hadoop using a virtual machine and Cloudera quickstart VM, configure Hadoop, and run a sample word count program on Hadoop to count the words in a file. It describes downloading required software, starting the Cloudera VM, checking the Hadoop installation, obtaining sample code and input data, executing the word count jar on Hadoop to process the input file and generate output, and creating a custom word count Java program, compiling it to a JAR file and running it on Hadoop.

More Related Content

What's hot (20)

Big Data in Container; Hadoop Spark in Docker and MesosHeiko Loewe

Multi-tenant, Multi-cluster and Multi-container Apache HBase DeploymentsDataWorks Summit

Why Your Apache Spark Job is FailingCloudera, Inc.

January 2015 HUG: Using HBase Co-Processors to Build a Distributed, Transacti...Yahoo Developer Network

Structor - Automated Building of Virtual Hadoop ClustersOwen O'Malley

MariaDB on DockerMariaDB plc

Using Galera Cluster to Power Geo-distributed Applications on the WANphilip_stoev

OpenStack London Meetup, 18 Nov 2015Jesse Pretorius

MHA: Getting started & moving past quirks percona live santa clara 2013Colin Charles

Managing Enterprise Hadoop Clusters with Apache AmbariJayush Luniya

Cloudera ImpalaAlex Moundalexis

Mesosphere and Contentteam: A New Way to Run CassandraDataStax Academy

Leveraging Docker and CoreOS to provide always available Cassandra at Instacl...DataStax

Do more with Galera Cluster in your OpenStack cloudphilip_stoev

The State of HBase ReplicationHBaseCon

Lessons Learned From Running Spark On DockerSpark Summit

MHA (MySQL High Availability): Getting started & moving past quirksColin Charles

Building Effective Near-Real-Time Analytics with Spark Streaming and KuduJeremy Beard

Zero-downtime Hadoop/HBase Cross-datacenter MigrationScott Miao

Productionizing Spark and the Spark Job ServerEvan Chan

Big Data in Container; Hadoop Spark in Docker and MesosHeiko Loewe

Multi-tenant, Multi-cluster and Multi-container Apache HBase DeploymentsDataWorks Summit

Why Your Apache Spark Job is FailingCloudera, Inc.

January 2015 HUG: Using HBase Co-Processors to Build a Distributed, Transacti...Yahoo Developer Network

Structor - Automated Building of Virtual Hadoop ClustersOwen O'Malley

MariaDB on DockerMariaDB plc

Using Galera Cluster to Power Geo-distributed Applications on the WANphilip_stoev

OpenStack London Meetup, 18 Nov 2015Jesse Pretorius

MHA: Getting started & moving past quirks percona live santa clara 2013Colin Charles

Managing Enterprise Hadoop Clusters with Apache AmbariJayush Luniya

Cloudera ImpalaAlex Moundalexis

Mesosphere and Contentteam: A New Way to Run CassandraDataStax Academy

Leveraging Docker and CoreOS to provide always available Cassandra at Instacl...DataStax

Do more with Galera Cluster in your OpenStack cloudphilip_stoev

The State of HBase ReplicationHBaseCon

Lessons Learned From Running Spark On DockerSpark Summit

MHA (MySQL High Availability): Getting started & moving past quirksColin Charles

Building Effective Near-Real-Time Analytics with Spark Streaming and KuduJeremy Beard

Zero-downtime Hadoop/HBase Cross-datacenter MigrationScott Miao

Productionizing Spark and the Spark Job ServerEvan Chan

Viewers also liked (17)

บทความ Big Data School ใน IMC e-MagazineIMC Institute

Installation and setup hadoop publishedDipendra Kusi

HdfsJaganadh Gopinadhan

קורס אנדרואידNathan Krasney

Introduction to big data Nathan Krasney

This document summarizes an introduction to big data presentation. It defines big data as high volume, velocity, and variety of structured and unstructured data. It provides examples of how companies like Facebook and Target use big data analytics to gain insights into user preferences. The document also discusses technologies like Hadoop, Spark, and NoSQL that help process and analyze large datasets. Finally, it notes that the future is bright for big data due to growing data sources, improved processing abilities, and the ability to extract valuable insights from big data.

Introducing Cloudera Director at Big Data BashAndrei Savu

Introduction to Cloudera's Administrator Training for Apache HadoopCloudera, Inc.

The document provides an overview of Cloudera's Administrator Training course for Apache Hadoop. The training covers topics such as planning and deploying Hadoop clusters, installing and configuring Hadoop components like HDFS, Hive and Impala, using Cloudera Manager for administration, configuring advanced cluster options and HDFS high availability, and Hadoop security. The hands-on course includes exercises for deploying Hadoop clusters, importing data, and troubleshooting issues.

Hadoop OperationsCloudera, Inc.

Hadoop Workshop using Cloudera on Amazon EC2IMC Institute

This document provides instructions for a hands-on workshop on installing and using Hadoop and Cloudera on Amazon EC2. It outlines the steps to launch an EC2 virtual server instance, install Cloudera Manager and Cloudera Express Edition, import and export data from HDFS, write MapReduce programs in Eclipse, and use various Hadoop tools like HDFS and Hue. The workshop is led by Dr. Thanachart Numnonda and aims to teach participants how to set up their own Hadoop cluster on EC2 and start using Hadoop for big data tasks.

Guide "LinkedIn - Tremplin pour l'emploi"LinkedIn France Presse

Hadoop Internals (2.3.0 or later)Emilio Coppa

Big data processing using Cloudera QuickstartIMC Institute

This document outlines a hands-on workshop for using Cloudera Quickstart with a Docker container to process big data. It discusses launching an AWS EC2 instance, installing Docker on Ubuntu, pulling the Cloudera Quickstart Docker image, and using various big data tools including HDFS, Hive, Pig, Impala, Spark, Spark SQL and Spark Streaming. The workshop will use a Cloudera cluster on EC2 consisting of a master node and three worker nodes to demonstrate importing and exporting data to HDFS, writing MapReduce programs, and using tools like Hive.

Deep Dive into Apache Apex App DevelopmentApache Apex

Big Data - The 5 Vs Everyone Must KnowBernard Marr

Introduction to Spark InternalsPietro Michiardi

The document provides an overview of Apache Spark internals and Resilient Distributed Datasets (RDDs). It discusses: - RDDs are Spark's fundamental data structure - they are immutable distributed collections that allow transformations like map and filter to be applied. - RDDs track their lineage or dependency graph to support fault tolerance. Transformations create new RDDs while actions trigger computation. - Operations on RDDs include narrow transformations like map that don't require data shuffling, and wide transformations like join that do require shuffling. - The RDD abstraction allows Spark's scheduler to optimize execution through techniques like pipelining and cache reuse.

Hadoop 2.0 Architecture | HDFS Federation | NameNode High Availability | Edureka!

The document describes the key limitations of Hadoop 1.x including single point of failure of the NameNode, lack of horizontal scalability, and the JobTracker being overburdened. It then discusses how Hadoop 2.0 addresses these issues through features like HDFS federation for multiple NameNodes, NameNode high availability, and YARN which replaces MapReduce and allows sharing of cluster resources for various workloads.

Hadoop introduction , Why and What is Hadoop ?sudhakara st

บทความ Big Data School ใน IMC e-MagazineIMC Institute

Installation and setup hadoop publishedDipendra Kusi

HdfsJaganadh Gopinadhan

קורס אנדרואידNathan Krasney

Introduction to big data Nathan Krasney

Introducing Cloudera Director at Big Data BashAndrei Savu

Introduction to Cloudera's Administrator Training for Apache HadoopCloudera, Inc.

Hadoop OperationsCloudera, Inc.

Hadoop Workshop using Cloudera on Amazon EC2IMC Institute

Guide "LinkedIn - Tremplin pour l'emploi"LinkedIn France Presse

Hadoop Internals (2.3.0 or later)Emilio Coppa

Big data processing using Cloudera QuickstartIMC Institute

Deep Dive into Apache Apex App DevelopmentApache Apex

Big Data - The 5 Vs Everyone Must KnowBernard Marr

Introduction to Spark InternalsPietro Michiardi

Hadoop 2.0 Architecture | HDFS Federation | NameNode High Availability | Edureka!

Hadoop introduction , Why and What is Hadoop ?sudhakara st

Similar to Hadoop cluster setup by using cloudera manager (20)

Webinar: The Future of HadoopCloudera, Inc.

Deployment and Management of Hadoop ClustersAmal G Jose

Hadoop operationsMarc Cluet

Cloudera hadoop installationSumitra Pundlik

The document discusses installing Cloudera Hadoop (CDH 4) on Ubuntu 12.04 LTS. It provides an overview of Hadoop and its components. It then outlines the installation steps for Cloudera Hadoop which include preparing the system by installing prerequisites like OpenSSH, configuring password-less SSH and sudo, editing the host file, installing MySQL and the JDBC connector, and downloading and running the Cloudera Manager installer.

Instant hadoop of your ownJack (Yaakov) Bezalel

Upgrade Without the Headache: Best Practices for Upgrading Hadoop in ProductionCloudera, Inc.

This document discusses best practices for upgrading Hadoop clusters with Cloudera Manager. It describes how the Cloudera Manager upgrade wizard provides a simplified, guided process for upgrading Hadoop distributions with minimal downtime. The upgrade wizard automates many of the manual steps previously required for upgrades and allows rolling upgrades for non-major upgrades when certain conditions are met. Following best practices like testing upgrades in non-production environments and having backup policies in place can help avoid issues during upgrades.

LuisRodriguezLocalDevEnvironmentsDrupalOpenDaysLuis Rodríguez Castromil

This document discusses setting up a local development environment for Drupal. It covers installing and configuring XAMPP, a local web server package, downloading and installing Drupal, and installing useful development tools like Git, Drush, and Sass. XAMPP is used to create a local server for testing Drupal sites without needing a live server. Drupal is downloaded and its installation wizard is used to set up a new Drupal site. Git is installed for version control and Drush provides commands for common Drupal tasks from the command line. Sass is also installed to allow writing CSS in a more reusable, object-oriented way.

Flume-based Independent News AggregatorMário Almeida

This document describes a proof of concept news aggregator service built on Amazon EC2 using Cloudera Flume, Cloudera Manager, and Hadoop Distributed File System. The service collects RSS feeds from various news sources using Flume agents and stores the data in HDFS. It was deployed on EC2 using Cloudera Manager for simplified setup. The document outlines the architecture, implementation details, and issues encountered with scaling and configuring the distributed system in the cloud.

Hadoop Administration Online Training.pdfSpiritsoftsTraining

Enroll for Expert Level Online Hadoop Administration Training By Spiritsofts, Learn Hadoop Admin Training Course with certified Experts, Enroll Today Demo for free & you will find Spiritsofts is the best Online Training Institute within reasonable fee, updated Certification material. Spiritsofts is the best Training Institutes to expand your skills and knowledge. We Provides the best learning Environment. The Training in is every thing we explained based on real time scenarios, it works which we do in companies.

20150425 experimenting with openstack sahara on dockerWei Ting Chen

Setting up a local WordPress EnvironmentChris La Nauze

Intro to Hadoop Presentation at Carnegie Mellon - Silicon Valleymarkgrover

The document provides an introduction to Apache Hadoop and its ecosystem. It discusses how Hadoop addresses the need for scalable data storage and processing to handle large volumes, velocities and varieties of data. Hadoop's two main components are the Hadoop Distributed File System (HDFS) for reliable data storage across commodity hardware, and MapReduce for distributed processing of large datasets in parallel. The document also compares Hadoop to other distributed systems and outlines some of Hadoop's fundamental design principles around data locality, reliability, and throughput over latency.

Ozone - Evolution of hdfs scalabilityDinesh Chitlangia

The document discusses Ozone, which is designed to address HDFS scalability limitations and enable trillions of file system objects. It was created as HDFS struggles with hundreds of millions of files. Ozone uses a microservices architecture of Ozone Manager, Storage Container Managers, and Recon Server to divide responsibilities and scale independently. It provides seamless transition for applications like YARN, MapReduce, Hive and Spark, and supports Kubernetes deployments. The document outlines Ozone's architecture, deployment options, write and read paths, usage similarities to HDFS/S3, enterprise-grade features around security, high availability and roadmap.

Hadoop FuturesSteve Loughran

Tom White presented on the future of Hadoop at a user group meeting. Key goals for Hadoop include modularity, support for multiple languages, and integration with other systems. The Hadoop project was split into core, HDFS, and MapReduce repositories. Upcoming releases include 0.20.1 and 0.21, with 1.0 to establish versioning rules. Interesting projects include using Avro for RPC, distributed configuration, and improving MapReduce performance.

Applications on Hadoopmarkgrover

This document discusses building applications on Hadoop and introduces the Kite SDK. It provides an overview of Hadoop and its components like HDFS and MapReduce. It then discusses that while Hadoop is powerful and flexible, it can be complex and low-level, making application development challenging. The Kite SDK aims to address this by providing higher-level APIs and abstractions to simplify common use cases and allow developers to focus on business logic rather than infrastructure details. It includes modules for data, ETL processing with Morphlines, and tools for working with datasets and jobs. The SDK is open source and supports modular adoption.

Tales from the Cloudera FieldHBaseCon

This document discusses tales from the field of deploying and managing HBase production clusters at Cloudera. It introduces the authors and their backgrounds with HBase. It then covers stories about starting production deployments, fixing bugs, and upgrading clusters. Key lessons include properly sizing clusters, managing regions, fixing issues like hotspots or faulty hardware, and automating upgrades. The goal is to help others avoid problems and have fewer tales of their own through best practices.

Optimizing Dell PowerEdge Configurations for HadoopMike Pittaro

This document discusses optimizing Dell PowerEdge server configurations for Hadoop deployments. It recommends tested server configurations like the PowerEdge R720 and R720XD that provide balanced compute and storage. It also recommends a reference architecture using these servers along with networking best practices and validated software configurations from Cloudera to provide a proven, optimized big data platform.

Clouldera Implementation Guide for Production DeploymentsAhmed Mekawy

Modernize Your Drupal DevelopmentChris Tankersley

The document discusses modernizing Drupal development practices. It covers setting up development environments using local stacks, Docker, Vagrant or cloud-based options like Acquia Dev Desktop. It also discusses using version control like Git, the drush command line tool, coding standards, building modules with testing, caching and assets. The goal is to help developers work more efficiently on teams and produce high-quality, standards-compliant code.

Hadoop and Hive in Enterprisesmarkgrover

Webinar: The Future of HadoopCloudera, Inc.

Deployment and Management of Hadoop ClustersAmal G Jose

Hadoop operationsMarc Cluet

Cloudera hadoop installationSumitra Pundlik

Instant hadoop of your ownJack (Yaakov) Bezalel

Upgrade Without the Headache: Best Practices for Upgrading Hadoop in ProductionCloudera, Inc.

LuisRodriguezLocalDevEnvironmentsDrupalOpenDaysLuis Rodríguez Castromil

Flume-based Independent News AggregatorMário Almeida

Hadoop Administration Online Training.pdfSpiritsoftsTraining

20150425 experimenting with openstack sahara on dockerWei Ting Chen

Setting up a local WordPress EnvironmentChris La Nauze

Intro to Hadoop Presentation at Carnegie Mellon - Silicon Valleymarkgrover

Ozone - Evolution of hdfs scalabilityDinesh Chitlangia

Hadoop FuturesSteve Loughran

Applications on Hadoopmarkgrover

Tales from the Cloudera FieldHBaseCon

Optimizing Dell PowerEdge Configurations for HadoopMike Pittaro

Clouldera Implementation Guide for Production DeploymentsAhmed Mekawy

Modernize Your Drupal DevelopmentChris Tankersley

Hadoop and Hive in Enterprisesmarkgrover

More from Co-graph Inc. (6)

ITベンチャー社長が語る！《新時代の採用戦略！》Co-graph Inc.

HAL東京インターン生業務成果プレゼンCo-graph Inc.

[コグラフ]spss modelerによるデータ加工入門Co-graph Inc.

MongoDB + XSD/XMLCo-graph Inc.

業務システムにおけるMongoDB活用法Co-graph Inc.

Watch Your Log!Co-graph Inc.

This document discusses tools for log data collection, storage, search and visualization. It introduces Fluentd for data collection from sources like Apache, MongoDB and MySQL. Elasticsearch is presented as a distributed storage and search engine that works with JSON documents. Kibana is shown to provide powerful search and visualization of logs stored in Elasticsearch. The document promotes Co-graph's services for building full log collection environments and interacting with collected data.

ITベンチャー社長が語る！《新時代の採用戦略！》Co-graph Inc.

HAL東京インターン生業務成果プレゼンCo-graph Inc.

[コグラフ]spss modelerによるデータ加工入門Co-graph Inc.

MongoDB + XSD/XMLCo-graph Inc.

業務システムにおけるMongoDB活用法Co-graph Inc.

Watch Your Log!Co-graph Inc.

Recently uploaded (20)

ThousandEyes Partner Innovation Updates for May 2025ThousandEyes

How Can I use the AI Hype in my Business Context?Daniel Lehner

𝙄𝙨 𝘼𝙄 𝙟𝙪𝙨𝙩 𝙝𝙮𝙥𝙚? 𝙊𝙧 𝙞𝙨 𝙞𝙩 𝙩𝙝𝙚 𝙜𝙖𝙢𝙚 𝙘𝙝𝙖𝙣𝙜𝙚𝙧 𝙮𝙤𝙪𝙧 𝙗𝙪𝙨𝙞𝙣𝙚𝙨𝙨 𝙣𝙚𝙚𝙙𝙨? Everyone’s talking about AI but is anyone really using it to create real value? Most companies want to leverage AI. Few know 𝗵𝗼𝘄. ✅ What exactly should you ask to find real AI opportunities? ✅ Which AI techniques actually fit your business? ✅ Is your data even ready for AI? If you’re not sure, you’re not alone. This is a condensed version of the slides I presented at a Linkedin webinar for Tecnovy on 28.04.2025.

2025-05-Q4-2024-Investor-Presentation.pptxSamuele Fogagnolo

Automation Dreamin' 2022: Sharing Some Gratitude with Your UsersLynda Kane

Rusty Waters: Elevating Lakehouses Beyond Sparkcarlyakerly1

Spark is a powerhouse for large datasets, but when it comes to smaller data workloads, its overhead can sometimes slow things down. What if you could achieve high performance and efficiency without the need for Spark? At S&P Global Commodity Insights, having a complete view of global energy and commodities markets enables customers to make data-driven decisions with confidence and create long-term, sustainable value. 🌍 Explore delta-rs + CDC and how these open-source innovations power lightweight, high-performance data applications beyond Spark! 🚀

Complete Guide to Advanced Logistics Management Software in Riyadh.pdfSoftware Company

Explore the benefits and features of advanced logistics management software for businesses in Riyadh. This guide delves into the latest technologies, from real-time tracking and route optimization to warehouse management and inventory control, helping businesses streamline their logistics operations and reduce costs. Learn how implementing the right software solution can enhance efficiency, improve customer satisfaction, and provide a competitive edge in the growing logistics sector of Riyadh.

Semantic Cultivators : The Critical Future Role to Enable AIartmondano

The Evolution of Meme Coins A New Era for Digital Currency ppt.pdfAbi john

What is Model Context Protocol(MCP) - The new technology for communication bw...Vishnu Singh Chundawat

The MCP (Model Context Protocol) is a framework designed to manage context and interaction within complex systems. This SlideShare presentation will provide a detailed overview of the MCP Model, its applications, and how it plays a crucial role in improving communication and decision-making in distributed systems. We will explore the key concepts behind the protocol, including the importance of context, data management, and how this model enhances system adaptability and responsiveness. Ideal for software developers, system architects, and IT professionals, this presentation will offer valuable insights into how the MCP Model can streamline workflows, improve efficiency, and create more intuitive systems for a wide range of use cases.

Special Meetup Edition - TDX Bengaluru Meetup #52.pptxshyamraj55

Drupalcamp Finland – Measuring Front-end Energy ConsumptionExove

SAP Modernization: Maximizing the Value of Your SAP S/4HANA Migration.pdfPrecisely

Leading AI Innovation As A Product Manager - Michael JidaelMichael Jidael

Unlike traditional product management, AI product leadership requires new mental models, collaborative approaches, and new measurement frameworks. This presentation breaks down how Product Managers can successfully lead AI Innovation in today's rapidly evolving technology landscape. Drawing from practical experience and industry best practices, I shared frameworks, approaches, and mindset shifts essential for product leaders navigating the unique challenges of AI product development. In this deck, you'll discover: - What AI leadership means for product managers - The fundamental paradigm shift required for AI product development. - A framework for identifying high-value AI opportunities for your products. - How to transition from user stories to AI learning loops and hypothesis-driven development. - The essential AI product management framework for defining, developing, and deploying intelligence. - Technical and business metrics that matter in AI product development. - Strategies for effective collaboration with data science and engineering teams. - Framework for handling AI's probabilistic nature and setting stakeholder expectations. - A real-world case study demonstrating these principles in action. - Practical next steps to begin your AI product leadership journey. This presentation is essential for Product Managers, aspiring PMs, product leaders, innovators, and anyone interested in understanding how to successfully build and manage AI-powered products from idea to impact. The key takeaway is that leading AI products is about creating capabilities (intelligence) that continuously improve and deliver increasing value over time.

Linux Professional Institute LPIC-1 Exam.pdfRHCSA Guru

Automation Hour 1/28/2022: Capture User Feedback from AnywhereLynda Kane

Buckeye Dreamin 2024: Assessing and Resolving Technical DebtLynda Kane

AI and Data Privacy in 2025: Global TrendsInData Labs

In this infographic, we explore how businesses can implement effective governance frameworks to address AI data privacy. Understanding it is crucial for developing effective strategies that ensure compliance, safeguard customer trust, and leverage AI responsibly. Equip yourself with insights that can drive informed decision-making and position your organization for success in the future of data privacy. This infographic contains: -AI and data privacy: Key findings -Statistics on AI data privacy in the today’s world -Tips on how to overcome data privacy challenges -Benefits of AI data security investments. Keep up-to-date on how AI is reshaping privacy standards and what this entails for both individuals and organizations.

Learn the Basics of Agile Development: Your Step-by-Step GuideMarcel David

Mobile App Development Company in Saudi ArabiaSteve Jonas

EmizenTech is a globally recognized software development company, proudly serving businesses since 2013. With over 11+ years of industry experience and a team of 200+ skilled professionals, we have successfully delivered 1200+ projects across various sectors. As a leading Mobile App Development Company In Saudi Arabia we offer end-to-end solutions for iOS, Android, and cross-platform applications. Our apps are known for their user-friendly interfaces, scalability, high performance, and strong security features. We tailor each mobile application to meet the unique needs of different industries, ensuring a seamless user experience. EmizenTech is committed to turning your vision into a powerful digital product that drives growth, innovation, and long-term success in the competitive mobile landscape of Saudi Arabia.

Cyber Awareness overview for 2025 month of securityriccardosl1