Infrastructure as Code – Automation of Your Cloud Operations

Infrastructure as Code – Automation of Your Cloud Operations

Azure MVP Matous Rokos and Ken Kaban from PowerON discuss the power of automation and templates for Azure best practice. If you would like to learn more, visit our website: www.poweronplatforms.com
Alternatively, please email us at: info@poweronplatforms.com
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Big Data Analytics with HDInsight: Hadoop on Azure

Big Data Analytics with HDInsight: Hadoop on Azure

Ahttp://hadoop.apache.org/

https://azure.microsoft.com/es-es/services/hdinsight/

Welcome to Apache™ Hadoop®!
What Is Apache Hadoop?
The Apache™ Hadoop® project develops open-source software for reliable, scalable, distributed computing.
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 project includes these modules:
• Hadoop Common: The common utilities that support the other Hadoop modules.
• Hadoop Distributed File System (HDFS™): A distributed file system that provides high-throughput access to application data.
• Hadoop YARN: A framework for job scheduling and cluster resource management.
• Hadoop MapReduce: A YARN-based system for parallel processing of large data sets.
Other Hadoop-related projects at Apache include:
• Ambari™: A web-based tool for provisioning, managing, and monitoring Apache Hadoop clusters which includes support for Hadoop HDFS, Hadoop MapReduce, Hive, HCatalog, HBase, ZooKeeper, Oozie, Pig and Sqoop. Ambari also provides a dashboard for viewing cluster health such as heatmaps and ability to view MapReduce, Pig and Hive applications visually alongwith features to diagnose their performance characteristics in a user-friendly manner.
• Avro™: A data serialization system.
• Cassandra™: A scalable multi-master database with no single points of failure.
• Chukwa™: A data collection system for managing large distributed systems.
• HBase™: A scalable, distributed database that supports structured data storage for large tables.
• Hive™: A data warehouse infrastructure that provides data summarization and ad hoc querying.
• Mahout™: A Scalable machine learning and data mining library.
• Pig™: A high-level data-flow language and execution framework for parallel computation.
• Spark™: A fast and general compute engine for Hadoop data. Spark provides a simple and expressive programming model that supports a wide range of applications, including ETL, machine learning, stream processing, and graph computation.
• Tez™: A generalized data-flow programming framework, built on Hadoop YARN, which provides a powerful and flexible engine to execute an arbitrary DAG of tasks to process data for both batch and interactive use-cases. Tez is being adopted by Hive™, Pig™ and other frameworks in the Hadoop ecosystem, and also by other commercial software (e.g. ETL tools), to replace Hadoop™ MapReduce as the underlying execution engine.
• ZooKeeper™: A high-performance coordination service for distributed applications.
Getting Started
To get started, begin here:
1. Learn about Hadoop by reading the documentation.
2. Download Hadoop from the release page.
3. Discuss Hadoop on the mailing list.
Download Hadoop
Please head to the releases page to download a release of Apache Hadoop.
Who Uses Hadoop?
A wide variety of companies and organizations use Hadoop for both research and production. Users are encouraged to add themselves to the Hadoop PoweredBy wiki page.
News
11 February, 2016: Release 2.6.4 available
A point release for the 2.6 line.
Please see the Hadoop 2.6.4 Release Notes for the list of 46 critical bug fixes and since the previous release 2.6.3.
25 January, 2016: Release 2.7.2 (stable) available
A point release for the 2.7 line.
Please see the Hadoop 2.7.2 Release Notes for the list of 155 bug fixes and patches since the previous release 2.7.1.
17 December, 2015: Release 2.6.3 available
A point release for the 2.6 line.
Please see the Hadoop 2.6.3 Release Notes for the list of 35 critical bug fixes and since the previous release 2.6.2.
28 October, 2015: Release 2.6.2 available
A point release for the 2.6 line.
Please see the Hadoop 2.6.2 Release Notes for the list of 15 critical bug fixes and since the previous release 2.6.1.
23 September, 2015: Release 2.6.1 available
A point release for the 2.6 line.
Please see the Hadoop 2.6.1 Release Notes for the list of 158 critical bug fixes and since the previous release 2.6.0.
06 July, 2015: Release 2.7.1 (stable) available
A point release for the 2.7 line. This release is now considered stable.
Please see the Hadoop 2.7.1 Release Notes for the list of 131 bug fixes and patches since the previous release 2.7.0. Please look at the 2.7.0 section below for the list of enhancements enabled by this first stable release of 2.7.x.
21 April 2015: Release 2.7.0 available
Apache Hadoop 2.7.0 contains a number of significant enhancements. A few of them are noted below.
• IMPORTANT notes
o This release drops support for JDK6 runtime and works with JDK 7+ only.
o This release is not yet ready for production use. Critical issues are being ironed out via testing and downstream adoption. Production users should wait for a 2.7.1/2.7.2 release.
• Hadoop Common
o Support Windows Azure Storage – Blob as a file system in Hadoop.
• Hadoop HDFS
o Support for file truncate
o Support for quotas per storage type
o Support for files with variable-length blocks
• Hadoop YARN
o Make YARN authorization pluggable
o Automatic shared, global caching of YARN localized resources (beta)
• Hadoop MapReduce
o Ability to limit running Map/Reduce tasks of a job
o Speed up FileOutputCommitter for very large jobs with many output files.
Full information about this milestone release is available at Hadoop Releases.
18 November, 2014: release 2.6.0 available
Apache Hadoop 2.6.0 contains a number of significant enhancements such as:
• Hadoop Common
o Key management server (beta)
o Credential provider (beta)
• Hadoop HDFS
o Heterogeneous Storage Tiers – Phase 2
 Application APIs for heterogeneous storage
 SSD storage tier
 Memory as a storage tier (beta)
o Support for Archival Storage
o Transparent data at rest encryption (beta)
o Operating secure DataNode without requiring root access
o Hot swap drive: support add/remove data node volumes without restarting data node (beta)
o AES support for faster wire encryption
• Hadoop YARN
o Support for long running services in YARN
 Service Registry for applications
o Support for rolling upgrades
 Work-preserving restarts of ResourceManager
 Container-preserving restart of NodeManager
o Support node labels during scheduling
o Support for time-based resource reservations in Capacity Scheduler (beta)
o Global, shared cache for application artifacts (beta)
o Support running of applications natively in Docker containers (alpha)
Full information about this milestone release is available at Hadoop Releases.
19 November, 2014: release 2.5.2 available
Full information about this milestone release is available at Hadoop Releases.
12 September, 2014: release 2.5.1 available
Full information about this milestone release is available at Hadoop Releases.
11 August, 2014: release 2.5.0 available
Full information about this milestone release is available at Hadoop Releases.
30 June, 2014: release 2.4.1 available
Full information about this milestone release is available at Hadoop Releases.
27 June, 2014: release 0.23.11 available
Full information about this milestone release is available at Hadoop Releases.
07 April, 2014: release 2.4.0 available
Full information about this milestone release is available at Hadoop Releases.
20 February, 2014: release 2.3.0 available
Full information about this milestone release is available at Hadoop Releases.
11 December, 2013: release 0.23.10 available
Full information about this milestone release is available at Hadoop Releases.
15 October, 2013: release 2.2.0 available
Apache Hadoop 2.x reaches GA milestone! Full information about this milestone release is available at Hadoop Releases.
25 August, 2013: release 2.1.0-beta available
Apache Hadoop 2.x reaches beta milestone! Full information about this milestone release is available at Hadoop Releases.
27 December, 2011: release 1.0.0 available
Hadoop reaches 1.0.0! Full information about this milestone release is available at Hadoop Releases.
March 2011 – Apache Hadoop takes top prize at Media Guardian Innovation Awards
Described by the judging panel as a “Swiss army knife of the 21st century”, Apache Hadoop picked up the innovator of the year award for having the potential to change the face of media innovations.
See The Guardian web site
January 2011 – ZooKeeper Graduates
Hadoop’s ZooKeeper subproject has graduated to become a top-level Apache project.
Apache ZooKeeper can now be found at http://zookeeper.apache.org/
September 2010 – Hive and Pig Graduate
Hadoop’s Hive and Pig subprojects have graduated to become top-level Apache projects.
Apache Hive can now be found at http://hive.apache.org/
Pig can now be found at http://pig.apache.org/
May 2010 – Avro and HBase Graduate
Hadoop’s Avro and HBase subprojects have graduated to become top-level Apache projects.
Apache Avro can now be found at http://avro.apache.org/
Apache HBase can now be found at http://hbase.apache.org/
July 2009 – New Hadoop Subprojects
Hadoop is getting bigger!
• Hadoop Core is renamed Hadoop Common.
• MapReduce and the Hadoop Distributed File System (HDFS) are now separate subprojects.
• Avro and Chukwa are new Hadoop subprojects.
See the summary descriptions for all subprojects above. Visit the individual sites for more detailed information.
March 2009 – ApacheCon EU
In case you missed it…. ApacheCon Europe 2009
November 2008 – ApacheCon US
In case you missed it….
July 2008 – Hadoop Wins Terabyte Sort Benchmark
Hadoop Wins Terabyte Sort Benchmark: One of Yahoo’s Hadoop clusters sorted 1 terabyte of data in 209 seconds, which beat the previous record of 297 seconds in the annual general purpose (Daytona) terabyte sort benchmark. This is the first time that either a Java or an open source program has won.

Hadoop Azure Support: Azure Blob Storage
• Introduction
• Features
• Limitations
• Usage
o Concepts
o Configuring Credentials
o Page Blob Support and Configuration
o Atomic Folder Rename
o Accessing wasb URLs
• Testing the hadoop-azure Module
Introduction
The hadoop-azure module provides support for integration with Azure Blob Storage. The built jar file, named hadoop-azure.jar, also declares transitive dependencies on the additional artifacts it requires, notably the Azure Storage SDK for Java.
Features
• Read and write data stored in an Azure Blob Storage account.
• Present a hierarchical file system view by implementing the standard Hadoop FileSystem interface.
• Supports configuration of multiple Azure Blob Storage accounts.
• Supports both page blobs (suitable for most use cases, such as MapReduce) and block blobs (suitable for continuous write use cases, such as an HBase write-ahead log).
• Reference file system paths using URLs using the wasb scheme.
• Also reference file system paths using URLs with the wasbs scheme for SSL encrypted access.
• Can act as a source of data in a MapReduce job, or a sink.
• Tested on both Linux and Windows.
• Tested at scale.
Limitations
• The append operation is not implemented.
• File owner and group are persisted, but the permissions model is not enforced. Authorization occurs at the level of the entire Azure Blob Storage account.
• File last access time is not tracked.
Usage
Concepts
The Azure Blob Storage data model presents 3 core concepts:
• Storage Account: All access is done through a storage account.
• Container: A container is a grouping of multiple blobs. A storage account may have multiple containers. In Hadoop, an entire file system hierarchy is stored in a single container. It is also possible to configure multiple containers, effectively presenting multiple file systems that can be referenced using distinct URLs.
• Blob: A file of any type and size. In Hadoop, files are stored in blobs. The internal implementation also uses blobs to persist the file system hierarchy and other metadata.

Apache Hadoop 2.7.2
Apache Hadoop 2.7.2 is a minor release in the 2.x.y release line, building upon the previous stable release 2.7.1.
Here is a short overview of the major features and improvements.
• Common
o Authentication improvements when using an HTTP proxy server. This is useful when accessing WebHDFS via a proxy server.
o A new Hadoop metrics sink that allows writing directly to Graphite.
o Specification work related to the Hadoop Compatible Filesystem (HCFS) effort.
• HDFS
o Support for POSIX-style filesystem extended attributes. See the user documentation for more details.
o Using the OfflineImageViewer, clients can now browse an fsimage via the WebHDFS API.
o The NFS gateway received a number of supportability improvements and bug fixes. The Hadoop portmapper is no longer required to run the gateway, and the gateway is now able to reject connections from unprivileged ports.
o The SecondaryNameNode, JournalNode, and DataNode web UIs have been modernized with HTML5 and Javascript.
• YARN
o YARN’s REST APIs now support write/modify operations. Users can submit and kill applications through REST APIs.
o The timeline store in YARN, used for storing generic and application-specific information for applications, supports authentication through Kerberos.
o The Fair Scheduler supports dynamic hierarchical user queues, user queues are created dynamically at runtime under any specified parent-queue.
Getting Started
The Hadoop documentation includes the information you need to get started using Hadoop. Begin with the Single Node Setup which shows you how to set up a single-node Hadoop installation. Then move on to the Cluster Setup to learn how to set up a multi-node Hadoop installation.

NEW MICROSOFT WINDOWS AZURE CONSOLE

New Console Microsoft Windows Azure

Knowing the new console Microsoft Windows Azure services platform Cloud services has had a number of important changes in recent months have inserted new features also have been potentiated services redundancy, replication scenarios IaaS, PaaS.

New console has TAP for new features that are being inserted to the market.

This catch can observe groups have changed availability, the time set, the slideshow design allows a panoramic view of the configuration components of Windows Azure

Improve significant that have been included in Windows Azure.

machines. Support for extensions Chef and puppets, level base prices to calculate the instances the network: General availability of Gateways VPN DynamicRouting and Point-to-Site VPN mobile support preview Visual Studio. net, integration with Active Directory Azure and support Offline;

• Notification hubs: Support for Kindle Fire and integration of server explorer Visual Studio

Auto-estalación: general availability release storage: launch of the general availability of read access Geo redundant storage
programming: version availability General

Automation hear the launch of the new Azure service automation