text:
Arenas Del Mar Beach & Spa - Adults only(4 stars)Avd.europa 2, 38612 Medano View on map
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Hotel review overview
The most helpful positive review
read more Excellent, Very good reviews
If you want to relax this hotel is very recommendable, it has everything you need including Spa. The service was very good and not pushy. In the evening there was additionally shows. … read hotel review
The most helpful negative review
read more Acceptable, Poor, Terrible reviews
From the outside, a ugly Concrete block and a jail inside as well as laid outfive floors., rooms at the cube architecture outside and inside opened the Stairwell. Just as, as an American prison... Just awful. We were luckily only 1 night on a stopover here, that was enough but absolutely. The room itself was okay, but the rest ☹ I refused the first time with the breakfast the coffee, which is saying something. In this regard, I would definitely not vacati … read hotel review
Nevertheless, i am recovered.
- Recommended hotel
- Couples
- Translated
- Proof of booking
- Beach holiday
- February 2014
The hotel is 90% english tourists. We still felt comfortable. Unfortunately the hotel was to be used only against surcharge. read hotel review
Anja Petra
This hotel we are not more in question.
- Hotel not recommended
- Couples
- Translated
- Proof of booking
- Beach holiday
- December 2013
At the property they wanted to place us in a room which booked halbseitigen sea view, although we booked sea view had, after long back and forth we had the ocean view rooms also get. It was very spacious and a great balcony. Cleanliness in our room left something to be desired. Were thursdays and sundays i asked the towels, Bathroom clean and change linen and the sand from balcony way to sweep. Was immediately dealt with, but the days after come back to. The bar in the hotel was only from 16° ° open since we had half board. ... The prices for drinks were too expensive. We were evenings always in the place very varied and nice people. To foot about 20 minutes. The entertainment they had was rather something for older guests. In large and whole this hotel made a dirty impression on us. read hotel review
Arenas del mar - again had a nice time
- Recommended hotel
- Friends
- Translated
- Other stay
- November 2013
2 weeks with lots of relaxation. Directly on the sea, long beach to walk and bathe in the sea. The pool was also good and busy. But still enough beach chairs. Good, varied food. Variety for breakfast. Breakfast fresh from the grill meat and fish. Vegetables, salads, fruit full lunch and dinner. Spacious rooms and wonderful view of the sea. Staff attentive and really friendly. Also at the reception german was spoken, so there was also for people who do not speak spanish always council, assistance or simply a nice word. El medano is a nice small place. Many restaurants ..., small shops, 2 beaches. Nightlife is not so - and the hotel is about 20 minutes' walk. But there was also taxi for very little money. Negative was only sometimes the strong wind. Conclusion: this was the 4. stay at arenas del mar. And we'll be back. read hotel review
Nice, clean hotel with a magnificent view!
- Recommended hotel
- Couples
- Translated
- Other stay
- June 2013
Overall the hotel was very satisfactory! The staff was very friendly and helpful. The location of the hotel is very pleasant, since one somewhat outside is, to foot in 15 minutes at el médano is. Because the hotel over 18 hotel is of course in addition still something more 'peace', this was very pleasant. The food in the hotel was very english, but always good and varied. If you want to have lunch or dinner withing walking distance because trip or co. You could order a packed lunch, which always very tasty was. The rooms themselves were very spacious, clean ... and bright. The only minor problem was and, that the hotel respectively the rooms very poorly soundproofed are. All in all a successful vacation, very good for relaxation and as a starting point for desk. read hotel review
- Recommended hotel
- Couples
- Translated
- Proof of booking
- Beach holiday
- May 2013
Time and again, with pleasure, aircraft noise, but it didn't bother us. Staff very friendly and corteous Room amazing, large and bright. read hotel review
Information about Arenas Del Mar Beach & Spa - Adults only
Hotel Information
hotel.de *
This new hotel complex is enhanced just near La Jaquita Beach and will satisfy the tastes of our most demanding customers, achieving a perfect combination of modernity and relax. Among the aspects we must emphasize, is the proximity and orientation of each element towards the sea and also modern facilities and SPA services, as well as calm and entertainment in the zone. All our spacious rooms are well-equiped, thus allowing them a stay with all amenities you need, offering you Junior Suites with sea or mountain view. Business hours of reception: Every day: from 00:00 to 00:00
* Neither HolidayCheck nor the provider assumes liability for the accuracy and completeness of the content displayed above. Please take note of the hotel description of the respective operator and bear in mind that the services included in an offer may differ from the catalog description presented here. We are obliged to offer solely those services available at the time of booking as shown on the booking form. © 2013 Giata
Hotel amenities
- Terrace
- Meeting/Banquet Facilities
- Non-smoking room
- Hairdressers/Beauty salon
- Playground
- Cash machine on site
- Hotel safe
- Phone
- Lift
- Air conditioning
- Free parking
- Car park
Room amenities
- Shower
- Writing bureau
- Additional childrens' bed
- Phone
- Minibar
- Safe
- Balcony
- Terrace
- Bathroom
- Electronic room keys
- Wake up call/Alarm clock
- Fire alarm
- Restaurant
- Vegetarian
- Hotel bar
- Cafe
A choice of sports
- Fitness centre
- Outdoor pool
Spa offers
- Spa centre
- Massages
- Special massages
- Sauna
- Steam room/ turkish bath
- Whirlpool
Other hotel information
- City hotel
- 24 hour reception
- Evening programs and entertainment
- Express check in and check out
- Eurocard/Mastercard
- Car hire
- Ticket service
- Information desk
- Currency exchange
- All public and private areas are no smoking zones
- Wifi
- Free Wifi access included
- Newspaper
- Laundry service
Nearby - Hotels and Things to do
- Hotels
- Things to do & See


text:
See other San Juan Del Sur properties
Corck's Beach hostel & Irish Pub
Calle Costera, across from Meson Espanol and the beach, San Juan Del Sur, Nicaragua, Distance from city centre: 0.3km
Real Reviews
Real reviews from fellow travellers
These are trusted reviews from guests who have actually stayed here. We hope you find them helpful.
Note: we only give a property a rating once it has received a minimum of 3 reviews.
- Page:1/1
- " the first day there the cleaning lady as bit by a scorpion, non lethal se was treated at the hospital and returned to finish her day. At night the owner a spanish woman would play Led Zepplin music and get drunk,this would go on till 11pm. then it would tone down. There was one incident where two nico queens from Minauga, tried to seduce a British surfer, while in his sleep. They were chastised and left the following day.they tried to pick me up but I said take a walk on the wild side.It is peop "
- 02 Oct 2012 John
- " Love the place on the beach! "
- 18 Jun 2012 Anonymous
- " It was a great place to stay right by the beach just out of range of the sound of the bars but just close enough for a nice stumble home. And you can't go wrong with two for one margaritas! "
- 01 Apr 2012 Theophilus

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Sherlock Holmes - The Best Kept Secret
An original theatrical thriller full of murder, intrigue and danger arrives on stage this summer. Sherlock Holmes, the World's Greatest Detective embarks on a gripping new case.
The legendary occupant of 221b Baker Street faces his greatest challenge ever in a mystery spiked with such an emotional charge that Sherlock's faith in logic seems lost to an impenetrable world of magic.
Two years after the Reichenbach Falls, Sherlock Holmes has lost his hunger. Refusing to take on any new cases he's listless, bankrupt and reduced to selling the stories of his past glories. But when his brother Mycroft is arrested for treason, Holmes is jolted back into action along with Watson and the mysterious and beautiful Irene Adler.
With demons of his own to fight and unsettled by feelings for the captivating Miss Adler, he must battle unseen forces in a race to save Mycroft's life. Has the deadly puzzle of The Best Kept Secret finally defeated the World's Greatest Detective, or can Sherlock win the race against time and save them all as well as himself?
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Venue Facts
Venue: New Theatre
Website: newtheatrecardiff.co.uk
Phone: 02920 878 889
→ Cardiff Queen Street 5 min walk


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Bill Daley: My brother Rich lost weight on vegetarian diet in China
By ABDON M. PALLASCH Political Reporter firstname.lastname@example.com April 25, 2012 12:54PM
Related Stories
Updated: April 25, 2012 1:55PM
A couple weeks of vegetarian diet in China has been good for former Mayor Richard M. Daley, his brother Bill said Wednesday.
"We had dinner with him last night for his birthday," Bill Daley said. "He has lost some weight. He went to China for two weeks, got on a vegetarian diet — that's what you eat when you're there."
The former mayor left Wednesday for a three-day Coca-Cola shareholders meeting in Atlanta, his brother said. Daley now serves on the board of Coke in addition to working at the Chicago firm of Katten Muchin.
Will Bill Daley be following the family call to run for office?
Without ruling it out, Daley said, "I have no plans whatsoever right now to do anything public. I've talked to a bunch of people in financial services and other things. I'm talking to some New York and Chicago operations. I decided when I left the White House I would try to take the summer off."
Daley quipped: "I'm loafing, I'm unemployed. I haven't gone to the unemployment office."
Daley is a vice-chair — along with dozens of others — of President Barack Obama's re-election campaign. But, he said with a laugh, "I'm waiting for the meeting. They haven't set up a meeting yet."
Would his brother's successor, Rahm Emanuel, make a good candidate for president?
Daley smiled and said, "So far, after a year, he's done a very good job [as mayor.]"


text:
I think it was Tina Fey who said that what places like the Container Store are really selling is hope. I completely agree – the promise of a more organized life is so optimistic and uplifting. To that end, I've scoured the web for 25 fresh storage tips and tricks that are easy to execute and promise a dose of satisfaction and a sense of peace in your home. So go ahead and check out 25 Totally Clever Storage Tips and Tricks and get inspired!
TAGS: Organization, Prudent Home
One comment
the CONTAINER STORE?! how come nobody ever told me about the container store until just NOW?! IM going to go write a nasty letter to everyone i know for holding out on me!
but seriously…


text:
Apache Hadoop
|Apache Software Foundation
|Initial release
|December 10, 2011[1]
|Stable release
|2.6.0 / November 18, 2014[2]
|Development status
|Written in
|Operating system
|Distributed file system
|Apache License 2.0
Apache Hadoop is an open-source software framework for distributed storage and distributed processing of Big Data on clusters of commodity hardware. Its Hadoop Distributed File System (HDFS) splits files into large blocks (default 64MB or 128MB) and distributes the blocks amongst the nodes in the cluster. For processing the data, the Hadoop Map/Reduce ships code (specifically Jar files) to the nodes that have the required data, and the nodes then process the data in parallel. This approach takes advantage of data locality,[3] in contrast to conventional HPC architecture which usually relies on a parallel file system (compute and data separated, but connected with high-speed networking).[4]
Since 2012,[5] the term "Hadoop" often refers not to just the base Hadoop package but rather to the Hadoop Ecosystem, which includes all of the additional software packages that can be installed on top of or alongside Hadoop, such as Apache Pig, Apache Hive, Apache HBase, Apache Spark, and others.[6][7]
The base Apache Hadoop framework is composed of the following modules:
- Hadoop Common – contains libraries and utilities needed by other Hadoop modules.
- Hadoop Distributed File System (HDFS) – a distributed file-system that stores data on commodity machines, providing very high aggregate bandwidth across the cluster.
- Hadoop YARN – a resource-management platform responsible for managing compute resources in clusters and using them for scheduling of users' applications.
- Hadoop MapReduce – a programming model for large scale data processing.
All the modules in Hadoop are designed with a fundamental assumption that hardware failures (of individual machines, or racks of machines) are common and thus should be automatically handled in software by the framework. Apache Hadoop's MapReduce and HDFS components originally derived respectively from Google's MapReduce and Google File System (GFS) papers.
YARN stands for "Yet Another Resource Negotiator" and was added later as part of Hadoop 2.0. YARN takes the resource management capabilities that were in MapReduce and packages them so they can be used by new engines. This also streamlines MapReduce to do what it does best, process data. With YARN, you can now run multiple applications in Hadoop, all sharing a common resource management. As of September, 2014, YARN manages only CPU (number of cores) and memory,[8] but management of other resources such as disk, network and GPU is planned for the future.[9]
For the end-users, though MapReduce Java code is common, any programming language can be used with "Hadoop Streaming" to implement the "map" and "reduce" parts of the user's program.[10] Apache Pig, Apache Hive, Apache Spark among other related projects expose higher level user interfaces like Pig Latin and a SQL variant respectively. The Hadoop framework itself is mostly written in the Java programming language, with some native code in C and command line utilities written as shell-scripts.
Apache Hadoop is a registered trademark of the Apache Software Foundation.
- 1 History
- 2 Architecture
- 3 Prominent users
- 4 Hadoop hosted in the Cloud
- 5 Industry support of academic clusters
- 6 Running Hadoop in compute farm environments
- 7 Commercial support
- 8 Papers
- 9 See also
- 10 References
- 11 Bibliography
- 12 External links
Hadoop was created by Doug Cutting and Mike Cafarella[11] in 2005. Cutting, who was working at Yahoo! at the time,[12] named it after his son's toy elephant.[13] It was originally developed to support distribution for the Nutch search engine project.[14]
Hadoop consists of the Hadoop Common package, which provides filesystem and OS level abstractions, a MapReduce engine (either MapReduce/MR1 or YARN/MR2)[15] and the Hadoop Distributed File System (HDFS). The Hadoop Common package contains the necessary Java ARchive (JAR) files and scripts needed to start Hadoop. The package also provides source code, documentation, and a contribution section that includes projects from the Hadoop Community.[citation needed]
For effective scheduling of work, every Hadoop-compatible file system should provide location awareness: the name of the rack (more precisely, of the network switch) where a worker node is. Hadoop applications can use this information to run work on the node where the data is, and, failing that, on the same rack/switch, reducing backbone traffic. HDFS uses this method when replicating data to try to keep different copies of the data on different racks. The goal is to reduce the impact of a rack power outage or switch failure, so that even if these events occur, the data may still be readable.[16]
A small Hadoop cluster includes a single master and multiple worker nodes. The master node consists of a JobTracker, TaskTracker, NameNode and DataNode. A slave or worker node acts as both a DataNode and TaskTracker, though it is possible to have data-only worker nodes and compute-only worker nodes. These are normally used only in nonstandard applications.[17] Hadoop requires Java Runtime Environment (JRE) 1.6 or higher. The standard startup and shutdown scripts require that Secure Shell (ssh) be set up between nodes in the cluster.[18]
In a larger cluster, the HDFS is managed through a dedicated NameNode server to host the file system index, and a secondary NameNode that can generate snapshots of the namenode's memory structures, thus preventing file-system corruption and reducing loss of data. Similarly, a standalone JobTracker server can manage job scheduling. In clusters where the Hadoop MapReduce engine is deployed against an alternate file system, the NameNode, secondary NameNode, and DataNode architecture of HDFS are replaced by the file-system-specific equivalents.
File system[edit]
Hadoop distributed file system[edit]
The Hadoop distributed file system (HDFS) is a distributed, scalable, and portable file-system written in Java for the Hadoop framework. A Hadoop cluster has nominally a single namenode plus a cluster of datanodes, although redundancy options are available for the namenode due to its criticality. Each datanode serves up blocks of data over the network using a block protocol specific to HDFS. The file system uses TCP/IP sockets for communication. Clients use remote procedure call (RPC) to communicate between each other.
HDFS stores large files (typically in the range of gigabytes to terabytes[19]) across multiple machines. It achieves reliability by replicating the data across multiple hosts, and hence theoretically does not require RAID storage on hosts (but to increase I/O performance some RAID configurations are still useful). With the default replication value, 3, data is stored on three nodes: two on the same rack, and one on a different rack. Data nodes can talk to each other to rebalance data, to move copies around, and to keep the replication of data high. HDFS is not fully POSIX-compliant, because the requirements for a POSIX file-system differ from the target goals for a Hadoop application. The tradeoff of not having a fully POSIX-compliant file-system is increased performance for data throughput and support for non-POSIX operations such as Append.[20]
HDFS added the high-availability capabilities, as announced for release 2.0 in May 2012,[21] letting the main metadata server (the NameNode) fail over manually to a backup. The project has also started developing automatic fail-over.
The HDFS file system includes a so-called secondary namenode, a misleading name that some might incorrectly interpreted as a backup namenode for when the primary namenode goes offline. In fact, the secondary namenode regularly connects with the primary namenode and builds snapshots of the primary namenode's directory information, which the system then saves to local or remote directories. These checkpointed images can be used to restart a failed primary namenode without having to replay the entire journal of file-system actions, then to edit the log to create an up-to-date directory structure. Because the namenode is the single point for storage and management of metadata, it can become a bottleneck for supporting a huge number of files, especially a large number of small files. HDFS Federation, a new addition, aims to tackle this problem to a certain extent by allowing multiple namespaces served by separate namenodes.
An advantage of using HDFS is data awareness between the job tracker and task tracker. The job tracker schedules map or reduce jobs to task trackers with an awareness of the data location. For example: if node A contains data (x,y,z) and node B contains data (a,b,c), the job tracker schedules node B to perform map or reduce tasks on (a,b,c) and node A would be scheduled to perform map or reduce tasks on (x,y,z). This reduces the amount of traffic that goes over the network and prevents unnecessary data transfer. When Hadoop is used with other file systems, this advantage is not always available. This can have a significant impact on job-completion times, which has been demonstrated when running data-intensive jobs.[22]
HDFS was designed for mostly immutable files[20] and may not be suitable for systems requiring concurrent write-operations.
HDFS can be mounted directly with a Filesystem in Userspace (FUSE) virtual file system on Linux and some other Unix systems.
File access can be achieved through the native Java API, the Thrift API to generate a client in the language of the users' choosing (C++, Java, Python, PHP, Ruby, Erlang, Perl, Haskell, C#, Cocoa, Smalltalk, and OCaml), the command-line interface, browsed through the HDFS-UI webapp over HTTP, or via 3rd-party network client libraries.[23]
Other file systems[edit]
Hadoop works directly with any distributed file system that can be mounted by the underlying operating system simply by using a file:// URL; however, this comes at a price: the loss of locality. To reduce network traffic, Hadoop needs to know which servers are closest to the data; this is information that Hadoop-specific file system bridges can provide.
In May 2011, the list of supported file systems bundled with Apache Hadoop were:
- HDFS: Hadoop's own rack-aware file system.[24] This is designed to scale to tens of petabytes of storage and runs on top of the file systems of the underlying operating systems.
- FTP File system: this stores all its data on remotely accessible FTP servers.
- Amazon S3 file system. This is targeted at clusters hosted on the Amazon Elastic Compute Cloud server-on-demand infrastructure. There is no rack-awareness in this file system, as it is all remote.
- Windows Azure Storage Blobs (WASB) file system. WASB, an extension on top of HDFS, allows distributions of Hadoop to access data in Azure blob stores without moving the data permanently into the cluster.
A number of third-party file system bridges have also been written, none of which are currently in Hadoop distributions. However, some commercial distributions of Hadoop ship with an alternative filesystem as the default, -specifically IBM and MapR.
- In 2009 IBM discussed running Hadoop over the IBM General Parallel File System.[25] The source code was published in October 2009.[26]
- In April 2010, Parascale published the source code to run Hadoop against the Parascale file system.[27]
- In April 2010, Appistry released a Hadoop file system driver for use with its own CloudIQ Storage product.[28]
- In June 2010, HP discussed a location-aware IBRIX Fusion file system driver.[29]
- In May 2011, MapR Technologies, Inc. announced the availability of an alternative file system for Hadoop, which replaced the HDFS file system with a full random-access read/write file system.
JobTracker and TaskTracker: the MapReduce engine[edit]
Above the file systems comes the MapReduce engine, which consists of one JobTracker, to which client applications submit MapReduce jobs. The JobTracker pushes work out to available TaskTracker nodes in the cluster, striving to keep the work as close to the data as possible. With a rack-aware file system, the JobTracker knows which node contains the data, and which other machines are nearby. If the work cannot be hosted on the actual node where the data resides, priority is given to nodes in the same rack. This reduces network traffic on the main backbone network. If a TaskTracker fails or times out, that part of the job is rescheduled. The TaskTracker on each node spawns off a separate Java Virtual Machine process to prevent the TaskTracker itself from failing if the running job crashes the JVM. A heartbeat is sent from the TaskTracker to the JobTracker every few minutes to check its status. The Job Tracker and TaskTracker status and information is exposed by Jetty and can be viewed from a web browser.
If the JobTracker failed on Hadoop 0.20 or earlier, all ongoing work was lost. Hadoop version 0.21 added some checkpointing to this process; the JobTracker records what it is up to in the file system. When a JobTracker starts up, it looks for any such data, so that it can restart work from where it left off.
Known limitations of this approach are:
- The allocation of work to TaskTrackers is very simple. Every TaskTracker has a number of available slots (such as "4 slots"). Every active map or reduce task takes up one slot. The Job Tracker allocates work to the tracker nearest to the data with an available slot. There is no consideration of the current system load of the allocated machine, and hence its actual availability.
- If one TaskTracker is very slow, it can delay the entire MapReduce job – especially towards the end of a job, where everything can end up waiting for the slowest task. With speculative execution enabled, however, a single task can be executed on multiple slave nodes.
By default Hadoop uses FIFO, and optionally 5 scheduling priorities to schedule jobs from a work queue.[30] In version 0.19 the job scheduler was refactored out of the JobTracker, while adding the ability to use an alternate scheduler (such as the Fair scheduler or the Capacity scheduler, described next).[31]
Fair scheduler[edit]
The fair scheduler was developed by Facebook.[32] The goal of the fair scheduler is to provide fast response times for small jobs and QoS for production jobs. The fair scheduler has three basic concepts.[33]
- Jobs are grouped into pools.
- Each pool is assigned a guaranteed minimum share.
- Excess capacity is split between jobs.
By default, jobs that are uncategorized go into a default pool. Pools have to specify the minimum number of map slots, reduce slots, and a limit on the number of running jobs.
Capacity scheduler[edit]
The capacity scheduler was developed by Yahoo. The capacity scheduler supports several features that are similar to the fair scheduler.[34]
- Jobs are submitted into queues.
- Queues are allocated a fraction of the total resource capacity.
- Free resources are allocated to queues beyond their total capacity.
- Within a queue a job with a high level of priority has access to the queue's resources.
There is no preemption once a job is running.
Other applications[edit]
The HDFS file system is not restricted to MapReduce jobs. It can be used for other applications, many of which are under development at Apache. The list includes the HBase database, the Apache Mahout machine learning system, and the Apache Hive Data Warehouse system. Hadoop can in theory be used for any sort of work that is batch-oriented rather than real-time, is very data-intensive, and benefits from parallel processing of data. It can also be used to complement a real-time system, such as lambda architecture.
As of October 2009, commercial applications of Hadoop[35] included:
- Log and/or clickstream analysis of various kinds
- Marketing analytics
- Machine learning and/or sophisticated data mining
- Image processing
- Processing of XML messages
- Web crawling and/or text processing
- General archiving, including of relational/tabular data, e.g. for compliance
Prominent users[edit]
On February 19, 2008, Yahoo! Inc. launched what it claimed was the world's largest Hadoop production application. The Yahoo! Search Webmap is a Hadoop application that runs on a Linux cluster with more than 10,000 cores and produced data that was used in every Yahoo! web search query.[36]
There are multiple Hadoop clusters at Yahoo! and no HDFS file systems or MapReduce jobs are split across multiple datacenters. Every Hadoop cluster node bootstraps the Linux image, including the Hadoop distribution. Work that the clusters perform is known to include the index calculations for the Yahoo! search engine.
On June 10, 2009, Yahoo! made the source code of the version of Hadoop it runs in production available to the public.[37] Yahoo! contributes all the work it does on Hadoop to the open-source community. The company's developers also fix bugs, provide stability improvements internally, and release this patched source code so that other users may benefit from their effort.
In 2010 Facebook claimed that they had the largest Hadoop cluster in the world with 21 PB of storage.[38] On June 13, 2012 they announced the data had grown to 100 PB.[39] On November 8, 2012 they announced the data gathered in the warehouse grows by roughly half a PB per day.[40]
Other users[edit]
As of 2013, Hadoop adoption is widespread. For example, more than half of the Fortune 50 use Hadoop.[41]
Hadoop hosted in the Cloud[edit]
Hadoop can be deployed in a traditional onsite datacenter as well as in the cloud.[42] The cloud allows organizations to deploy Hadoop without hardware to acquire or specific setup expertise.[43] Vendors who currently have an offer for the cloud include Microsoft, Amazon, and Google.
Hadoop on Microsoft Azure[edit]
Azure HDInsight [44] is a service that deploys Hadoop on Microsoft Azure. HDInsight uses a Windows-based Hadoop distribution that was jointly developed with Hortonworks and allows programming extensions with .NET (in addition to Java).[44] By deploying HDInsight in the cloud, organizations can spin up the number of nodes they want and only get charged for the compute and storage that is used.[44] Hortonworks implementations can also move data from the on-premises datacenter to the cloud for backup, development/test, and bursting scenarios.[44]
Hadoop on Amazon EC2/S3 services[edit]
It is possible to run Hadoop on Amazon Elastic Compute Cloud (EC2) and Amazon Simple Storage Service (S3).[45] As an example The New York Times used 100 Amazon EC2 instances and a Hadoop application to process 4 TB of raw image TIFF data (stored in S3) into 11 million finished PDFs in the space of 24 hours at a computation cost of about $240 (not including bandwidth).[46]
There is support for the S3 file system in Hadoop distributions, and the Hadoop team generates EC2 machine images after every release. From a pure performance perspective, Hadoop on S3/EC2 is inefficient, as the S3 file system is remote and delays returning from every write operation until the data is guaranteed not lost. This removes the locality advantages of Hadoop, which schedules work near data to save on network load.
Amazon Elastic MapReduce[edit]
Elastic MapReduce (EMR)[47] was introduced by Amazon in April 2009. Provisioning of the Hadoop cluster, running and terminating jobs, and handling data transfer between EC2(VM) and S3(Object Storage) are automated by Elastic MapReduce. Apache Hive, which is built on top of Hadoop for providing data warehouse services, is also offered in Elastic MapReduce.[48]
Support for using Spot Instances[49] was later added in August 2011.[50] Elastic MapReduce is fault tolerant for slave failures,[51] and it is recommended to only run the Task Instance Group on spot instances to take advantage of the lower cost while maintaining availability.[52]
Industry support of academic clusters[edit]
IBM and Google announced an initiative in 2007 to use Hadoop to support university courses in distributed computer programming.[53]
In 2008 this collaboration, the Academic Cloud Computing Initiative (ACCI), partnered with the National Science Foundation to provide grant funding to academic researchers interested in exploring large-data applications. This resulted in the creation of the Cluster Exploratory (CLuE) program.[54]
Running Hadoop in compute farm environments[edit]
Hadoop can also be used in compute farms and high-performance computing environments. Instead of setting up a dedicated Hadoop cluster, an existing compute farm can be used if the resource manager of the cluster is aware of the Hadoop jobs, and thus Hadoop jobs can be scheduled like other jobs in the cluster.
Condor integration[edit]
The Condor High-Throughput Computing System integration was presented at the Condor Week conference in 2010.[55]
Commercial support[edit]
A number of companies offer commercial implementations or support for Hadoop.[56]
ASF's view on the use of "Hadoop" in product names[edit]
The Apache Software Foundation has stated that only software officially released by the Apache Hadoop Project can be called Apache Hadoop or Distributions of Apache Hadoop.[57] The naming of products and derivative works from other vendors and the term "compatible" are somewhat controversial within the Hadoop developer community.[58]
Some papers influenced the birth and growth of Hadoop and big data processing. Here is a partial list:
- 2004 MapReduce: Simplified Data Processing on Large Clusters by Jeffrey Dean and Sanjay Ghemawat from Google. This paper inspired Doug Cutting to develop an open-source implementation of the Map-Reduce framework. He named it Hadoop, after his son's toy elephant.
- 2005 From Databases to Dataspaces: A New Abstraction for Information Management, the authors highlight the need for storage systems to accept all data formats and to provide APIs for data access that evolve based on the storage system's understanding of the data.
- 2006 Bigtable: A Distributed Storage System for Structured Data from Google.
- 2008 H-store: a high-performance, distributed main memory transaction processing system
- 2009 MAD Skills: New Analysis Practices for Big Data
- 2011 Apache Hadoop Goes Realtime at Facebook
See also[edit]
- Apache Accumulo – Secure BigTable[59]
- Apache Bigtop - Packaging and interoperability testing of Hadoop-related projects
- Apache Cassandra – A column-oriented database that supports access from Hadoop
- Apache CouchDB is a database that uses JSON for documents, JavaScript for MapReduce queries, and regular HTTP for an API
- Apache Mahout – Machine Learning algorithms implemented on Hadoop
- Big data
- Cask (company)
- Cloud computing
- Data Intensive Computing
- Datameer Analytics Solution (DAS) – data source integration, storage, analytics engine and visualization
- Druid (open-source data store) - Provides a native indexing service for ingesting from HDFS.
- HBase – BigTable-model database
- HPCC – LexisNexis Risk Solutions High Performance Computing Cluster
- Hortonworks - Open source, Hortonworks Data Platform (HDP) provides Hadoop designed for enterprise data processing
- Hypertable – HBase alternative
- MapReduce – Hadoop's fundamental data filtering algorithm
- Nutch – An effort to build an open source search engine based on Lucene and Hadoop, also created by Doug Cutting
- Pentaho – Open source data integration (Kettle), analytics, reporting, visualization and predictive analytics directly from Hadoop nodes
- Pivotal HD - Apache Hadoop distribution enhanced to support enterprise Big Data analytics. Industry's first native massively parallel processing (MPP) SQL database on Hadoop.
- Qubole - a cloud-based Big Data as a service developer
- RapidMiner Radoop – In-Hadoop big data analytics providing a set of algorithms for doing scalable data transformations, advanced analytics, and predictive modeling
- Sector/Sphere – Open source distributed storage and processing
- Simple Linux Utility for Resource Management
- Talend – An open source integration software
- ^ "Hadoop Releases". apache.org. Apache Software Foundation. Retrieved 2014-12-06.
- ^ "Hadoop Releases". Hadoop.apache.org. Retrieved 2014-12-01.
- ^ "What is the Hadoop Distributed File System (HDFS)?". ibm.com. IBM. Retrieved 2014-10-30.
- ^ Malak, Michael (2014-09-19). "Data Locality: HPC vs. Hadoop vs. Spark". datascienceassn.org. Data Science Association. Retrieved 2014-10-30.
- ^ "Continuuity Raises $10 Million Series A Round to Ignite Big Data Application Development Within the Hadoop Ecosystem". finance.yahoo.com. Marketwired. 2012-11-14. Retrieved 2014-10-30.
- ^ "Hadoop-related projects at". Hadoop.apache.org. Retrieved 2013-10-17.
- ^ Roman, Javi. "The Hadoop Ecosystem Table". github.com. Retrieved 2014-12-06.
- ^ "Resource (Apache Hadoop Main 2.5.1 API)". apache.org. Apache Software Foundation. 2014-09-12. Retrieved 2014-09-30.
- ^ Murthy, Arun (2012-08-15). "Apache Hadoop YARN – Concepts and Applications". hortonworks.com. Hortonworks. Retrieved 2014-09-30.
- ^ "[nlpatumd] Adventures with Hadoop and Perl". Mail-archive.com. 2010-05-02. Retrieved 2013-04-05.
- ^ "Michael J. Cafarella". Web.eecs.umich.edu. Retrieved 2013-04-05.
- ^ Hadoop creator goes to Cloudera[dead link]
- ^ Ashlee Vance (2009-03-17). "Hadoop, a Free Software Program, Finds Uses Beyond Search". The New York Times. Archived from the original on 11 February 2010. Retrieved 2010-01-20.
- ^ "Hadoop contains the distributed computing platform that was formerly a part of Nutch. This includes the Hadoop Distributed Filesystem (HDFS) and an implementation of MapReduce." About Hadoop[dead link]
- ^ Harsh Chouraria (21 October 2012). "MR2 and YARN Briefly Explained". cloudera.com. Cloudera. Retrieved 23 October 2013.
- ^ "HDFS User Guide". Hadoop.apache.org. Retrieved 2014-09-04.
- ^ "Running Hadoop on Ubuntu Linux (Multi-Node Cluster)".
- ^ "Running Hadoop on Ubuntu Linux (Single-Node Cluster)". Retrieved 6 June 2013.
- ^ "HDFS Architecture". Retrieved 1 September 2013.
- ^ a b Yaniv Pessach (2013). "Distributed Storage" (Distributed Storage: Concepts, Algorithms, and Implementations ed.). Amazon.com
- ^ "Version 2.0 provides for manual failover and they are working on automatic failover:". Hadoop.apache.org. Retrieved 30 July 2013.
- ^ "Improving MapReduce performance through data placement in heterogeneous Hadoop Clusters" (PDF). Eng.auburn.ed. April 2010.
- ^ "Mounting HDFS". Retrieved May 2014.
- ^ "HDFS Users Guide – Rack Awareness". Hadoop.apache.org. Retrieved 2013-10-17.
- ^ "Cloud analytics: Do we really need to reinvent the storage stack?". IBM. June 2009.
- ^ "HADOOP-6330: Integrating IBM General Parallel File System implementation of Hadoop Filesystem interface". IBM. 2009-10-23.
- ^ "HADOOP-6704: add support for Parascale filesystem". Parascale. 2010-04-14.
- ^ "HDFS with CloudIQ Storage". Appistry,Inc. 2010-07-06.
- ^ "High Availability Hadoop". HP. 2010-06-09.
- ^ job[dead link]
- ^ "Refactor the scheduler out of the JobTracker". Hadoop Common. Apache Software Foundation. Retrieved 9 June 2012.
- ^ M. Tim Jones (6 December 2011). "Scheduling in Hadoop". ibm.com. IBM. Retrieved 20 November 2013.
- ^ [1] Hadoop Fair Scheduler Design Document
- ^ [2]Capacity Scheduler Guide
- ^ October 10, 2009 (2009-10-10). ""How 30+ enterprises are using Hadoop", in DBMS2". Dbms2.com. Retrieved 2013-10-17.
- ^ Yahoo! Launches World's Largest Hadoop Production Application
- ^ "Hadoop and Distributed Computing at Yahoo!". Yahoo!. 2011-04-20. Retrieved 2013-10-17.
- ^ "HDFS: Facebook has the world's largest Hadoop cluster!". Hadoopblog.blogspot.com. 2010-05-09. Retrieved 2012-05-23.
- ^ "Under the Hood: Hadoop Distributed File system reliability with Namenode and Avatarnode". Facebook. Retrieved 2012-09-13.
- ^ "Under the Hood: Scheduling MapReduce jobs more efficiently with Corona". Facebook. Retrieved 2012-11-09.
- ^ "Altior's AltraSTAR – Hadoop Storage Accelerator and Optimizer Now Certified on CDH4 (Cloudera's Distribution Including Apache Hadoop Version 4)" (Press release). Eatontown, New Jersey: Altior Inc. 2012-12-18. Retrieved 2013-10-30.
- ^ "What is Hadoop?".
- ^ "Hadoop". Azure.microsoft.com. Retrieved 2014-07-22.
- ^ a b c d "HDInsight | Cloud Hadoop". Azure.microsoft.com. Retrieved 2014-07-22.
- ^ Varia, Jinesh (@jinman). "Taking Massive Distributed Computing to the Common Man – Hadoop on Amazon EC2/S3". Amazon Web Services Blog. Amazon.com. Retrieved 9 June 2012.
- ^ Gottfrid, Derek (November 1, 2007). "Self-service, Prorated Super Computing Fun!". The New York Times. Retrieved May 4, 2010.
- ^ "AWS | Amazon Elastic MapReduce (EMR) | Hadoop MapReduce in the Cloud". Aws.amazon.com. Retrieved 2014-07-22.
- ^ "Amazon Elastic MapReduce Developer Guide" (PDF). Retrieved 2013-10-17.
- ^ "Amazon EC2 Spot Instances". Aws.amazon.com. Retrieved 2014-07-22.
- ^ "Amazon Elastic MapReduce Now Supports Spot Instances". Amazon.com. 2011-08-18. Retrieved 2013-10-17.
- ^ "Amazon Elastic MapReduce FAQs". Amazon.com. Retrieved 2013-10-17.
- ^ Using Spot Instances with EMR on YouTube
- ^ "Google Press Center: Google and IBM Announce University Initiative to Address Internet-Scale Computing Challenges". Google. 2007-10-08. Retrieved 2013-10-17.
- ^ "NSF, Google, IBM form CLuE". Hadoopcommunity.wordpress.com. Retrieved 2013-10-17.
- ^ "Condor integrated with Hadoop's Map Reduce". University of Wisconsin–Madison. 2010-04-15.
- ^ "Why the Pace of Hadoop Innovation Has to Pick Up". Gigaom.com. 2011-04-25. Retrieved 2013-10-17.
- ^ "Defining Hadoop". Wiki.apache.org. 2013-03-30. Retrieved 2013-10-17.
- ^ "Defining Hadoop Compatibility: revisited". Mail-archives.apache.org. 2011-05-10. Retrieved 2013-10-17.
- ^ "Apache Accumulo User Manual: Security". apache.org. Apache Software Foundation. Retrieved 2014-12-03.
- Lam, Chuck (July 28, 2010). Hadoop in Action (1st ed.). Manning Publications. p. 325. ISBN 1-935182-19-6.
- Venner, Jason (June 22, 2009). Pro Hadoop (1st ed.). Apress. p. 440. ISBN 1-4302-1942-4.
- White, Tom (June 16, 2009). Hadoop: The Definitive Guide (1st ed.). O'Reilly Media. p. 524. ISBN 0-596-52197-9.
External links[edit]
- Official Hadoop Homepage
- Official Hadoop Wiki
- Introducing Apache Hadoop: The Modern Data Operating System — lecture given at Stanford University by Co-Founder and CTO of Cloudera, Amr Awadallah (video archive) (YouTube).


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Gill Bejerano
Publication Details
Enhancers: five essential questions
2013; 14 (4): 288-295
It is estimated that the human genome contains hundreds of thousands of enhancers, so understanding these gene-regulatory elements is a crucial goal. Several fundamental questions need to be addressed about enhancers, such as how do we identify them all, how do they work, and how do they contribute to disease and evolution? Five prominent researchers in this field look at how much we know already and what needs to be done to answer these questions.
View details for Web of Science ID 000316975300012
View details for PubMedID 23503198


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Denying Gravity - Guitar Playthrough
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Strathisla Distillery, the oldest operating distillery in the Highlands of Scotland, offers a choice of tours with some very special drams.
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Advance reservation recommended. Allow 3 hours. £35.00 Over 18s only please.


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Spiritual Life Leadership
One of the most important groups on campus is Spiritual Life Leadership. It is the umbrella organization of all spiritual life organizations on campus.
Spiritual Life Leadership's function is to coordinate efforts by individuals and groups to promote spiritual life on campus. Information is shared in a bi-monthly meeting and special events such as retreats, prayer vigils, film showings, music concerts and outreach activities are planned together. The key role that Spiritual Life Leadership plays is to promote a spirit of unity and fellowship on campus as we face the challenges of life.
What: IVOP (Inspirational Voices of Praise)
Where: Dewald Chapel, Myers Sanctuary
When: Every Friday at 6 p.m.
Who: Anyone interested in offering their voices to sing Gospel Music (auditions required)
Contact: Barbara Lyons email@example.com IVOP Advisor
What: FLOCK (For the Love of Christ Our King)
Where: Dewald Chapel Room 104
When: Every Monday at 6 p.m.
Who: Interested Catholics and others who want to learn more about their faith
Contacts: Gabby Pishotti email@example.com or Kate O'Neil email@example.com
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Where: HPCC, Alumni Room
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