Skip to main content

Wearable Hadoop technology

While Hadoop and MapReduce have been harping on distributed parallel processing on community hardware for long, some Hadoop enthusiasts have taken this too far. Enter Datasayer from Edward J Yoon who has built wearable Hadoop technology. This means every time you walk, jump, blink your eyes or move your hand, you would be using your kinetic energy to run a MapReduce job.

Read below to understand this breathtaking innovation.




  • Hadoop Glass– Inspired by Google glass, this forms the client layer of your Hadoop cluster. Using the eye wear interface, you can trigger a MapReduce job or fire a Pig, Hive or Hama query.
  • Hadoop Watch– Inspired by Samsung Gear (Watch) technology, this forms the name node of your Hadoop cluster and stores the meta data for the data stored in data nodes. Further, the job tracker is also hosted on the watch itself and using MRv1, controls the execution of tasks on different nodes.

  • Hadoop Shoes– This forms the data nodes layer of the cluster. By default the replication factor is 2 for each block resident in data node shoe. Each time you walk or jump, the kinetic energy is converted to CPU cycles and powers the tasks running via tasktracker on each shoe.

  • Hadoop Kinect- Inspired by Microsoft’s Kinect technology, you can configure the shoes a.k.a data nodes of another person in vicinity of 100 meters. The architecture also leverages advanced wireless technology to communicate between nodes. From a scalability perspective, all you need to do is have more people jumping or walking with data node shoes in the vicinity.


If all this sounds too good to be true, well, you guessed it right. This is an All Fools Day prank by Datasayer. 

Follow @datasayer

Comments

  1. Way to go Edward....

    ReplyDelete
  2. Wearable Hardtop technology is taking the best technological environment which will be very essential to me as well and sure the content will be very important to me as well.
    Best Tech News

    ReplyDelete

Post a Comment

Popular posts from this blog

Data deduplication tactics with HDFS and MapReduce

As the amount of data continues to grow exponentially, there has been increased focus on stored data reduction methods. Data compression, single instance store and data deduplication are among the common techniques employed for stored data reduction.
Deduplication often refers to elimination of redundant subfiles (also known as chunks, blocks, or extents). Unlike compression, data is not changed and eliminates storage capacity for identical data. Data deduplication offers significant advantage in terms of reduction in storage, network bandwidth and promises increased scalability.
From a simplistic use case perspective, we can see application in removing duplicates in Call Detail Record (CDR) for a Telecom carrier. Similarly, we may apply the technique to optimize on network traffic carrying the same data packets.
Some of the common methods for data deduplication in storage architecture include hashing, binary comparison and delta differencing. In this post, we focus on how MapReduce and…

In-memory data model with Apache Gora

Open source in-memory data model and persistence for big data framework Apache Gora™ version 0.3, was released in May 2013. The 0.3 release offers significant improvements and changes to a number of modules including a number of bug fixes. However, what may be of significant interest to the DynamoDB community will be the addition of a gora-dynamodb datastore for mapping and persisting objects to Amazon's DynamoDB. Additionally the release includes various improvements to the gora-core and gora-cassandra modules as well as a new Web Services API implementation which enables users to extend Gora to any cloud storage platform of their choice. This 2-part post provides commentary on all of the above and a whole lot more, expanding to cover where Gora fits in within the NoSQL and Big Data space, the development challenges and features which have been baked into Gora 0.3 and finally what we have on the road map for the 0.4 development drive.
Introducing Apache Gora Although there are var…

Large scale graph processing with Apache Hama

Recently Apache Hama team released official 0.7.0 version. According to the release announcement, there were big improvements in Graph package. In this article, we provide an overview of the newly improved Graph package of Apache Hama, and the benchmark results that performed by cloud platform team at Samsung Electronics.

Large scale datasets are being increasingly used in many fields. Graph algorithms are becoming important for analyzing big data. Data scientists are able to predict the behavior of the customer, the trends of the market, and make a decision by analyzing the graph structure and characteristics. Currently there are a variety of open source graph analytic frameworks, such as Google’s Pregel[1], Apache Giraph[2], GraphLab[3] and GraphX[4]. These frameworks are aimed at computations varying from classical graph traversal algorithms to graph statistics calculations such as triangle counting to complex machine learning algorithms. However these frameworks have been developed…