Hortonworks said that the distributed Hadoop open source big data software has been to provide Google's public cloud Cloud Platform Google (Gu Geyun platform) support. This news is actually not too surprising, which marks the common development of Google Corporation and Hortonworks company.
With the explosive growth of data and the thousands of machine clusters, we need to make algorithms can be adapted to run in such a distributed environment. Running machine learning algorithms in a general distributed computing environment has a series of challenges. This paper explores how to implement and deploy deep learning in a Hadoop cluster.
2014, although big data applications have not thorough popular, but there are more and more industry users attempt to introduce large data related technology solve how to manage and use of growing all kinds of data and data access control for a new type of risk associated with the rise of big data, the analysis of present situation and put forward the solution.
Since the hive, SQL on Hadoop related system has been flourishing, faster and faster, function more and more complete. This article is not to compare the so-called "interactive query which is strong", but trying to sort out a unified perspective, to see what the various systems have the same technology.
The main discussion of this paper is to use Classification to carry out MLlib work. The typical application scenario is CTR Prediction AD, which is the source of most of the Internet Co's profits. According to amateur understanding, advertising CTR estimated using the most basic algorithm or L1 regularization of Regression Logistic.
Data in the data mean that the data volume has exceeded the upper limit of single server processing, but there is no need to use a cluster consisting of thousands of nodes, usually TB, rather than PB. Here, we might as well go into the Bloomberg use cases, focusing on time series data processing data and the volume of the challenge.
As we all know, cloud computing is to provide services in the form of IT capabilities, service forms can be divided into IaaS, PaaS, SaaS three. Today's protagonist Qingyun qingcloud is to provide IAAs service leader, it is understood currently Qingyun active users has more than 22000, of which 80% is paying business users.
Business and consumers are generating TB and even PB level data, a large number of companies have increased research and development, is committed to the collection, storage, management, analysis of data. American IT website CRN named the 2014 big data is particularly eye-catching ten emerging big data Venture Company, take a look.
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