Meta Data Api

The core concept of the cascading API is piping and streaming. A pipeline is a series of processing steps (parsing, looping, filtering, and so on) that define the data processing to be performed, and the flow is the union of pipelines with data sources and data receivers (Data-sink). Cascading is a new data processing API for Hadoop clusters that uses expressive APIs to build complex processing workflows, and ...

The REST service can help developers to provide services to end users with a simple and unified interface. However, in the application scenario of data analysis, some mature data analysis tools (such as Tableau, Excel, etc.) require the user to provide an ODBC data source, in which case the REST service does not meet the user's need for data usage. This article provides a detailed overview of how to develop a custom ODBC driver based on the existing rest service from an implementation perspective. The article focuses on the introduction of ODBC ...

In terms of how the organization handles data, Apache Hadoop has launched an unprecedented revolution--through free, scalable Hadoop, to create new value through new applications and extract the data from large data in a shorter period of time than in the past. The revolution is an attempt to create a Hadoop-centric data-processing model, but it also presents a challenge: How do we collaborate on the freedom of Hadoop? How do we store and process data in any format and share it with the user's wishes?

Blog Description: 1, research version hbase 0.94.12;2, posted source code may be cut, only to retain the key code. 　　Discusses the HBase write data process from the client and server two aspects. One, client-side 1, write data API write data is mainly htable and batch write two API, the source code is as follows://write the API public void to put ("final") throws IO ...

HBase is a distributed, column-oriented, open source database based on Google's article "Bigtable: A Distributed Storage System for Structured Data" by Fay Chang. Just as Bigtable takes advantage of the distributed data storage provided by Google's File System, HBase provides Bigtable-like capabilities over Hadoop. HBase Implements Bigtable Papers on Columns ...

"Editor's note" in the famous tweet debate: MicroServices vs. Monolithic, we shared the debate on the microservices of Netflix, Thougtworks and Etsy engineers. After watching the whole debate, perhaps a large majority of people will agree with the service-oriented architecture. In fact, however, MicroServices's implementation is not simple. So how do you build an efficient service-oriented architecture? Here we might as well look to mixrad ...

Table & Column Accessibility Row keytimestampcolumn accessibility urihttp://www.aliyun.com/zixun/aggregation/33959.html "> Parser r1t3url=http://www.taobao.comtitle= daily specials t2host=taobao.com t1 R2 ...

According to the authority forecast, in the next few years the Chinese cloud storage market's annual compound growth rate will reach 103%, the cloud storage market scale will grow from 2009 6.05 million US dollars rapidly to 2014 210 million dollars, the cloud storage has the future storage market development trend. In the current cloud storage market, the cloud disk is the most typical and the most mature application mode, it can be in the public cloud or private cloud environment to achieve data storage, access, backup, sharing functions, to provide users with simple and efficient data storage or data sharing services. At present, facing the personal consumer market of cloud disk technology has been the trend ...

This time, we share the 13 most commonly used open source tools in the Hadoop ecosystem, including resource scheduling, stream computing, and various business-oriented scenarios. First, we look at resource management.

Hadoop is a large data distributed system infrastructure developed by the Apache Foundation, the earliest version of which was the 2003 original Yahoo! Dougcutting based on Google's published academic paper. Users can easily develop and run applications that process massive amounts of data in Hadoop without knowing the underlying details of the distribution. The features of low cost, high reliability, high scalability, high efficiency and high fault tolerance make Hadoop the most popular large data analysis system, yet its HDFs and mapreduc ...