Apache Giraph

Hadoop is a large data distributed system infrastructure developed by the Apache Foundation, the earliest version of which was the 2003 original Yahoo! Doug cutting is 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 mapred ...

The appearance of MapReduce is to break through the limitations of the database. Tools such as Giraph, Hama and Impala are designed to break through the limits of MapReduce. 　　While the operation of the above scenarios is based on Hadoop, graphics, documents, columns, and other NoSQL databases are also an integral part of large data. 　　Which large data tool meets your needs? The problem is really not easy to answer in the context of the rapid growth in the number of solutions available today. Apache Hado ...

Top Ten Open Source technologies: Apache HBase: This large data management platform is built on Google's powerful bigtable management engine. As a database with open source, Java coding, and distributed multiple advantages, HBase was originally designed for the Hadoop platform, and this powerful data management tool is also used by Facebook to manage the vast data of the messaging platform. Apache Storm: A distributed real-time computing system for processing high-speed, large data streams. Storm for Apache Had ...

The use of Hadoop has been going on for some time, from the beginning of confusion, to various attempts, to the current combination of .... Slowly involved in data processing things, has been inseparable from Hadoop. The success of Hadoop in large data fields has led to its own accelerated development. 　　Now the Hadoop family product, has already reached 20 many. It is necessary to do a collation of their knowledge, the product and technology are strung together. 　　Not only can deepen the impression, but also to the future technology direction, technical selection to do the groundwork. A word product introduction: ...

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 ...

Big data has grown rapidly in all walks of life, and many organizations have been forced to look for new and creative ways to manage and control such a large amount of data, not only to manage and control data, but to analyze and tap the value to facilitate business development. Looking at big data, there have been a lot of disruptive technologies in the past few years, such as Hadoop, Mongdb, Spark, Impala, etc., and understanding these cutting-edge technologies will also help you better grasp the trend of large data development. It is true that in order to understand something, one must first understand the person concerned with the thing. So, ...

Big data has grown rapidly in all walks of life, and many organizations have been forced to look for new and creative ways to manage and control such a large amount of data, not only to manage and control data, but to analyze and tap the value to facilitate business development. Looking at big data, there have been a lot of disruptive technologies in the past few years, such as Hadoop, Mongdb, Spark, Impala, etc., and understanding these cutting-edge technologies will also help you better grasp the trend of large data development. It is true that in order to understand something, one must first understand the person concerned with the thing. So, ...

In today's technology world, big Data is a popular it buzzword. To mitigate the complexity of processing large amounts of data, Apache developed a reliable, scalable, distributed computing framework for hadoop--. Hadoop is especially good for large data processing tasks, and it can leverage its distributed file systems, reliably and cheaply, to replicate data blocks to nodes in the cluster, enabling data to be processed on the local machine. 　　Anoop Kumar explains the techniques needed to handle large data using Hadoop in 10 ways. For the ...

Large data areas of processing, my own contact time is not long, formal projects are still in development, by the large data processing attraction, so there is the idea of writing articles. Large data is presented in the form of database technologies such as Hadoop and "NO SQL", Mongo and Cassandra. Real-time analysis of data is now likely to be easier. Now the transformation of the cluster will be more and more reliable, can be completed within 20 minutes. Because we support it with a table? But these are just some of the newer, untapped advantages and ...