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The establishment of enterprise security building Open source SIEM platform, SIEM (security information and event management), as the name suggests is for security information and event management system for most businesses is not cheap security system, this article combined with the author's experience describes how to use open source software Analyze data offline and use algorithms to mine unknown attacks. Recalling the system architecture to WEB server log, for example, through logstash WEB server to collect query log, near reality ...

Construction of enterprise security building open source SIEM platform. SIEM (security information and event management), as its name implies, is a management system for security information and events. It is not a cheap security system for most enterprises. This article uses the author's experience to introduce how to use open source software to analyze data offline and use attack modeling Way to identify attacks. Review the system architecture to the database, for example, through logstash to collect mysql query log, near real-time backup ...

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 ...

Spark can read and write data directly to HDFS and also supports Spark on YARN. Spark runs in the same cluster as MapReduce, shares storage resources and calculations, borrows Hive from the data warehouse Shark implementation, and is almost completely compatible with Hive. Spark's core concepts 1, Resilient Distributed Dataset (RDD) flexible distribution data set RDD is ...

To use Hadoop, data consolidation is critical and hbase is widely used. In general, you need to transfer data from existing types of databases or data files to HBase for different scenario patterns. The common approach is to use the Put method in the HBase API, to use the HBase Bulk Load tool, and to use a custom mapreduce job. The book "HBase Administration Cookbook" has a detailed description of these three ways, by Imp ...

Big Data is the industry's future trend, its momentum has been unstoppable, and Hadoop as a larger distributed computing and storage offline processing cluster representatives, in this year is more red all over the world. Admittedly, big data is becoming an important factor in changing people's lives, and the integration of big data and cloud computing is in full swing. From the quantitative, structural world, to the uncertain, unstructured world. This transformation allows us to understand the real information, improve the level of decision-making, when the community of natural data has a more perfect, ready to analyze the ability, our grasp of the event and the prediction can ...

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 ...

Spark is a cluster computing platform that originated at the University of California, Berkeley Amplab. It is based on memory calculation, from many iterations of batch processing, eclectic data warehouse, flow processing and graph calculation and other computational paradigm, is a rare all-round player. Spark has formally applied to join the Apache incubator, from the "Spark" of the laboratory "" EDM into a large data technology platform for the emergence of the new sharp. This article mainly narrates the design thought of Spark. Spark, as its name shows, is an uncommon "flash" of large data. The specific characteristics are summarized as "light, fast ...

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 from HD ...