Previsna tailor solutions to customers who require real-time analysis, historical data analysis, storage and backup of very large amounts of data. We use tools, technologies and programming languages such as Hadoop HDFS, Hadoop MapReduce, Apache Spark, Apache Storm and Scala.
Hadoop MapReduce
 MapReduce is the heart of Hadoop®. It is this programming paradigm that allows for massive scalability across hundreds or thousands of servers in a Hadoop cluster. The MapReduce concept is fairly simple to understand for those who are familiar with clustered scale-out data processing solutions.
For people new to this topic, it can be somewhat difficult to grasp, because it’s not typically something people have been exposed to previously.
Hadoop HDFS
 HDFS is a Java-based file system that provides scalable and reliable data storage, and it was designed to span large clusters of commodity servers. HDFS has demonstrated production scalability of up to 200 PB of storage and a single cluster of 4500 servers, supporting close to a billion files and blocks. When that quantity and quality of enterprise data is available in HDFS, and YARN enables multiple data access applications to process it, Hadoop users can confidently answer questions that eluded previous data platforms.
HDFS is a scalable, fault-tolerant, distributed storage system that works closely with a wide variety of concurrent data access applications, coordinated by YARN. HDFS will “just work” under a variety of physical and systemic circumstances. By distributing storage and computation across many servers, the combined storage resource can grow linearly with demand while remaining economical at every amount of storage.
|
