Comparison Of Apache Hadoop & Apache Spark
Corporate world is abuzz with talk of big data. Hadoop & Spark provide the most common tools for executing big data related responsibilities. These frameworks share many common features, but they also have some notable differences. Below are a few of them:
Hadoop, at its core, is a distributed database: It distributes massive data collections among a large number of servers. It indexes data and tracks it, making big-data processing and analytics much more efficient than before. Spark is an alternative data-processing software that uses distributed data.
You can use them both. Hadoop is made up of two components: HDFS (Hadoop Distributed file system) and MapReduce. Spark is not required to process data. Spark can be used independently of Hadoop. Spark has no file management system of its own, and so must be combined, either with HDFS, or another cloud platform. Spark was developed for Hadoop and many people agree that the two work well together.
Spark is faster because it uses a different method to process data. MapReduce takes steps, whereas Spark uses the whole data set.
Spark’s rapidity may not be necessary. MapReduce processing is fine for data operations and reporting if they are relatively static. Spark can be used to perform analytics on data streams, such as data collected by sensors in an aircraft, or apps that require multiple operations. Spark implementations include online product recommendation, real-time campaigning, cyber-security analysis & log monitoring.
Failure recovery Hadoop has built-in resilience to system faults because it writes data directly to disk for each operation. Spark however, offers similar fault tolerence as data in Spark is stored across multiple data clusters using resilient distributed datasets. RDD can recover data stored on disks or in memory.