I hope this post will help you to answer some questions related to Apache spark that might be coming into your mind these days related to Spark in Big Data Analytics.
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It is a framework for performing analytics on a distributed cluster, It uses in memory computation over map reduce for better performance and speed. It runs on the top of Hadoop cluster and access Hadoop file system. It can process structured data stored in hive and streaming data from flume.
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Will Spark replace Hadoop?
– Hadoop is a distributed, parallel processing framework that has been used for Map Reduce jobs. These jobs take minutes to hours for completion. Spark has come up as an alternative approach to traditional Map reduce model that can be used for real time data processing and fast interactive queries that complete quite fast. Thus, Hadoop supports both Map Reduce and Apache Spark.
Spark uses in memory storage whereas Hadoop cluster stores data on disk. Hadoop uses replication policy for fault tolerance mechanism whereas Spark uses Resilient Distributed Datasets for fault tolerant mechanism.
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1.) Speed – It completes job running in memory on Hadoop Clusters 100 times faster, on disk 10 times faster. It Stores intermediate data in memory using concept of Resilient Distributed Dataset It removes unnecessary read and write on disk for intermediate data.
2.) Easy to use – It allows you to develop your code in JAVA, Scala and Python.
3.) SQL, Complex Analytics and Streaming –Spark supports SQL like features, Complex Analytics likemachine learning.
4.) Runs Everywhere – Spark runs on Hadoop, Mesos, standalone, or in cloud. It can access data in diverse data sources like HDFS, HBase, Cassandra and S3.
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Spark Use Cases –
Insurance – optimize claim process by using Spark’s machine learning capabilities to process and analyze all claims being filed.
Retail – Use spark to analyze point of sale transaction data and coupon usage.Used for Interactive Data Processing and Data Mining.
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