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Features of Apache Spark
1. Swift Processing
Apache Spark is 100 times faster while talking about memory and 10 times faster while talking on the disk on comparing with Hadoop. It Provides high data processing speed.2. Dynamic in Nature
It is easily possible to develop a parallel application. Since Spark offers 80 high-level operators.3. In-Memory Computation in Spark
Processing speed increases because of in-memory computation in spark. Due to in-memory processing data is being cached. Hence we need not fetch data from the disk every time. Therefore, time is saved. There is a DAGexecution engine available in Spark. That facilitates in-memory computation. Also, due to acyclic data flow, it achieves high speed.4. Reusability
For join stream of historical data, batch-processing or run ad-hoc queries on stream state the Spark code can be reused.5. Fault Tolerance in Spark
Apache Spark provides fault tolerance through Spark abstraction-RDD. Spark RDDs are capable to dignosis the failure of any worker node in the cluster. Hence, it results as no loss of data or loss of data is decreased to zero.6. Real-Time Stream Processing
Spark Offers real-time stream processing. Hadoop MapReduce was able to handle and process data which is already present, but it does not support real-time data Processing. Since, Spark Streaming comes in picture, we can solve this problem.7. Lazy Evaluation in Apache Spark
All the transformations we make in Spark RDD does not give the result right away rather a new RDD is formed from the existing one because they are Lazy (Lazy Evaluation) in nature. That enhances the efficiency of the system.8. Support Multiple Languages
Apache Spark supports multiple languages. For Example Java, R, Scala, Python. Thus, it ensures dynamicity. Also, improves the limitation of Hadoop . Since, Hadoop can build applications only in Java.There are many more sparkiling features of Spark available. To study all, follow link: 11 shining Features of Apache Spark – Learn the benefits of using Spark
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