on 15 October 20
Spark has everything going right for it, from user admiration to client satisfaction, Spark is the new favorite of one and all when it comes to dealing with big data streaming and analysis. As a matter of fact, spark streaming analytics is the best solution for the real-time distributed computation. It was incubated at University of California at Berkeley in AMP Lab and then taken up by Apache Incubator. It emerged as a premium project of the year 2014. Spark and Storm, have a lot in common but Spark indeed is a general-purpose distributed computing platform. Spark streaming analytics has now become the norm and giving run for the money to its competitors.
Spark is an efficient replacement of MapReduce functions of Hadoop. It can run on a Hadoop cluster as it relies on YARN for resource allocation. Apart from this, the beauty of Spark is that it can also gel with Mesos for scheduling. Besides, it can also run on its own with the help of its built-in scheduler. One must note that distributed file system is required if it is not using Hadoop and running on a cluster.
Spark can be programmed with multi-language programming as it is written in Scala. It also has specific API support for Scala, Python, and Java. It also has adapters that make it compatibles with data stored in various sources which may be as diverse as HDFS files, Cassandra, Hbase, and S3.
The most startling thing about Spark is that it supports multiprocessing and uses libraries involved. Spark also supports a streaming model which comes from many spark modules including purpose-built modules for SQL, Access, and Machine Learning along with Stream processing.
Spark also gives the facility of an interactive shell that can perform quick-and-dirty prototyping and explore data in real time with the help of Scala or Python APSs.
Spark overpowers Storm
If one was to compare Spark with Storm, the major difference comes in the functionality of the two. In spark, one works with API that interweaves consecutive method calls to invoke earlier operations whereas in Storm classes have to be created and interfaces to be decided upon. The Data scientists find Spark processing of Data more convenient and hassle free while the Storm is rather cumbersome and asks for greater skills and experience from the user.
Indeed Spark is the ultimate answer for massive scalability and can handle production clusters with thousands of nodes. Now it has been firmly established through numerous documents and tests that Spark is faster, highly scalable and flexible open source distributed computing framework which goes well with Hadoop and Mesos. It supports several computational models, including streaming, graph-centric operations, SQL, Access and Machine Learning. Spark can be easily used to develop real time analytics as well.
Now that most people are choosing Spark over Storm, the supremacy of Spark is already established beyond doubt and it is being dubbed that future belongs to Spark in big data analysis. Besides, if one is working with Hadoop and you would be dealing with graph processing, SQL and Access or batch process there is nothing better than putting your money on Spark.
However, you would do well to make a factor by factor analysis of Storm and Spark before making an informed decision. You could test both platforms after benchmarking with the estimated workload before adopting it.
At times one could find that a mix of both Storm and Spark is ideal then you could go for both as we all know, both are open source and therefore very affordable.
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