Distributed Computing Engines for Big Data Analytics
Technologies like cloud computing paved way for dealing with massive amounts of data. Prior to cloud, it was not possible unless you invest large amounts for computing resources. Now there is ecosystem which is conducive to storing and processing voluminous data that cannot be handled by local compu...
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| Vydáno v: | International journal of recent technology and engineering Ročník 8; číslo 2; s. 5841 - 5845 |
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| Hlavní autoři: | , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
30.07.2019
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| ISSN: | 2277-3878, 2277-3878 |
| On-line přístup: | Získat plný text |
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| Shrnutí: | Technologies like cloud computing paved way for dealing with massive amounts of data. Prior to cloud, it was not possible unless you invest large amounts for computing resources. Now there is ecosystem which is conducive to storing and processing voluminous data that cannot be handled by local computing resources. With such ecosystem, big data technology came into existence. Big data is the data characterized by volume, velocity, veracity and variety. This has enabled enterprises to give more value to every piece of data. This in turn led to the increased usage of cloud for both storage and processing. For processing big data efficient technologies are required. New programming paradigm like MapReduce with Hadoop distributed programming framework is widely used. However, there are other emerging frameworks like Apache Spark and Apache Flink to handle big data more efficiently. In this paper, empirical study is made on the three frameworks like Hadoop, Apache Spark and Apache Flink with different parameters like type of network, block size of HDFS, input data size and other configuration changes. The experimental results revealed that Apache Spark and Apache Flink outperform Hadoop. This is evaluated with different benchmark big data workloads. |
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| ISSN: | 2277-3878 2277-3878 |
| DOI: | 10.35940/ijrte.B3771.078219 |