Abstract :
The rapid advancement of the Internet of Things and Electronic Commerce that entered the era of big data.
The characteristics, such as great amount and heterogeneousity of big data bring the dare to the storage and analytics.
As there are many architecture in the field for implementing big data in cloud computing literature. This paper open
a comparison of universal storage architecture, smash and Hadoop YARN with Apache Spark for big data in cloud
environment. In recent times, big data has become a trendy research topic and brought about a scope of new
challenges that must be tackled to sustain many commercial and research demands. Tackling these big data issues
requires capabilities not characteristically found in common Cloud platforms. This includes a distributed file system
for capturing and storing data; a high performance computing engine able to process such huge quantities of data; a
reliable database system able to optimize the indexing and querying of the data, and geospatial capabilities to imagine
the resultant analyzed data. With the high-scalability cloud technologies, Hadoop and Spark, the proposed system
architecture is first implemented successfully and resourcefully. Experimental results illustrate the effectiveness and
efficiency of the proposed system services via an advanced web technology. In addition, some experimental results
signify that the computing ability of Spark is better than that of Hadoop.
Keyword :
Cloud, big data, data analytics, traffic analysis, data model, Apache Spark, NoSQL, Hadoop YARN, HBase