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Extra info for Big data in complex systems : challenges and opportunities
Unavailability of Query Language: There is no specific query language for big data. e. SQL and at the same time compromising the consistency and accuracy. It is important to understand that if the relational database using SQL is serving the purpose effectively then what is the need to switch to big data (After all it is not the next generation of database technology). Big data is unstructured data which scale up our analysis and has a limited query capability. Lack of Analyst: One of the major emerging concerns is the lack of analysts who have the expertise to handle big data for finding useful patterns using cloud computing.
Cost savings of cloud computing primarily occur when a business first starts using it. SaaS (Software as a Service) applications will have lower total cost of ownership for the first two years because these applications do not require large capital investment for licenses or support infrastructure. After that, the on-premises option can become the cost-savings winner from an accounting perspective as the capital assets involved depreciate. 24 • 7 R. Vashist Validity of Patterns: The validity of the patterns found after the analysis of big data is another important factor.
Hadoop has quickly become a standard in industry as a highly scalable data-intensive MapReduce platform. , 2012)). 34 J. Pokorný et al. Table 1 The three-layered Hadoop software stack Level of abstraction L5 Data processing HiveQL/PigLatin/Jaql L2 L4 Hadoop MapReduce Dataflow Layer M/R jobs L1 Get/Put ops HBase Key Value Store Hadoop Distributed File System One remarkable difference of the Hadoop software stack from the universal DBMS architecture is that we can access data by three different sets of tools in particular layers.