SQL and Hadoop engines point to something serious going on

Benchmarks are responsible for blowing smoke and wherever there is smoke, there is fire. The competitive benchmarks by Hortonworks and Cloudera are the reasons why SQL remains table stakes for big data platforms Hadoop.
Further benchmarks can also be viewed as self service exercises that are used by vendors in their own favor. But benchmarks published by Hortonworks and Cloudera for SQL on Hadoop engines points out that something really serious is going on. In the era of technology advancements like Hype and Hive, SQL continues to be table stakes for various Hadoop platforms.
Obviously, SQL on Hadoop can perform effective machine learning, data analysis through social graphs, data streaming or data transformation into meaningful insights. But most important question that is usually asked by most of the organizations when implementing Hadoop is how faster interactive SQL can work?
For many organizations, usage of Hadoop platform only for SQL queries seems complete waste and it should be will integrated with R or Python languages. At the same time, it is satisfying large BI crowd too because for many industries SQL still remains gateway drug to Hadoop. For the small or medium sized businesses that wanted to run analytics on affordable Hadoop clusters, there is fully compliant SQL system running successfully on big data analytics platforms.
There are always competitive benchmarks published from time to time that will give you an idea who afraid of whom. For example, Cloudera is afraid of Amazon. Similarly SQL on Hadoop engines are afraid of Cloudera Impala that is performing up to ten times faster and even better.
Cloudera is an effective database management system where data is distributed over multiple storage locations and easy to access by users. This is obvious that Cloudera is performing better than SQL on Hadoop engines due to its storage architecture and advance storage techniques. If you wanted to achieve better consistency, supreme concurrency, and faster execution of queries then this is the right to switch for better database system instead of Hadoop.
CLoudera is not only getting faster but it knows that most of the customers are moving toward cloud technology today. They are always looking to adopt advance technologies that can help them the most. Amazon web services are getting immense popular today due to its remarkable storage structure and easy accessibility.
Cloudera is working with a single objective to deliver maximum performance to its customers and its performance is really competitive. Further, it has been proved more economical option and its deployment structure is also simpler. So, this is clear that Impala storage by Cloudera benchmarks SQL on Hadoop engines.
During past times, When Cloudera was deployed on AWS then it has to be configured manually. But now both Companies have joined hands together and deployment has become much simpler than earlier. Few months back, Hortonworks released Hive to outperform Impala and it is almost 100 times faster than existing big data platforms. Even most complex queries can be managed with ease and convenience.
Hive is latest cost based query optimizer that is especially designed to handle continuous workloads. Both Cloudera and Hortonworks benchmarks are the reflections how SQL on Hadoop engines point something serious is going on to buy-in Hadoop. 
Manchun Pandit is working in JanBask Training as a Digital Marketing Manager. We are providing Online SQL Server Training & Hadoop Training.
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