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Network World - For some time Microsoft didn't offer a solution for processing big data in cloud environments. SQL Server is good for storage, but its ability to analyze terabytes of data is limited. Hadoop, which was designed for this purpose, is written in Java and was not available to .NET developers. So, Microsoft launched the Hadoop on Windows Azure service to make it possible to distribute the load and speed up big data computations.
But it is hard to find guides explaining how to work with Hadoop on Windows Azure, so here we present an overview of two out-of-the-box ways of processing big data with Hadoop on Windows Azure and compare their performance.
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IN PICTURES: 'The Human Face of Big Data'
We created eight types of queries in both languages and measured how fast they were processed.
A data set was generated based on US Air Carrier Flight Delays information downloaded from Windows Azure Marketplace. It was used to test how the system would handle big data. Here, we present the results of the following four queries:
From this analysis you will see performance results tests and observe how the throughput varies depending on the block size. The research contains a table and three diagrams that demonstrate the findings.
As a testing environment we used a Windows Azure cluster. The capacities of its physical CPU were divided among three virtual machines that served as nodes. Obviously, this could introduce some errors into performance measurements. Therefore we launched each query several times and used the average value for our benchmark. The cluster consisted of three nodes (a small cluster). The data we used for the tests consisted of five CSV files of 1.83GB each. In total, we processed 9.15GB of data. The replication factor was equal to three. This means that each data set had a synchronized replica on each node in the cluster.
The speed of data processing varied depending on the block size -- therefore, we compared results achieved with 8MB, 64MB and 256MB blocks.
The table below contains test results for the four queries. (The information on processing other queries depending on the size of HDFS block is available in the full version of the research.)