Scientific Data Analytics on Cloud

Cloud and Big Data platform supporting advanced analytics of HDF5 data

What We Do

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HDF5-Cloud Converter and Visualizer

* Problem - Scientific data are most often stored in self-describing portable data formats like HDF (Hierarchical Data Format) and NetCDF. Big Data Systems do not support these scientific data formats.

* Solution - Decision Rocket has developed Cloud and Big data based Product and Solution, enabling scientific community to seamlessly visualize vast amount of scientific data interactively

* Cost - Compared to HPC costs, the distributed parallel computing architecture along with Cloud elasticity can be leveraged to lower the cost and perform advanced analytics on scientific data. Cloud elasticity helps reduce the cost as resources can be increased only at the time of analysis.

Scientific Data Analytics on Cloud

Scientific data are most often stored in self-describing portable data formats like HDF (Hierarchical Data Format) and NetCDF. Big Data Systems do not support these scientific data formats. For example, HDF5 raw data loaded into HDFS cannot be processed by MapReduce programs. Scientific users who want to analyze the raw data in its entirety, have limitations. Decision Rocket has developed Cloud and Big data based Product and Solution, enabling scientific community to seamlessly visualize vast amount of scientific data interactively. Any amount of data can be loaded into Cloud using our HDF5-Cloud Converter and Visualizer. Once data is converted and loaded, the entire data set is available for further analytics, Apache Spark and R can be used to run machine learning algorithms on the converted data. The distributed parallel computing architecture along with Cloud elasticity can be leveraged to do advanced analytics on scientific data. Cloud elasticity helps reduce the cost as resources can be increased only at the time of analysis.

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Run Machine Learning Algorithms on HDF5 Data

Conversion and Migration of HDF5 data to Cloud, involves bringing various technologies and its capabilities together. Any amount of data can be loaded into Cloud using our HDF5-Cloud Converter and Visualizer. Apache Spark or R can be used to run Machine Learning (ML) algorithms on the converted data for further analytics.

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