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Big data and computational modeling tools and technologies

To effectively handle and analyze big data and computationally intensive processes, various tools and technologies have emerged. Some of the tools that we use for this at Bates White include:  

  • Distributed file systems, such as Apache Hadoop's HDFS provide efficient storage, retrieval, and parallel processing of data across clusters of machines. 
  • Data processing frameworks like Apache Spark offer high-speed, in-memory analytics capabilities, facilitating distributed processing and supporting multiple programming languages.
  • Cloud Computing, such as Amazon Web Services and Azure provide scalability for storage, data processing, and modeling of with big data.
  • High-performance computing (HPC) software parallelizes computationally intensive models and simulations. We evaluate out-of-sample model predictions with HPC and leverage this tool for bootstrapping, where we run tens of thousands of simulations to evaluate confidence intervals of a statistical model.

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