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.