![]() ![]() NATO has already begun testing quantum-safe solutions to investigate the feasibility and practicality of such technology for real-world implementations while the US National Institute of Standards and Technology (NIST) launched a competition to identify and standardize quantum-safe encryption algorithms. ![]() Organizations, technology providers, and internet standards will therefore soon be required to transition to quantum-safe encryption. Security experts and scientists predict that quantum computers will one day be able to break commonly used encryption methods rendering email, secure banking, cryptocurrencies, and communications systems vulnerable to significant cybersecurity threats. IBM also unveiled the Quantum Safe Roadmap to guide industries along their journey to post-quantum cryptography. Announced at its annual Think conference in Orlando, Florida, Quantum Safe technology combines expertise across cryptography and critical infrastructure to address the potential future security risks that quantum computing poses, according to the company. You can use IBM SMF Explorer stand-alone in Python scripts or use it with the provided JupyterLab setup.Technology giant IBM has debuted a new set of tools and capabilities designed as an end-to-end, quantum-safe solution to secure organizations and governmental agencies as they head toward the post-quantum computing era. Thanks to the convenient interface to access SMF data using Python provided by IBM SMF Explorer, you can retrieve SMF data in tabular form, which can further be processed for the task of data analysis and machine learning. Novice users like system programmers, data scientists and data engineers might be struggling when trying to understand and interpret SMF data if they are still not acquainted with z/OS. System Management Facility (SMF) records represent a wealth of information that can be extracted to get insights into the activities of your z/OS systems. Python is relatively easy to learn, and if you are used to scripting languages or programming in general you might find the Tutorial Jupyter Notebooks provided with IBM SMF Explorer sufficient to learn Python on the go.Īdditionally you may find very good publicly available guides and other resources to get you started with Python (e.g. In addition to the setup itself, fundamental Python knowledge and a basic understanding of the Pandas library is helpful to get you started. There are some technical requirements that are listed below. JupyterLab is a web-based interface to execute Python interactively and makes data visualization and handling easy.įor that reason the IBM SMF Explorer Github repository provides you with a JupyterLab environment to get started quickly. system utilization, LPAR utilization, cache statistics, …).Īn easy way to use IBM SMF Explorer is through JupyterLab. The framework enables easy SMF data record fetching, provides information on selected SMF fields, and serves chunks of SMF data, suitable for different analysis types, that can be selected for further processing.įor various SMF records and subtypes, sets of SMF fields are provided to make getting relevant data even easier (e.g. ![]() IBM SMF Explorer is a Python library, which means that you can use it to write scripts, embed it into other applications or just fetch data interactively.Īdditionally the Python ecosystem provides access to a large set of libraries for visualization, data analysis, machine learning and many more. The framework uses the z/OS® Data Gatherer: SMF Data REST Services to fetch data from a z/OS host. IBM® SMF Explorer is a Python framework to access SMF data directly from dump data sets. How to use Mapping and Samples Documentation ![]()
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