Take advantage of Open Source Load Balancer to elevate your business security and IT infrastructure with a custom ADC Solution.
Boost application security and continuity with SKUDONET ADC, our Open Source Load Balancer, that maximizes IT infrastructure flexibility. Additionally, save up to $470 K per incident with AI and SKUDONET solutions, further enhancing your organization’s risk management and cost-efficiency strategies.
ConnectWise Identify is a powerful cybersecurity risk assessment platform offering strategic cybersecurity assessments and recommendations.
When it comes to cybersecurity, what your clients don’t know can really hurt them. And believe it or not, keep them safe starts with asking questions. With ConnectWise Identify Assessment, get access to risk assessment backed by the NIST Cybersecurity Framework to uncover risks across your client’s entire business, not just their networks. With a clearly defined, easy-to-read risk report in hand, you can start having meaningful security conversations that can get you on the path of keeping your clients protected from every angle. Choose from two assessment levels to cover every client’s need, from the Essentials to cover the basics to our Comprehensive Assessment to dive deeper to uncover additional risks. Our intuitive heat map shows you your client’s overall risk level and priority to address risks based on probability and financial impact. Each report includes remediation recommendations to help you create a revenue-generating action plan.
hewies user interface - 3D scientific visualisation tool
Python project with goal to provide FOSS library to extract, analyse and visualise data in a 3D fashion.
The instance will connect to a data source, ods sheet, csv, sql DB, pyodbc
the instance will analyse and/or transform the data to be presented to the visualisation functionality
the instance will visualise the data in a 3D fashion, likely using third party FOSS
A Reproducible Data Analysis Workflow with R Markdown, Git, Make, etc.
In this tutorial, we describe a workflow to ensure long-term reproducibility of R-based data analyses. The workflow leverages established tools and practices from software engineering. It combines the benefits of various open-source software tools including R Markdown, Git, Make, and Docker, whose interplay ensures seamless integration of version management, dynamic report generation conforming to various journal styles, and full cross-platform and long-term computational reproducibility....