Infrastructure-as-Code (IaC), an important component of cloud computing, allows the definition of cloud infrastructure in high-level programs.

Infrastructure-as-Code (IaC), an important component of cloud computing, allows the definition of cloud infrastructure in high-level programs.

Many large enterprises that operate highly governed and complex ICT environments have no efficient and effective way to support their Data and AI teams in rapidly spinning up and tearing down self-service data and compute infrastructure, to experiment with new data analytic tools, and deploy data products into operational use. This paper proposes a key piece of the solution to the overall problem, in the form of an on-demand self-service data-platform infrastructure to empower de-centralised data teams to build data products on top of centralised templates, policies and governance. The core innovation is an efficient method to leverage immutable container operating systems and infrastructure-as-code methodologies for creating, from scratch, vendor-neutral and short-lived Kubernetes clusters on-premises and in any cloud environment. Our proposed approach can serve as a repeatable, portable and cost-efficient alternative or complement to commercial Platform-as-a-Service (PaaS) offerings, and this is particularly important in supporting interoperability in complex data mesh environments with a mix of modern and legacy compute infrastructure.

As the number and sophistication of cyber attacks have increased, threat hunting has become a critical aspect of active security, enabling proactive detection and mitigation of threats before they cause significant harm. Open-source cyber threat intelligence (OS-CTI) is a valuable resource for threat hunters, however, it often comes in unstructured formats that require further manual analysis. Previous studies aimed at automating OSCTI analysis are limited since (1) they failed to provide actionable outputs, (2) they did not take advantage of images present in OSCTI sources, and (3) they focused on on-premises environments, overlooking the growing importance of cloud environments. To address these gaps, we propose LLMCloudHunter, a novel framework that leverages large language models (LLMs) to automatically generate generic-signature detection rule candidates from textual and visual OSCTI data. We evaluated the quality of the rules generated by the proposed framework using 12 annotated real-world cloud threat reports. The results show that our framework achieved a precision of 92% and recall of 98% for the task of accurately extracting API calls made by the threat actor and a precision of 99% with a recall of 98% for IoCs. Additionally, 99.18% of the generated detection rule candidates were successfully compiled and converted into Splunk queries.

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AWS customers are relying on Infrastructure as Code (IaC) to design, develop, and manage their cloud infrastructure. IaC ensures that customer infrastructure and services are consistent, scalable, and reproducible, while being able to follow best practices in the area of development operations (DevOps). One possible approach to manage AWS infrastructure and services with IaC is Terraform, which allows developers to organize their infrastructure in reusable code modules. This aspect is increasingly gaining importance in the area of machine learning (ML). Developing and managing ML pipelines, including training and inference with Terraform as IaC, lets you easily scale for multiple ML use cases or Regions without having to develop the infrastructure from scratch.

Modern Data Stack (MDS) is a stack of technologies that makes a modern data warehouse perform 10–10,000x better than a legacy data warehouse. Ultimately, an MDS saves time, money, and effort. The four pillars of an MDS are a data connector, a cloud data warehouse, a data transformer, and a BI & data exploration tool. Easy integration is made possible with managed and open-source tools that pre-build hundreds of ready-to-use connectors. What used to take a team of data engineers to build and maintain regularly can now be replaced with a tool for simple use cases.

Frameworks and libraries can be said as the fundamental building blocks when developers build software or applications. These tools help in opting out the repetitive tasks as well as reduce the amount of code that the developers need to write for a particular software. Recently, the Stack Overflow Developer Survey 2020 surveyed nearly 65,000 developers, where they voted their go-to tools and libraries. Here, we list down the top 12 frameworks and libraries from the survey that are most used by developers around the globe in 2020. About: Originally developed by researchers of Google Brain team, TensorFlow is an end-to-end open-source platform for machine learning.

Like most 12-year-old boys, Mitchell Hashimoto played a lot of videogames. But he never liked the repetitive parts of games like Neopets, where players feed and care for virtual animals. "I used a lot of bot software that other people wrote to play the more mundane parts for me, so I could do the fun stuff," he says. Those bots were often blocked by gamemakers, so Hashimoto taught himself to program and created his own bot. When the creators of Neopets ordered him to stop using that bot, he was done with the game.

A 2017 IDC White Paper "recommend[s] that organizations that want to get the most out of cloud should train a wide range of stakeholders on cloud fundamentals and provide deep training to key technical teams" (emphasis ours). Regular readers of the Cloud Academy blog know we've been talking about this for a long time. Future-proofing your organization requires technical excellence, collective experience, business context, and shared understanding. Cloud Academy's latest Learning Paths go broad and deep--covering CI/CD, machine learning, AI, big data, and even preparation for the first AWS certification designed for non-technical staff. DevOps and IT professionals managing infrastructure across public, private, and hybrid clouds can use this learning path to get started with Terraform.