The tech industry faces a paradox: despite high demand for skills, many developers and engineers are unemployed. At KubeCon + CloudNativeCon North America in Salt Lake City, Utah, Andela and the Cloud Native Computing Foundation (CNCF) announced an initiative to train 20,000 technologists in cloud native computing over the next decade. oss O'neill, Senior Program Manager at Andela and Chris Aniszczyk, CNCF’s CTO, highlighted the lack of Kubernetes-certified professionals in regions like Africa and emphasized the need for global inclusivity to make cloud native technology ubiquitous.Andela, operating in over 135 countries and founded in Nigeria, views this program as a continuation of its mission to upskill African talent, aligning with its partnerships with tech giants like Google, AWS, and Nvidia. This initiative also addresses the increasing employer demand for Kubernetes and modern cloud skills, reflecting a broader skills mismatch in the tech workforce.Aniszczyk noted that companies urgently seek expertise in cloud native infrastructure, observability, and platform engineering. The partnership aims to bridge these gaps, offering opportunities to meet evolving global tech needs.Learn more from The New Stack about developer talent, skills and needs: Top Developer Skills for AI and Cloud Jobs5 Software Development Skills AI Will Render ObsoleteCloud Native Skill Gaps are Killing Your GainsJoin our community of newsletter subscribers to stay on top of the news and at the top of your game.  

When open source projects shift to proprietary licensing, forks and new communities often emerge. Such was the case with MapLibre, born from Mapbox’s 2020 decision to make its map rendering engine proprietary. In conjunction with All Things Open 2024, Seth Fitzsimmons, a principal engineer at AWS and Tarus Balog, principal technical strategist for open source at AWS shared that this engine, popular for its WebGL-powered vector maps and dynamic customization features, was essential for organizations like BMW, The New York Times, and Instacart. However, Mapbox’s move disappointed its open-source user base by tying the upgraded Mapbox GL JS library to proprietary products.In response, three users forked the engine to create MapLibre, committing to modernizing and preserving its open-source ethos. Despite challenges—forking often struggles to sustain momentum—MapLibre has thrived, supported by contributors and corporate sponsors like AWS, Meta, and Microsoft. Notably, a community member transitioned the project from JavaScript to TypeScript over nine months, showcasing the dedication of unpaid contributors.Thanks to financial backing, MapLibre now employs maintainers, enabling it to reciprocate community efforts while fostering equality among participants. The project illustrates the resilience of open-source communities when proprietary shifts occur.Learn more from The New Stack about forking open source projects:Why Do Open Source Projects Fork?OpenSearch: How the Project Went From Fork to FoundationJoin our community of newsletter subscribers to stay on top of the news and at the top of your game. 

Anandhi Bumstead, Director of Software Engineering at AWS, shares insights on OpenSearch’s fascinating evolution from a fork of Elasticsearch to a project under the Linux Foundation. She discusses the importance of neutral governance and community engagement, enhancing its versatility across analytics and security. Performance boosts are notable, with the latest release showing 6.5x faster query performance. Anandhi emphasizes the need for open source innovation, calling for community contributions to further elevate OpenSearch’s capabilities, especially in AI integration.

Is Apache Spark too costly? Amazon Principal Engineer Patrick Ames tackled this question during an interview with The New Stack Makers, sharing insights into transitioning from Spark to Ray for managing large-scale data. Ames, described as a "go-to" engineer for exabyte-scale projects, emphasized a goal-driven approach to solving complex engineering problems, from simplifying daily chores to optimizing software solutions.Initially, Spark was chosen at Amazon for its simplicity and open-source flexibility, allowing efficient merging of data with minimal SQL code. The team leveraged Spark in a decoupled architecture over S3 storage, scaling it to handle thousands of jobs daily. However, as data volumes grew to hundreds of terabytes and beyond, Spark’s limitations became apparent. Long processing times and high costs prompted a search for alternatives.Enter Ray—a unified framework designed for scaling AI and Python applications. After experimentation, Ames and his team noted significant efficiency improvements, driving the shift from Spark to Ray to meet scalability and cost-efficiency needs.Learn more from The New Stack about Apache Spark and Ray: Amazon to Save Millions Moving From Apache Spark to RayHow Ray, a Distributed AI Framework, Helps Power ChatGPT Join our community of newsletter subscribers to stay on top of the news and at the top of your game. 

In this New Stack Makers, Codiac aims to simplify app deployment on Kubernetes by offering a unified interface that minimizes complexity. Traditionally, Kubernetes is powerful but challenging for teams due to its intricate configurations and extensive manual coding. Co-founded by Ben Ghazi and Mark Freydl, Codiac provides engineers with infrastructure on demand, container management, and advanced software development life cycle (SDLC) tools, making Kubernetes more accessible.Codiac’s interface streamlines continuous integration and deployment (CI/CD), reducing deployment steps to a single line of code within CI/CD pipelines. Developers can easily deploy, manage containers, and configure applications without mastering Kubernetes' esoteric syntax. Codiac also offers features like "cabinets" to organize assets across multi-cloud environments and enables repeatable processes through snapshots, making cluster management smoother.For experienced engineers, Codiac alleviates the burden of manually managing YAML files and configuring multiple services. With ephemeral clusters and repeatable snapshots, Codiac supports scalable, reproducible development workflows, giving engineers a practical way to manage applications and infrastructure seamlessly across complex Kubernetes environments.Learn more from The New Stack about deploying applications on Kubernetes:Kubernetes Needs to Take a Lesson from Portainer on Ease-of-Use Three Common Kubernetes Challenges and How to Solve Them Join our community of newsletter subscribers to stay on top of the news and at the top of your game. 

Valkey, an open-source fork of Redis launched in March, introduced its multithreaded Version 8.0 in September, now available through AWS ElastiCache. At All Things Open 2024 in Raleigh, AWS's Kyle Davis explains that Valkey was developed after Redis changed to a restrictive license, drawing contributors from companies like AWS, Google, Alibaba, and Oracle. Notably, some contributors emerged independently, including a significant contributor from Vietnam. Version 8.0 differentiates itself from Redis by leveraging multithreaded CPUs, addressing the efficiency of I/O operations in modern hardware. Additionally, data structure refinements were made to improve memory efficiency by up to 20%, particularly benefiting large-key databases.Looking ahead, Valkey plans two annual updates, with the next release expected in 2025. New modules are anticipated, including a JSON module for efficient data manipulation and a Bloom filter for probabilistic data presence checks. Version 9.0 may bring substantial changes to clustering, updating it to better leverage modern technologies. The Valkey project aims to continue evolving its capabilities to meet the demands of advanced data storage needs.Learn more from The New Stack about Valkey: Valkey Is a Different Kind of Fork AWS Adds Support, Drops Prices, for Redis-Forked Valkey Valkey: A Redis Fork With a Future Join our community of newsletter subscribers to stay on top of the news and at the top of your game.  

Deb Nicholson, executive director of the Python Software Foundation, attributes Python’s popularity to its minimal syntactical complexity, which appeals to beginners and seasoned developers alike. Python allows flexibility for those exploring coding without a specific focus, unlike purpose-built languages. Since her leadership began in 2022, Nicholson has overseen the foundation’s role in managing Python’s fiscal and operational needs, including the package index that hosts over half a million add-ons. This open ecosystem enables contributions from large corporations and individual developers while demanding vigilant security measures.Nicholson envisions Python's future advancements, particularly in improving multi-threading and expanding usage in mobile development. She acknowledges Python’s critical role in AI and data science but remains cautious about AI’s pervasive application, likening it to a temporary trend. On open source in the enterprise, Nicholson critiques companies profiting from open-source tools while adopting restrictive licenses. Instead, she admires models like Red Hat’s, which leverage open source sustainably without compromising accessibility or innovation.Learn more from The New Stack about Python: Python 3.13: Blazing New Trails in Performance and ScaleThe Top 5 Python Packages and What They DoPython Mulls a Change in Version NumberingJoin our community of newsletter subscribers to stay on top of the news and at the top of your game.

Priyanka Sharma, Executive Director of the Cloud Native Computing Foundation, teams up with Joseph Sandoval, Principal Product Manager at Adobe, to discuss the exciting intersection of platform engineering and AI. They highlight how AI will transform platform operations, aiming for better efficiency and user-centered designs. The conversation covers the upcoming KubeCon event, the role of open-source initiatives like CNOE, and essential reference architectures for guiding developers. Explore the growing maturity of platform engineering amidst changing technological landscapes.

Rohit Choudhary, co-founder and CEO of Acceldata, is a visionary in enterprise data observability. He discusses the exponential growth of data driven by generative AI and the complexities of multicloud environments. Choudhary emphasizes the importance of traceability in data management, especially with unstructured data on the rise. He also highlights the challenge of alert fatigue and advocates for improved alert systems in critical sectors like finance and healthcare, showcasing how these issues can shape future data strategies.

Joel Marcey, Director of Technology at the Rust Foundation, shares insights on Rust's remarkable rise as a programming language, praised for its speed and safety. He discusses its ambitious expansion into sectors like game development and embedded systems while underlining its growing role in web applications through WebAssembly. Marcey also explores Rust’s competitive edge over Go in performance-critical tasks, highlights recent updates like Rust 1.81, and reveals government interest in enhancing security initiatives within the Rust ecosystem.