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@eon01 published a course, 3 weeks, 5 days ago
Founder, FAUN.dev

GitOps the Hard Way, with Argo CD

Argo CD Kubernetes GitLab GitLab CI/CD Helm

Build Real GitOps Pipelines From Empty Clusters to Automated Deploys

GitOps the Hard Way, with Argo CD
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@laura_garcia shared a post, 3 weeks, 6 days ago
Software Developer, RELIANOID

𝗥𝗘𝗟𝗜𝗔𝗡𝗢𝗜𝗗 𝗮𝘁 𝗟𝗼𝗻𝗱𝗼𝗻 𝗧𝗲𝗰𝗵 𝗪𝗲𝗲𝗸 𝟮𝟬𝟮𝟲

🚀 𝗝𝗼𝗶𝗻 𝗥𝗘𝗟𝗜𝗔𝗡𝗢𝗜𝗗 𝗮𝘁 𝗟𝗼𝗻𝗱𝗼𝗻 𝗧𝗲𝗰𝗵 𝗪𝗲𝗲𝗸 𝟮𝟬𝟮𝟲 📅 June 8–12, 2026 📍 London, United Kingdom London Tech Week brings together innovators, enterprises, startups, investors, and technology leaders to explore the future of AI, cybersecurity, cloud infrastructure, digital transformation, and emerging technologi..

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@eon01 shared a post, 4 weeks, 1 day ago
Founder, FAUN.dev

AWX in Action is out, and there's a course

Ansible AWX

"AWX in Action: Ansible Orchestration at Scale" is now available in print and ebook. It covers running AWX on Kubernetes for real, not a sandbox demo that falls over the moment you add a second execution node.

AWX in Action - Ansible Orchestration at Scale
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@varbear shared a link, 4 weeks, 1 day ago
FAUN.dev()

Design Patterns Are Dead. Long Live Design Patterns.

Design patterns were created for human comprehension, not machines, serving as a shared vocabulary to communicate complex ideas quickly, manage working memory, and standardize solutions. Even in the era of AI-generated code, design patterns are crucial for containing the limitations of AI models and.. read more  

BigQuery is a cloud-native, serverless analytics platform designed to store, query, and analyze massive volumes of structured and semi-structured data using standard SQL. It separates storage from compute, automatically scales resources, and eliminates the need for infrastructure management, indexing, or capacity planning.

BigQuery is optimized for analytical workloads such as business intelligence, log analysis, data science, and machine learning. It supports real-time data ingestion via streaming, batch loading from cloud storage, and federated queries across external data sources like Cloud Storage, Bigtable, and Google Drive.

Query execution is distributed and highly parallel, enabling interactive performance even on petabyte-scale datasets. The platform integrates deeply with the Google Cloud ecosystem, including Looker for BI, Vertex AI for ML workflows, Dataflow for streaming pipelines, and BigQuery ML, which allows users to train and run machine learning models directly using SQL.

Built-in security features include fine-grained IAM controls, column- and row-level security, encryption by default, and audit logging. BigQuery follows a consumption-based pricing model, charging for storage and queries (on-demand or reserved capacity).