Updates and recent posts about BigQuery..

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@devopslinks shared a link, 2 months, 1 week ago

FAUN.dev()

How We Saved 70% of CPU and 60% of Memory in Refinery’s Go Code, No Rust Required.

Refinery 3.0 cuts CPU by 70% and slashes RAM by 60%. The trick: selective field extraction from serialized spans. No full deserialization. Fewer heap allocations. Way less waste. It also recycles buffers, handles metrics smarter, and is gearing up to parallelize its core decision loop... read more

News FAUN.dev() Team

@kaptain shared an update, 2 months, 1 week ago

FAUN.dev()

Docker Brings Production-Grade Hardened Images to Developers at No Cost

#Complia... #Docker ... #supply-... #Securit... #docker

Docker has launched Docker Hardened Images, a secure and minimal set of production-ready images. These images are now freely available to developers.

Docker Brings Production-Grade Hardened Images to Developers at No Cost

Link

@anjali shared a link, 2 months, 1 week ago

Customer Marketing Manager, Last9

OTel Updates: OpenTelemetry Deprecates Zipkin Exporters

OpenTelemetry deprecates Zipkin exporters in favor of native OTLP support. Migration paths and timeline through December 2026.

News FAUN.dev() Team

@kaptain shared an update, 2 months, 1 week ago

FAUN.dev()

Argo CD 3.2.2 Improves Secret Management, Retry Safety, and Auth Checks

#Securit... #gitops #argocd #Argo CD #kuberne...

ArgoCD v3.2.2 has been released, featuring a new addition, two enhancements, and a bug fix. This update aims to improve the overall functionality and reliability of the platform.

Argo CD 3.2.2 Improves Secret Management, Retry Safety, and Auth Checks

News FAUN.dev() Team

@devopslinks shared an update, 2 months, 1 week ago

FAUN.dev()

Rust Confirmed for Linux Kernel: Experiment Concludes Successfully

#Rust #linux #unix #Linux k... #The Rus...

The Rust experiment in the Linux kernel concludes, confirming its suitability and permanence in kernel development, with Rust now used in production and supported by major Linux distributions.

Rust Confirmed for Linux Kernel: Experiment Concludes Successfully

Course

@eon01 published a course, 2 months, 1 week ago

Founder, FAUN.dev

Generative AI For The Rest Of US

#AI #Generat... #LLM #Large L... #gpt

Your Future, Decoded

News FAUN.dev() Team

@kaptain shared an update, 2 months, 1 week ago

FAUN.dev()

Kubernetes v1.35 Timbernetes Release: 60 Enhancements

#k8s #Timbern... #cgroups #docker #kuberne...

Kubernetes v1.35, the Timbernetes Release, debuts with 60 enhancements, including stable in-place Pod updates and beta features for workload identity and certificate rotation.

Kubernetes v1.35 Timbernetes Release: 60 Enhancements

Activity

@kaptain added a new tool Kubernetes Gateway API , 2 months, 1 week ago.

News FAUN.dev() Team

@kala shared an update, 2 months, 1 week ago

FAUN.dev()

Google Releases Magika 1.0: AI File Detection in Rust

#Rust #Generat... #file ty... #Magika #AI

Google releases Magika 1.0, an AI file detection system rebuilt in Rust for improved performance and security.

Google Releases Magika 1.0: AI File Detection in Rust

Activity

@kala added a new tool Magika , 2 months, 1 week ago.

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).