Join us

ContentUpdates and recent posts about BigQuery..
Link
@faun shared a link, 3 months, 4 weeks ago
FAUN.dev()

Evolving Kubernetes for generative AI inference

Google Cloud, ByteDance, and Red Hat are wiring AI smarts straight intoKubernetes. Think: faster inference benchmarks, smarter LLM-aware routing, and on-the-fly resource juggling—all built to handle GenAI heat. Their new push,llm-d, bakesvLLMdeep into Kubernetes. That unlocks disaggregated serving .. read more  

Evolving Kubernetes for generative AI inference
Link
@faun shared a link, 3 months, 4 weeks ago
FAUN.dev()

v1.34: Of Wind & Will (O' WaW)

Kubernetes v1.34 drops with58 updates, and23 just hit stable. Highlights: Dynamic Resource Allocation (DRA), per-Pod resource limits, and secure image pulls using Pod-specific ServiceAccount tokens. Scalability gets a lift from streaming list responses. Security tightens with finer anonymous auth r.. read more  

v1.34: Of Wind & Will (O' WaW)
Link
@faun shared a link, 3 months, 4 weeks ago
FAUN.dev()

kube-bench Tutorial: Features, Use Cases, How It Works

kube-benchjust leveled up. Aqua Security’s CIS compliance scanner now snaps into CI/CD, runs pre-deploy checks, and helps dig through forensics after incidents. It plays nice with managed K8s—EKS, AKS, GKE—and handles custom YAML test suites if you’re going off the beaten path. Reports land in stru.. read more  

kube-bench Tutorial: Features, Use Cases, How It Works
Link
@faun shared a link, 3 months, 4 weeks ago
FAUN.dev()

An introduction to platform engineering

Platform engineering is stepping in where DevOps didn’t quite land. Think fewer duct-taped pipelines, more thoughtful systems. The fix? Internal Developer Platforms (IDPs), usually riding on Kubernetes, built to tame the sprawl. Gartner says 80% of big engineering orgs will run platform teams by 20.. read more  

An introduction to platform engineering
Link
@faun shared a link, 3 months, 4 weeks ago
FAUN.dev()

Battle for Resources or the SSA Path to Kubernetes Diplomacy

A full-stack engineer and systems architect with hands-on time incloudandIoT, building real-world tools for theoil and gas sector. Think connected rigs, smart pipelines, and infrastructure that doesn’t flinch at scale. Market signal:Industrial tech’s going deep. Cloud and IoT aren’t side projects a.. read more  

Link
@faun shared a link, 3 months, 4 weeks ago
FAUN.dev()

Kubernetes in an AI-Native World: Can It Stay Relevant?

At KubeCon + CloudNativeCon Hyderabad 2025, CNCF leads made it clear:cloud-native infraisn’t just supporting AI—it’s becoming its backbone. The conversation’s moved on from“Can Kubernetes run AI?”to“How does it evolve for AI-first everything?”.. read more  

Kubernetes in an AI-Native World: Can It Stay Relevant?
Link
@faun shared a link, 3 months, 4 weeks ago
FAUN.dev()

Kubernetes v1.34 brings networking refinements for cloud-native infrastructure

Kubernetes 1.34 comes packed withnetworking upgradesbuilt for scale. Less overhead. Fewer headaches. Easier to run big clusters without sweating packet flows. This triannual release keeps pushing the envelope for both cloud-native setups and the on-prem diehards... read more  

Story
@laura_garcia shared a post, 4 months ago
Software Developer, RELIANOID

💡 What is a VIP Load Balancer?

AVIP (Virtual IP)load balancer distributes traffic across multiple servers using a single IP. It ensures: ✅ Scalability ✅ High availability ✅ Session persistence ✅ Smart traffic routing 🚀RELIANOIDtakes VIP load balancing to the next level with: 🔒 SSL offloading 📊 Dynamic health monitoring ⚖️ Advance..

Knowledge base VIP LOAD BALANCER
Link
@anjali shared a link, 4 months ago
Customer Marketing Manager, Last9

A Practical Guide to Python Application Performance Monitoring(APM)

Monitor, debug, and optimize Python apps in production with APM—track transactions, DB queries, errors, and external calls.

python_apm
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).