Join us

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

Top 10 best practices for Amazon EMR Serverless

Amazon EMR Serverless allows users to run big data analytics frameworks without managing clusters, integrating with various AWS services for a comprehensive solution. The top 10 best practices for optimizing EMR Serverless workloads focus on performance, cost, and scalability, including consideratio.. read more  

Top 10 best practices for Amazon EMR Serverless
Link
@devopslinks shared a link, 3 weeks, 3 days ago
FAUN.dev()

AI Isn't Replacing SREs. It's Deskilling Them.

This post discusses the impact of AI on the role of Site Reliability Engineers (SREs) by drawing parallels to historical research on automation. It highlights the risk of deskilling and never-skilling for SREs who heavily rely on AI tools for incident response. The post also suggests potential appro.. read more  

Link
@devopslinks shared a link, 3 weeks, 3 days ago
FAUN.dev()

Building a Database on S3

This paper from 2008 proposes a shared-disk design over Amazon S3 for cloud-native databases, separating storage from compute. Clients write redo logs to Amazon SQS instead of directly to S3 to hide latency. The paper presents a blueprint for serverless databases before the term existed... read more  

Link
@devopslinks shared a link, 3 weeks, 3 days ago
FAUN.dev()

Introducing Agentic Observability in NGINX: Real-time MCP Traffic Monitoring

NGINX ships an open-sourceAgentic ObservabilityJS module. It parsesMCPtraffic and extracts tool names, error statuses, and client/server identities. The module uses nativeOpenTelemetryto export spans. A Docker Compose reference wires upOTel collector,Prometheus, andGrafanafor realtime throughput, la.. read more  

Introducing Agentic Observability in NGINX: Real-time MCP Traffic Monitoring
Link
@devopslinks shared a link, 3 weeks, 3 days ago
FAUN.dev()

AWS RDS Cost Optimization Guide: Cut Database Costs in 2026

Amazon RDS costs are not fixed - they vary based on configuration and usage. Making informed configuration and governance decisions is key to optimizing costs. Graviton instances offer better price-performance for common databases, while storage costs can be reduced by decoupling performance from ca.. read more  

AWS RDS Cost Optimization Guide: Cut Database Costs in 2026
Course
@eon01 published a course, 3 weeks, 3 days ago
Founder, FAUN.dev

Learn Git in a Day

GitLab git Ubuntu

Everything you need, nothing you don't

Learn Git in a Day
 Activity
Story Palark Team Trending
@shurup shared a post, 3 weeks, 6 days ago
@palark

Kubernetes best practices for DevOps engineers

Kubernetes

Have to manage Kubernetes in production but don’t feel confident about its many moving parts, complex architecture, and configurations? Here’s a selection of technical guides from experienced engineers for Kubernetes beginners looking to master this orchestration tool for running containerised apps efficiently and reliably.

Best practices for Kubernetes
Link
@pramod_kumar_0820 shared a link, 3 weeks, 6 days ago
Software Engineer, Teknospire

⚡ Why Your Spring Boot API Takes 3 Seconds to Respond (And How to Fix It)

A practical breakdown of the most common Spring Boot performance bottlenecks — and how we optimized our API from 3 seconds to 200 ms.

News FAUN.dev() Team Trending
@devopslinks shared an update, 3 weeks, 6 days ago
FAUN.dev()

Microsoft Project Silica: Your Data, Stored in a Pyrex Dish, for 10,000 Years

Microsoft's Project Silica encodes data in borosilicate glass using femtosecond lasers, offering long-term storage for up to 10,000 years. This method overcomes traditional storage limitations and is cost-effective, though write speed remains a challenge. The research phase is complete, but no product release has been announced.

Microsoft Project Silica: Your Data, Stored in a Pyrex Dish, for 10,000 Years
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).