Updates and recent posts about BigQuery..

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@laura_garcia shared a post, 2 months, 2 weeks ago

Software Developer, RELIANOID

𝗝𝗮𝗽𝗮𝗻 𝗜𝗧 & 𝗗𝗫 𝗪𝗲𝗲𝗸!

🚀 𝗛𝗲𝗮𝗱𝗶𝗻𝗴 𝘁𝗼 𝗧𝗼𝗸𝘆𝗼 𝗳𝗼𝗿 𝗝𝗮𝗽𝗮𝗻 𝗜𝗧 & 𝗗𝗫 𝗪𝗲𝗲𝗸! 𝗥𝗘𝗟𝗜𝗔𝗡𝗢𝗜𝗗 will be at the 23rd Information Security Expo Spring 2026 from April 8–10 at Tokyo Big Sight – 𝗝𝗮𝗽𝗮𝗻’𝘀 𝗹𝗮𝗿𝗴𝗲𝘀𝘁 𝘀𝗵𝗼𝘄𝗰𝗮𝘀𝗲 𝗳𝗼𝗿 𝗰𝘆𝗯𝗲𝗿𝘀𝗲𝗰𝘂𝗿𝗶𝘁𝘆 𝘀𝗼𝗹𝘂𝘁𝗶𝗼𝗻𝘀. Come see how our advanced ADC and secure application delivery solutions help protect critical infr..

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@laura_garcia shared a post, 2 months, 2 weeks ago

Software Developer, RELIANOID

Maritime Cybersecurity Is Still Too Weak – And the Risks Are Growing

🚢 Maritime Cybersecurity Is Still Too Weak – And the Risks Are Growing As ships become smarter, greener, and more connected, their cyber defenses remain worryingly outdated. 📉 Over 80% of shipowners have faced cyberattacks in the past 3 years 💸 Average cost per attack: $3.1 million 🎣 Phishing causes..

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@pramod_kumar_0820 shared a link, 2 months, 2 weeks ago

Software Engineer, Teknospire

Java 26 Released 🚀: What’s New, What Matters & Why It’s Faster Than Ever

Java 26 (March 2026) is out, and while it’s not a headline-heavy release, it brings meaningful improvements where it counts — performance, networking, and concurrency.

Some notable updates:

🌐 HTTP/3 support (QUIC-based, lower latency, better reliability)

🧵 Structured Concurrency (Preview) for safer multithreading

⚡ JVM & GC optimizations improving startup and runtime performance

🧠 Continued evolution of pattern matching

🧪 Vector API (Incubator) for high-performance workloads

This release is less about flashy features and more about incremental improvements that impact real-world systems.

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@mmaksimovic shared a link, 2 months, 2 weeks ago

Monitoring Your App Without Running Your Own Prometheus Stack

When to use Prometheus and when to look for other solutions.

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@tellsaqib shared a link, 2 months, 2 weeks ago

How Cloudways is manages its 90K servers fleet using Agentic SRE

Scaling Autonomous Site Reliability Engineering: Architecture, Orchestration, and Validation for a 90,000+ Server Fleet

News FAUN.dev() Team

@kala shared an update, 2 months, 2 weeks ago

FAUN.dev()

Anthropic Asked 81,000 People What They Want From AI. Here's What They Said.

#Study #researc... #Anthrop... #Claude #AI

Anthropic's global AI study surveyed 80,508 participants across 159 countries, revealing desires for more personal time and concerns about AI's unreliability and job displacement. Sentiments vary regionally, with lower-income countries seeing AI as an equalizer, while Western Europe and North America focus on governance issues. The study highlights a complex mix of hope and fear regarding AI's impact.

Anthropic Asked 81,000 People What They Want From AI. Here's What They Said.

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@kala added a new tool Claude , 2 months, 2 weeks ago.

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@varbear shared a link, 2 months, 2 weeks ago

FAUN.dev()

How Slack Rebuilt Notifications

At Slack, notifications were redesigned to address the overwhelming noise issue by simplifying choices and improving controls. The legacy system had complex preferences that made it difficult for users to understand and control notifications. Through a collaborative effort, the team refactored prefe.. read more

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@varbear shared a link, 2 months, 2 weeks ago

FAUN.dev()

The Slow Collapse of MkDocs

On March 9, 2026 a former maintainer grabbed the PyPI package forMkDocs. The original author's rights got stripped. Ownership snapped back within six hours. Core development stalled for 18 months.Material for MkDocswent into maintenance. The ecosystem splintered intoProperDocs,MaterialX, andZensical.. read more

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@varbear shared a link, 2 months, 2 weeks ago

FAUN.dev()

How we monitor internal coding agents for misalignment

AI systems are acting with more autonomy in real-world settings, with OpenAI focusing on responsibly navigating this transition to AGI by building capable systems and developing monitoring methods to deploy and manage them safely. OpenAI has implemented a monitoring system for coding agents to learn.. 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).