DevSecOps in Practice
A Hands-On Guide to Operationalizing DevSecOps at Scale
Updates and recent posts about BigQuery.. Activity
@devopslinks added a new tool JFrog Xray , 3 months ago.
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@devopslinks added a new tool OWASP Dependency-Check , 3 months ago.
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@varbear added a new tool pre-commit , 3 months ago.
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@devopslinks added a new tool GitGuardian , 3 months ago.
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@devopslinks added a new tool detect-secrets , 3 months ago.
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@devopslinks added a new tool Gitleaks , 3 months ago.
Course
@eon01 published a course, 3 months ago
Founder, FAUN.dev
Story
@tairascott shared a post, 3 months ago
AI Expert and Consultant, Trigma
How Do Large Language Models (LLMs) Work? An In-Depth Look
Discover how Large Language Models work through a clear and human centered explanation. Learn about training, reasoning, and real world applications including Agentic AI development and LLM powered solutions from Trigma.
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@laura_garcia shared a post, 3 months ago
Software Developer, RELIANOID
🔐 RELIANOID at Gartner IAM Summit 2025 | Dec 8–10, Grapevine, TX
We’re heading to the Gartner Identity & Access Management Summit to showcase how RELIANOID’s intelligent proxy and ADC platforms empower modern IAM: enhancing Zero Trust enforcement, adaptive access, and hybrid/multi-cloud security. Join us to explore AI-driven automation, ITDR, and identity governa..
Link
@varbear shared a link, 3 months ago
FAUN.dev()
Confessions of a Software Developer: No More Self-Censorship
A mid-career dev hits pause after ten years in the game -realizing core skills likepolymorphism, SQL, and automated testingnever quite clicked. Leadership roles, shipping products, mentoring junior devs - none of it filled those gaps. They'd been writingC#/.NETfor a while too. Not out of love, just .. read more
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).
