Updates and recent posts about BigQuery..

Posts
Description

Activity

@rafidbottler gave 🐾 to Android Architecture: Components, Patterns & Best Practices Guide , 4 days, 1 hour ago.

Activity

@rafidbottler gave 🐾 to Artificial Intelligence vs Machine Learning: What's the Difference? , 4 days, 1 hour ago.

Link

@anjali5 shared a link, 4 days, 21 hours ago

How to Fix Developer Productivity at 50+ Engineers

You ship a feature. It works. A week later, someone asks why it's not in staging yet, and you realize it's behind an infrastructure request that's still in review. The ticket isn't urgent enough to escalate. It's also not small enough to ignore. So it waits.

That's what a developer productivity problem feels like at 50 engineers.

Story

@laura_garcia shared a post, 4 days, 22 hours ago

Software Developer, RELIANOID

AI Reliability Engineering: The New Era of SRE

🤖 As AI becomes part of critical business operations, reliability is no longer just an infrastructure concern. From latency and model drift to observability and trust, AI workloads introduce a new set of challenges for modern SRE teams. In our latest article, we look at how reliability engineering i..

Activity

@pluto_native started using tool Terraform , 1 week ago.

Activity

@pluto_native started using tool Kubernetes , 1 week ago.

Activity

@pluto_native started using tool Google Cloud Platform , 1 week ago.

Activity

@pluto_native started using tool Amazon Web Services , 1 week ago.

Link

@eon01 shared a link, 1 week ago

Founder, FAUN.dev

A curated list of free AI models, APIs, and tools you can use without paying a cent.

Running AI shouldn't require a credit card. This list curates genuinely free models — open-weight models you can self-host, free API tiers from major providers, and tools to run everything locally.

Activity

@eon01 added a new tool Unsloth , 1 week, 1 day 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).