Grafana Tempo 2.9 Supercharges Distributed Tracing with LLM Integration
Grafana Tempo 2.9 debuts with MCP server support and TraceQL metrics sampling, enhancing data analysis and query efficiency.
Updates and recent posts about BigQuery.. Activity
@varbear added a new tool TypeScript , 1 month, 3 weeks ago.
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@devopslinks added a new tool Grafana Tempo , 1 month, 3 weeks ago.
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@kala shared an update, 1 month, 3 weeks ago
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LangChain Secures $125M and Launches LangChain & LangGraph 1.0
LangChain raised $125 million to enhance its agent engineering platform, introducing LangChain and LangGraph 1.0 with new tools like the Insights Agent and a no-code agent builder, aiming to transform LLM applications into reliable agents.
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@devopslinks added a new tool Snapper , 1 month, 3 weeks ago.
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SUSE Linux Enterprise 16 Launches with AI for Smarter Operations
SUSE releases Linux Enterprise Server 16, integrating AI to boost efficiency and cut costs, with features like secure AI integration and a 16-year support lifecycle.
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Ubuntu 25.10 Takes a Bold Leap — Targets Modern CPUs with New amd64v3 Architecture
Ubuntu 25.10 introduces support for amd64v3 architecture variants, enhancing performance by targeting specific silicon variants, with full support expected in the 26.04 LTS release.
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@devopslinks shared an update, 1 month, 3 weeks ago
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Google Introduces Quantum-Safe KEMs in Cloud KMS for Future Security
Google introduces quantum-safe KEMs in Cloud KMS to counter future quantum computing threats, urging organizations to transition to post-quantum cryptography.
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@devopslinks added a new tool Cloud KMS , 1 month, 3 weeks ago.
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).
