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laura_garcia
@laura_garcia shared a post, 1 week, 2 days ago
Software Developer, RELIANOID

RELIANOID at CII Delhi International Technology Summit 2025

16–17 December 2025 - New Delhi, India Our team continues a packed December schedule, and we’re excited to add another key event: the CII Delhi International Technology Summit 2025. Focused on “Accelerating the Techade”, this summit brings together industry, government, and research leaders to shape..

CII Delhi International Technology Summit relianoid
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anjali
@anjali shared a link, 1 week, 2 days ago
Customer Marketing Manager, Last9

OTel Updates: OpenTelemetry Proposes Changes to Stability, Releases, and Semantic Conventions

OpenTelemetry proposes stability changes: stable-by-default distributions, decoupled instrumentation, and epoch releases for production deployments.

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laura_garcia
@laura_garcia shared a post, 1 week, 3 days ago
Software Developer, RELIANOID

deploy the RELIANOID Load Balancer Community Edition v7 on Azure using Terraform

🚀 New Technical Guide Available! You can now deploy the RELIANOID Load Balancer Community Edition v7 on Azure using Terraform in just a few minutes: ✔️ Install prerequisites (Terraform, Azure CLI, SSH keys) ✔️ Use the official Terraform module from the Registry ✔️ Automatically provision all Azure r..

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@pixel_og started using tool Thanos , 1 week, 3 days ago.
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@pixel_og started using tool Terraform , 1 week, 3 days ago.
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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).