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Aviz Networks outlines BigQuery data lake update

Source Decision Insights Record Published October 1, 2026 Companies 2
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2 companies named across 2 categories, one of 853 articles referencing Aviz Networks. Previous coverage: Aviz Networks details ONES integration with NVIDIA UFM (Oct 2026).

Companies mentioned

Best suited for

Seniority
Director
Job function
Network / Network Architect
Persona
Network Architect
Buyer role
Architect / Technical Evaluator
Buyer journey
Post Buy
Adoption curve
Early Majority
Technology maturity
Operational Expansion
Industry
Information Technology / Software & Services / Software / Vertical SaaS - Other Industry-Specific Software

Our classification, not the publisher's statement. Best suited for, not only for.

Aviz Networks’ Network Copilot R1.7.0 adds Google BigQuery as a unified data lake for network analytics, combining inventory, telemetry, topology, and performance data so operations teams can query and analyze network conditions in one place.

Research Overview

The release builds on a BigQuery connector introduced in Network Copilot R1.6.0. It uses the dataset for AI model training and for operational queries across devices, interfaces, environmental and performance metrics, and service topology.

Operational Impact

The blog says teams no longer need to move among five to ten monitoring tools to review network data. Instead, they can ask questions in plain language, such as whether anomalies are present, and receive answers based on the data model.

Aviz says the system learns normal behavior for each device and flags deviations from that baseline. It also summarizes likely causes and formats the output for direct use in an issue ticket.

Technical Breakdown

The article describes a unified data model with eight core entities covering inventory, topology, telemetry, and performance and events. Tables are linked through a common device registry key, which lets the system connect an interface issue to the related circuit and affected services.

The architecture separates the user interface, the AI agent layer, an MCP server, and the BigQuery data lake. The MCP layer handles authentication, validation, schema enforcement, rate limits, and read-only controls, while BigQuery stores the entity tables with partitioning and clustering for analytical queries.

Product Update

Network Copilot uses pre-computed baselines to avoid querying raw time-series data for every request. Those baselines store mean, standard deviation, and 3-sigma thresholds at hourly, weekly, and monthly intervals for each device-interface-circuit pair.

The blog says this approach supports anomaly detection, day-of-week analysis, bandwidth tracking, interface health checks, and circuit performance review. It also notes that users can connect the BigQuery data lake through the BigQuery Data Connector with a name and a GCP service account key file in JSON format.

Aviz Networks’ Network Copilot R1.7.0 centers network analytics on a BigQuery data lake, an AI agent, and a controlled MCP layer so operators can query unified telemetry and receive structured anomaly and root-cause outputs. This Blog Summary is a fact-based summary of the vendor blog and is relevant for enterprise teams evaluating post-deployment network operations workflows.

Blog post, originally published at aviznetworks.com.

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