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Providing a Single Pane of Glass Through a Unique 5C Model

vuSmartMapsTM is built to operationalise every complexity and intricacy in an observability platform with a SC model. The platform is further built on an enterprise-grade, next-gen, fault-tolerant architecture known as a Control Centre to deliver high performance for large-scale enterprises.

Platform Architecture with a Unique 5C Model

detailed 5c diagram


Agnostic and heterogeneous data ingestion at scale

The platform enables the ingestion of heterogeneous data sets either natively through agents or via other systems, including data lakes and open telemetry, at a speed 10X faster than typical APM agents. You can also collect Performance data (Golden Signals) from all IT infrastructure touchpoints such as Network, Server, Middleware, Application, Database, Storage. vuBlocks also bring in the context of the banking domain and are compatible with IS08583 standards.

vuBlocks (imagined as Lego Kits) simplify the development and maintenance of data adaptors with object-oriented concepts. vuBlocks’ configurable nature allows it to adapt to any changing domain, environment, and business needs. The modular structure enables each vuBlock to encompass the power of information, enrichment, KPIs, storyboards and alerts for every single data type from logs to transactions and applications. The real power of vuBlock is seen when you create a transaction topology workflow by combining individual vuBlocks!

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Driving single source of truth and utmost data quality with vuCoDS (VuNet's Common Data Schema)

The platform follows an object-oriented approach and has built vuCoDS, a common data schema, which standardizes the use of data allowing for optimized performance of the platform. All possible sources of data from source systems can be accommodated to enable the delivery of rich insights and analytics.

Designed with a data-centric approach, vuCoDS focuses on consistency of data and ensuring data quality, lending itself to understanding the data better for better Al model creation. vuCoDS ensures any changes to the data or the database structure is carried on smoothly, removing any discordance in data eliminating data silos business-wide. This design of vuCoDS enables adapting to the platform to any industry workflow or any changes in domain workflow or business model changes in a business.

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Powering data for impactful, correlated insights

Streaming data with additional business, domain, semantic, syntax, and state context helps add context to data and allow for richer, connected insights. The pre-built vuBlocks collecting the data ensure compliance to industry standards, specifically the Banking domain, such as IS08583. This is enabled by the common data schema built into the platform.
The Smart Pipeline equipped with context engine and session plug-ins enables easy management of the contextualisation, enrichment, correlation and transformation of data. The platform’s distributed architecture ensures scalability and high availability to handle growing data volumes with no data loss.


Deliver business insights in real-time with our patented vu3T Correlation Engine

With our patent-pending advanced correlation technique, vu3T, we enrich all ingested data into rich information correlated with business and domain intelligence. Even at scale, data is processed at the same speed as it is ingested, thanks to Kafka and the no-SQL data store. The enriched data is stored in a Big Data warehouse which is used to create an operational data lake that is used by MLOPs for ML modelling.
Additionally, Our MLOps engine provides intelligence into your operations. Deep Learning algorithms enable it to provide alert correlation and suppression, proactive anomaly detection, predictive failure and outage detection, models for capacity planning, root cause analysis enabled by 3T correlation mechanisms, and KPIs such as User Experience Index, Current Health Index, and Operational Performance Index that score the current and predicted state Of systems and services.


Deliver impactful and timely decisions and remediations

The intelligent consumption layer of the platform comprises smart storyboards, programmable alerts, scheduled reports and is integrated with run book remediation scripts and ticketing systems.

Smart storyboards

The platform delivers intelligent insights with narration instantly through smart storyboards, which are available as pre-defined templates. It is also customisable with readily available and configurable widgets, making it extremely simple to create your own story boards.

Programmable alerts

The alert engine is a complex event processing engine that works on indexed data and programmable rules to create intelligent alerts in real- time. It delivers dynamic threshold-based alerts, Additionally, you can program your alerts, combine multiple conditions to create compound alerts and reduce noise and alert fatigue by shifting focus on the important issues. The platform also provides automation insights that use metric-based trend analysis and Natural Language generation to provide a narrative to technical alerts, helping in better resolution of issues.

Scheduled reports

The real-time reports can be customised for different personas to bring in relevant information and reduce noise. Scheduled runs can be executed from data polling to creating scheduled reports such as daily, weekly and quarterly reports.

Auto remediation

Through seamless integrations into ITSM ticketing systems or remediation frameworks, the platform can trigger automation scripts to execute the resolutions based on anomalies, correlated events, variation in the KPIs and automated insights.


Greater security and performance delivered at scale without missing a beat

The platform has a monitoring engine known as Control Centre, which self-monitors the platform for any issues in the clusters and has inbuilt service restarts and remediation mechanisms. Additional security is ensured with security plug-ins, role-based access, SSL based data exchange mechanisms and more control features.
Autoload balancing and self-balancing clusters ensure that data latencies are avoided when streaming and enriching at scale. This also includes self-balancing clusters, autoload balancers, tiered storage and data replication autoload balancer.
To prevent data loss even at scale, a high availability cluster combines active/passive modules and data sharing. It also has tiered storage and specialised sharing and replication capabilities. Without missing a beat, the platform adapts to any reconfiguration changes.