Component breakdown

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01 Deploy Inside Your Private Cloud

Condense is deployed inside a private Kubernetes cluster within the customer’s existing cloud environment. The Condense platform, including Kafka and supporting platform services, runs within the customer’s cloud and private network boundary. Supported Platforms:

• Microsoft Azure (AKS)  
• Amazon Web Services (EKS)  
• Google Cloud Platform (GKE)  
• Redhat Openshift 

The deployment model allows Condense workloads and customer data to remain within the customer’s cloud environment while the Condense platform is managed as a production service.

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02 Local Container Registry 

Condense uses a local container registry within the customer’s environment to store approved platform images required for the deployment. Platform images are synchronized to the local registry and subsequently pulled by the Kubernetes cluster from within the customer’s environment. This reduces dependency on external container registries during platform operation and deployment. Benefits :

• Faster and controlled image distribution
• Reduced external registry dependency
• Controlled image versions
• Support for restricted or air-gapped environments

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03 Secure Private Networking 

Condense components communicate through the customer’s private network infrastructure. Platform services, workloads, and internal communication paths remain within the configured cloud network boundary

Deployment Includes :

• Private Kubernetes cluster
• Cluster and management subnets
• VPC / VNet connectivity
• Internal service-to-service communication
• Private connectivity between platform components

This architecture keeps production data and internal platform communication within the customer’s configured network environment.

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04 Built-In Streaming Platform

Every Condense deployment includes the platform services required to operate a complete real-time streaming environment. These services are deployed as cloud-native Kubernetes workloads within the customer’s cloud environment and work together to provide the streaming, integration, application, and operational foundation of Condense.

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Streaming and Data Integration

• Apache Kafka : Provides the core event-streaming backbone for publishing, retaining, and consuming real-time data.
• I/O Connectors : Connect external devices, databases, messaging systems, cloud services, and enterprise systems with Condense.
• MQTT : Provides messaging connectivity for supported device and IoT workloads.

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Platform Services

• Redis / PostgreSQL : Provide supporting data and state-management capabilities required by platform services and workloads.
• Command Services : Provide platform-level command and control functions.
• Metering Services : Capture platform usage information required for metering and operational management.

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Application and Development

• AI-Assisted Development : Provides an integrated development environment for building custom processing logic and real-time applications, with AI-assisted development capabilities to help developers understand, generate, modify, and refine application logic while working within the Condense environment.
• Development Containers : Provide the isolated runtime environment required to develop, test, and execute custom processing and application workloads.
• Application Lifecycle : The same platform supports the progression from development and testing through deployment and production execution, maintaining a consistent runtime model across the application lifecycle.
• Production Scalability : Once deployed, applications can scale with workload requirements through the Condense runtime, allowing processing and application workloads to operate continuously as data volumes and event rates increase.

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Platform Operations

• Observability : Provides centralized visibility across connectors, Kafka, processing workloads, applications, infrastructure, and platform services, including health, performance, throughput, errors, logs, and operational events.
• AI-Assisted Operations : AI agents can assist with operational monitoring by continuously analyzing platform signals, identifying anomalies, correlating events across components, and helping teams investigate operational issues.
• AI-Led Monitoring : AI-assisted monitoring can move beyond displaying metrics and alerts by interpreting operational signals, identifying potential issues, and surfacing conditions that require attention across the platform lifecycle.
• Incident Diagnostics : Provides operational diagnostics for identifying the source and impact of failures across platform components, connectors, workloads, and applications.
• Access Control & ACLs : Provides controlled access to platform resources through authentication, authorization, role-based access controls, and resource-level permissions, including access controls for streaming resources and applications.
• Governance : Provides mechanisms for controlling and auditing the use of platform resources, applications, data streams, and operational capabilities according to organizational policies.
• Security Management : Supports platform-level security controls covering identities, credentials, certificates, communication, and access to deployed workloads.
• Release Management : Manages platform versions, releases, upgrades, and deployment lifecycle across Condense environments.
• Configuration Management : Provides centralized management of platform and workload configuration while maintaining deployment-specific isolation.
• Health Management : Continuously evaluates platform and workload health and provides mechanisms for detecting and recovering from service-level issues.
• Capacity and Scalability Management : Monitors resource and workload requirements and supports scaling of platform and application workloads as event volumes and processing requirements change.
• Automated Recovery : Supports automated service recovery and lifecycle actions to reduce manual intervention when supported platform components encounter operational failures.
• Operational Auditability : Maintains visibility into platform and administrative activities required for operational governance and troubleshooting.

These services operate together within the Condense deployment. Kafka provides the streaming foundation, connectors provide external system integration, supporting services provide the required platform capabilities, and development containers provide the execution environment for custom workloads. Operational services provide the visibility and lifecycle controls required to maintain the platform in production.

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05 Centralized Operations

Although each customer has an isolated Condense deployment within its own cloud environment, Zeliot provides centralized operational capabilities for managing the platform lifecycle. The centralized operations layer manages the Condense platform without combining customer workloads or moving customer application data into a shared execution environment. Operations Included:

• Health Monitoring
• Version Management
• Platform Upgrades
• Security Patches
• Diagnostics
• Configuration Dashboard
• Incident Management
• Release Automation

This establishes a separation between customer workload execution and platform operations: workloads remain within the customer’s deployment while Zeliot manages the operational lifecycle of the Condense platform.

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Automated Platform Lifecycle Management

Condense automates recurring platform-management activities required to maintain the deployment throughout its lifecycle. These capabilities include:

• Platform installation
• Cluster validation
• Rolling upgrades
• Kubernetes compatibility
• Kafka upgrades
• Certificate renewal
• Service recovery
• Autoscaling
• Platform backups
• Health validation

Automation reduces the amount of manual infrastructure management required to maintain the Condense deployment and provides a consistent operational lifecycle across supported deployment environments.