Cloud-based high-performance computing infrastructure with specialized machine types, TPU and GPU acceleration options, and integration with financial data sources. Supports quantitative investment strategies, portfolio optimization, and AI-driven market analysis.
Specialized hardware designed for computationally intensive tasks such as Monte Carlo simulations, optimization algorithms, and complex scenario modeling to support sophisticated strategy development.
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Node Count Total number of physical compute nodes within the cluster. |
No information available | |
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CPU Cores Aggregate number of processing cores available in the cluster. |
No information available | |
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GPU Acceleration Availability of GPU resources for parallel or accelerated computation. |
Product explicitly advertises NVIDIA GPU and TPU accelerator support for parallel and accelerated computation. | |
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Total Computational Power Aggregate computational capacity of the cluster. |
No information available | |
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Memory per Node RAM available to each compute node for memory-intensive tasks. |
No information available | |
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Interconnect Speed Maximum bandwidth of the network interconnect between cluster nodes. |
No information available | |
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Low Latency Networking Support for low-latency communication protocols (e.g., Infiniband) for distributed computing. |
Google Cloud HPC supports low-latency interconnects (RDMA, Infiniband-equivalent options) for distributed compute clusters. | |
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Storage IOPS Input/output operations per second of primary storage. |
No information available | |
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High-Speed SSD Tier Presence of a high-speed SSD storage tier for fast data reads/writes. |
Supports high-speed SSD Persistent Disk and Local SSD tiers for fast data read/write. | |
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Scalability Ability to increase computational resources quickly (vertical or horizontal scaling). |
Designed for rapid horizontal and vertical scaling using Google Cloud's infrastructure. | |
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High Availability Cluster redundancy and failover capabilities to ensure uptime. |
Google Cloud Platform includes high-availability and failover features for clusters and regional redundancy. | |
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Job Scheduler Advanced job scheduling and queuing software for resource allocation. |
Supports Slurm and other HPC job schedulers natively. | |
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Peak Power Consumption Peak electricity consumption during maximum load. |
No information available | |
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Burst Capability Capacity to handle load bursts above steady state. |
Cloud clusters can autoscale resources for burst compute loads. |
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Total Storage Capacity Aggregate storage space available for data, models, and logs. |
No information available | |
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Data Ingestion Rate Rate at which system can import new datasets. |
No information available | |
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Support for Distributed File Systems Ability to utilize distributed file systems for efficient data access (e.g., HDFS, Lustre). |
Google Cloud supports integration with distributed file systems such as Filestore, and integration with Hadoop (HDFS). | |
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Automated Backup Automated snapshotting and restoration features. |
Backup and snapshot automation is available for Persistent Disk and Cloud Storage. | |
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Data Encryption Data is encrypted at rest and in motion to meet security standards. |
Data encryption at rest and in transit is standard on Google Cloud. | |
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Role-based Data Access Control Fine-grained controls over which users/groups have access to specific data. |
Role-based access control (RBAC) is integrated through Cloud IAM. | |
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Data Retention Policy Management Configurable policies for data archival and disposal. |
Data retention can be managed via policies in Cloud Storage and via compliance tooling. | |
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Real-Time Stream Processing Ingestion and processing of data streams for live analytics. |
Real-time data streaming and analytics supported via Google Cloud Dataflow, Pub/Sub, and similar services. | |
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Support for Multiple Data Formats Ability to handle various data types (CSV, Parquet, JSON, SQL, etc). |
Supports multiple data formats: CSV, JSON, Parquet, SQL, etc. via data lakes and ingestion services. | |
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API Access to Data Storage Direct programmatic access to stored datasets. |
Data storage systems are accessible via APIs (REST, gRPC, etc.). | |
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Data Lineage Tracking Tracking and documenting data transformations and movements. |
BigQuery and GCP offer data lineage capabilities via Data Catalog and tracking tools. | |
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Data Versioning Maintaining multiple versions of datasets for audit and rollback. |
Cloud Storage and supported analytics tools allow versioning of datasets. | |
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Hybrid Cloud Storage Integration Ability to span on-premise and cloud storage seamlessly. |
Seamless hybrid cloud storage offers integrations with on-prem and multiple cloud providers. |
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End-to-End Encryption Encryption is applied from data source through storage and transmission. |
End-to-end encryption is available using Google Managed Encryption Keys and Customer-Managed Encryption Keys. | |
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Audit Logging All critical user and system actions are logged for audit and compliance purposes. |
Audit logging is standard through Google Cloud's Operations Suite (Cloud Audit Logs). | |
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Regulatory Compliance Certifications Compliance with standards such as GDPR, SOC 2, MiFID II, etc. |
Google Cloud maintains SOC 2, ISO, PCI DSS, GDPR, and MiFID II compliance certifications. | |
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Multi-Factor Authentication MFA required for user and administrator logins. |
Multi-factor authentication is supported for all users of the Google Cloud Console and APIs. | |
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User Role Management Ability to set granular user permissions and roles. |
User and group roles are controlled through Google Cloud IAM for fine-grained access. | |
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Intrusion Detection System Automated systems to detect and respond to unauthorized activities. |
Cloud IDS (Intrusion Detection System) and Security Command Center assist with threat detection. | |
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Data Masking Personally identifiable data is masked or anonymized when needed. |
Data masking is supported via Google Cloud DLP and related tools. | |
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Access Review Workflows Automated and auditable review of user access rights. |
Cloud Identity supports access review workflows and auditability. | |
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Secure APIs All API endpoints are secured following industry standards (e.g., OAuth2, TLS). |
All APIs are secured by OAuth2 and TLS as per Google Cloud platform requirements. | |
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Automated Security Patch Management System automatically deploys critical security updates. |
Security patches are deployed automatically for VMs and managed services. | |
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Incident Response Procedures Documented and tested response plans for security incidents. |
Documented incident response procedures maintained and tested for cloud services. |
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Preinstalled Quantitative Libraries Bundles of financial analytics, machine learning, and statistical packages (e.g., NumPy, pandas, TensorFlow, QuantLib). |
Cloud AI Platform and Deep Learning VM Images come with preinstalled quantitative, ML, and statistical libraries. | |
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Algorithmic Trading Frameworks Built-in support for backtesting and live implementation of trading strategies. |
Product supports algorithmic trading research and live execution in partnership with financial industry APIs and providers. | |
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Support for Multiple Programming Languages Ability to run code in Python, R, C++, Matlab, etc. |
Supports Python, R, C++, Java, and more for analytics and modeling workloads. | |
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Visualization Tools Integrated support for dashboards and advanced data visualization. |
Data visualization and dashboarding is available via partner solutions (Looker, Tableau) and Jupyter. | |
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Simulation Engines Tools for Monte Carlo, scenario, and stress testing. |
Simulation engines can be implemented using provided libraries and Google Cloud's scalable compute resources. | |
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Portfolio Optimization Built-in libraries for advanced risk and return optimization problems. |
Portfolio optimization available through integrated libraries and partner packages. | |
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Factor Model Integration Capability to build and analyze factor-based risk and performance models. |
Google Cloud enables custom and partner-based factor model integration for quantitative finance. | |
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Machine Learning Model Lifecycle Management Facilities for model building, validation, deployment, and monitoring. |
Vertex AI and Pipeline support full ML model lifecycle management. | |
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Real-Time Analytics Support Tools for low latency, high-frequency modeling and analytics. |
Real-time analytics tools provided via BigQuery, Dataflow, and other services. | |
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Interactive Computing Environments Availability of Jupyter, RStudio, or equivalent environments for exploration. |
Jupyter Notebook, Vertex AI Workbench, and RStudio integrations are available for interactive computing. | |
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Third-Party Model Marketplace Ability to access, evaluate, and integrate third-party models or analytics solutions. |
Access to third-party model marketplaces is available via Google Marketplace and partner APIs. |
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Pipeline Orchestration Automated scheduling and orchestration of data science and investment modeling workflows. |
Pipeline orchestration is supported via Vertex AI Pipelines and Workflow tooling. | |
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Job Scheduling Support for batch, real-time, and cron-based execution of jobs. |
Job scheduling for batch, real-time, and cron-based workflows is built into the platform. | |
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Error Monitoring and Notification Automated alerts on job failures or anomalous outcomes. |
Error monitoring and notification available via Google Cloud Monitoring and Alerting. | |
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Workflow Templates Prebuilt templates for typical financial data and modeling workflows. |
Workflow templates are available for typical financial and AI analytics via Google Cloud Marketplace and code repositories. | |
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Parameterization Support Ability to parameterize jobs for backtesting and scenario analysis. |
Jobs can be parameterized for rapid scenario analysis and backtests. | |
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Interactive Debugging Capabilities Ability to step through workflows interactively for development purposes. |
Interactive debugging is possible using Vertex AI Workbench and supported IDEs within VM environments. | |
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Automated Report Generation Generation of research, performance, and compliance reports via automation. |
Automated report generation supported through Data Studio, Looker, and reporting APIs. | |
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API-Driven Workflow Integration Integration of workflows with external systems and data feeds. |
Workflows can be triggered and controlled via API, enabling integrations. | |
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Scheduling Constraints Customization of resource and time constraints on workflow execution. |
Scheduling constraints supported within workflow and job management tools. | |
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Version Control Integration Integration with Git or similar tools for code and workflow versioning. |
Version control integrations for Git/GitHub are available in Workbench and scripting environments. |
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Standardized APIs REST, SOAP, or GraphQL APIs for bidirectional data and process integration. |
REST, gRPC, and custom APIs are standard for data/process bidirectional integration. | |
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Prebuilt Data Feed Integrations Out-of-the-box support for integrating with major financial and market data providers. |
Prebuilt integrations offered for Bloomberg, Refinitiv, and other major financial market data providers through the Google Cloud Marketplace. | |
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Support for FIX Protocol Native support for FIX messaging in trading workflows. |
FIX protocol can be supported via third-party and partner solutions. | |
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Custom Connectors Easily extensible connectors for proprietary data sources or systems. |
Custom connectors and partner-built adapters can be developed and attached for any proprietary sources. | |
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Cloud Service Integration Direct integration with leading public or private cloud offerings. |
Native integration with public and private cloud services (AWS, Azure, and on-premises) through Interconnect and Transfer tools. | |
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Excel Integration Ability to import/export and automate workflows with Excel. |
Excel integration is possible via Connected Sheets, APIs, and partner solutions. | |
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Real-time Market Data Integration Capability to consume streaming market data feeds. |
Supports real-time market data feeds through approved APIs and Pub/Sub. | |
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SaaS Platform Compatibility Interoperability with SaaS analytics or investment platforms. |
SaaS analytics/investment apps can be run and integrated via GCP's APIs and partner integration layers. | |
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Messaging & Notification Integration Hooks for email, SMS, or chat notifications for workflow and job status. |
Messaging (email, SMS, chat) events can be triggered through Pub/Sub, Cloud Functions, and integrations. | |
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Open-Source Package Compatibility Ability to use widely adopted open-source libraries or tools. |
Platform supports open-source ecosystems (Python, R, SQL engines, TensorFlow, etc). |
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Multi-user Access Support for concurrent access by multiple users. |
Multi-user access and concurrent resource provisioning is foundational within Google Cloud IAM and workspace constructs. | |
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Granular Permission Control Detailed assignment of permissions at project, data, or job level. |
Granular permissions can be controlled at the project, resource, and role level via IAM. | |
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Collaboration Workspaces Dedicated workspaces for project-based team collaboration. |
Collaboration workspaces available through shared projects, Cloud Workstations, and Vertex AI integration. | |
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Activity Logging Comprehensive logging of user activities and resource access. |
Activity logging is standard via Cloud Audit Logs. | |
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Integration with SSO Providers Single sign-on (SSO) integration for enterprise directory services. |
SSO integration is available for enterprise directory services via Google Identity and SAML/OAuth. | |
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Commenting and Notation Tools Ability for users to add comments and notes on shared assets. |
Commenting and annotation tools supported in collaborative notebooks and via integrations like Google Docs. | |
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Shared Project Templates Reusable collaborative templates for common research or strategy workflows. |
Shared project templates can be created and shared via GCP Marketplace, Vertex AI Workbench, and repositories. | |
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User Delegation Delegation of approval or workflow steps to alternate users. |
Workflows support delegation and approval steps in collaborative setups. | |
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Audit Trail Reporting Generating reports on user access and changes for compliance. |
Audit trail reporting is native in the platform for compliance. |
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System Health Dashboards Real-time visualizations of cluster, resource, and workflow status. |
System health dashboards are available as part of Google Cloud Operations Suite. | |
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Resource Usage Metrics Detailed statistics on CPU, RAM, storage, and network usage. |
Comprehensive resource usage metrics are available via Google Cloud Monitoring tools. | |
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Automated Usage Reports Scheduled summary reporting of resource and user activity. |
Automated usage reporting configurable via Cloud Monitoring. | |
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Alerting and Notification System Customizable threshold-based notifications for system events. |
Alerting and notification system can be user-configured for system events. | |
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Cost Tracking and Reporting Visibility into consumption-based or chargeback costs. |
Cost tracking and reporting are native through Google Cloud Billing and cost management tools. | |
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Job Execution Logs Retention of detailed logs for each computational job. |
Job execution logs are retained and viewable through Cloud Logging. | |
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Performance Benchmarking Tools Methods to evaluate and compare cluster performance over time. |
Performance benchmarking tools are available natively or via Marketplace for cluster/workload analysis. | |
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Compliance Reporting Automated generation of compliance and regulatory reports. |
Compliance reporting can be automated using partner solutions and platform features. | |
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Custom Report Builder Flexible construction of custom reports and dashboards. |
Custom reports can be built using Data Studio, Looker, and dashboarding APIs. | |
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External Audit Support Features to facilitate third-party audit and validation. |
Third-party audit support is available through compliance tooling and reporting. |
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Geographic Redundancy Replication of data and services across multiple geographic locations. |
Multi-regional replication and geographic redundancy are available across Google Cloud's data center network. | |
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Automated Failover Automatic redirection to backup systems upon failure. |
Automated failover is supported within high-availability and multi-zone deployments. | |
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Regular Disaster Recovery Drills Routine simulation and validation of DR processes. |
Google Cloud supports disaster recovery planning and testing. | |
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Snapshot Backups Regularly scheduled backups of environment and data. |
Snapshot backup scheduling is available for Persistent Disk and other storage. | |
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Restore Time Objective (RTO) Typical time to restore service after a major outage. |
No information available | |
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Restore Point Objective (RPO) Maximum data loss window allowed by backup strategy. |
No information available | |
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Replication Latency Maximum age of replicated data between primary and backup facilities. |
No information available | |
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Business Continuity Planning Support Integrated planning and documentation tools for business continuity. |
Integrated disaster recovery and business continuity planning tools available. | |
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Immutable Backup Storage Backups cannot be deleted or altered (protection against ransomware). |
Cloud Storage offers object versioning and retention features that support immutable backups. | |
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Self-Healing Infrastructure Automated identification and repair of certain types of hardware/software failures. |
Platform supports automatic remediation and high availability for infrastructure self-healing. |
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Flexible Deployment Options On-premises, cloud, and hybrid deployment capabilities. |
Cloud, on-premises, and hybrid deployments are all supported. | |
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Automated Provisioning Tools to quickly set up and configure cluster nodes and storage. |
Automated provisioning is available for clusters, VMs, and storage. | |
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Rolling Upgrades Cluster maintenance and software upgrades can occur without downtime. |
Rolling upgrade processes are supported for managed instance groups with zero downtime. | |
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Containerization Support Support for Docker, Kubernetes, or similar for packaging and orchestrating workloads. |
Full support for Docker and Kubernetes (GKE) for containerization. | |
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Automated Patch Management OS and package patches are automatically distributed and installed. |
Automated OS and package patching available via Google OS Config and managed services. | |
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Configuration as Code Cluster configuration is managed and versioned declaratively. |
Infrastructure as code (Terraform, Deployment Manager) fully supported. | |
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Hardware Health Monitoring Automated monitoring of hardware (CPU, memory, drives, fans) for failure prediction. |
Hardware health is monitored and proactive notifications are provided. | |
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Comprehensive Documentation Extensive and up-to-date documentation for installation, use, and troubleshooting. |
Comprehensive product documentation is hosted on Google Cloud's website. | |
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24/7 Technical Support Round-the-clock access to technical support personnel. |
24/7 enterprise-grade support packages are available. | |
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Professional Services Availability Availability of vendor-provided consulting, integration, or custom engineering support. |
Professional services available through Google Cloud Professional Services and partner network. |
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