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Introduction<\/strong><\/span><\/h2>\nAs AI adoption accelerates across industries, businesses face an undeniable truth \u2014 AI is only as powerful as the data that fuels it. To truly harness AI\u2019s potential, organizations must effectively manage, store, and process high-scale data while ensuring cost efficiency, resilience, performance and operational agility.<\/span>\u00a0<\/span><\/p>\nAt Cisco Support Case Management \u2013 IT, we confronted this challenge head-on. Our team delivers a centralized IT platform that manages the entire lifecycle of Cisco product and service cases. Our mission is to provide customers with the fastest and most effective case resolution, leveraging best-in-class technologies and AI-driven automation. We achieve this while maintaining a platform that is highly scalable, highly available, and cost-efficient. To deliver the best possible customer experience, we must efficiently store and process massive volumes of growing data. This data fuels and trains our AI models, which power critical automation solutions to deliver faster and more accurate resolutions. <\/span>Our biggest challenge was striking the right balance between building a <\/span>highly scalable<\/span><\/b> and <\/span>reliable<\/span><\/b> database cluster while ensuring <\/span>cost and operational efficiency<\/span><\/b>.<\/span>\u00a0<\/span><\/p>\nTraditional approaches to high availability often rely on separate clusters per datacenter, leading to significant costs, not just for the initial setup but to maintain and manage the data replication process and high availability. However, AI workloads demand real-time data access, rapid processing, and uninterrupted availability, something legacy architectures struggle to deliver.<\/span>\u00a0<\/span><\/p>\nSo, how do you architect a multi-datacenter infrastructure that can persist and process massive data to support AI and data-intensive workloads, all while keeping operational costs low? That\u2019s exactly the challenge our team set out to solve.<\/span>\u00a0<\/span><\/p>\nIn this blog, we\u2019ll explore how we built an intelligent, scalable, and AI-ready data infrastructure that enables real-time decision-making, optimizes resource utilization, reduces costs and redefines operational efficiency.<\/span>\u00a0<\/span><\/p>\nRethinking AI-ready case management at scale<\/strong><\/span><\/h2>\nIn today\u2019s AI-driven world, customer support is no longer just about resolving cases, it\u2019s about continuously learning and automating to make resolution faster and better while efficiently handling the cost and operational agility.\u00a0<\/span>\u00a0<\/span><\/p>\nThe same rich dataset that powers case management must also fuel AI models and automation workflows, reducing case resolution time from hours or days to mere minutes, which helps in increased customer satisfaction.<\/span>\u00a0<\/span><\/p>\nThis created a fundamental challenge: decoupling the primary database that serves mainstream case management transactional system from an AI-ready, search-friendly database, a necessity for scaling automation without overburdening the core platform. While the idea made perfect sense, it introduced two major concerns: cost and scalability. As AI workloads grow, so does the amount of data. Managing this ever-expanding dataset while ensuring high performance, resilience, and minimal manual intervention during outages required an entirely new approach.<\/span>\u00a0<\/span><\/p>\nRather than following the traditional model of deploying separate database clusters per data center for high availability, we took a bold step toward building a single stretched database cluster spanning multiple data centers. This architecture not only optimized resource utilization and reduced both initial and maintenance costs but also ensured seamless data availability.<\/span>\u00a0<\/span><\/p>\nBy rethinking traditional index database infrastructure models, we redefined AI-powered automation for Cisco case management, paving the way for faster, smarter, and more cost-effective support solutions.<\/span>\u00a0<\/span><\/p>\nHow we solved it \u2013 The technology foundation<\/strong><\/span><\/h2>\nBuilding a<\/span> multi-data<\/span><\/span> center <\/span>modern <\/span>index <\/span>database <\/span>cluster<\/span> required<\/span> a <\/span>robust technological foundation<\/span>, capable of handling <\/span>high-scale data processing, <\/span>ultra-low latency for faster data replication<\/span>, and <\/span>careful<\/span> design approach to build a fault-tolerance to support high availability <\/span>without compromising <\/span>performance, or cost-efficiency<\/span>.<\/span><\/span>\u00a0<\/span><\/p>\nNetwork Requirements<\/strong><\/h3>\nA key challenge in stretching an <\/span>index dat<\/span>abase <\/span>cluster across multiple data<\/span><\/span> centers is <\/span>network performance<\/span>. Traditional <\/span>high availability<\/span> architectures rely on <\/span>separate clusters per data<\/span><\/span> center<\/span>, often struggling with <\/span>data replication, latency, and synchronization bottlenecks<\/span>. T<\/span>o <\/span>begin with,<\/span> we conducted a <\/span>detailed network assessment<\/span> across our Cisco data<\/span><\/span> centers <\/span><\/span> focusing on:<\/span><\/span>\u00a0<\/span><\/p>\n\n- Latency and bandwidth requirements<\/span><\/b> \u2013 Our AI-powered automation workloads demand real-time data access. We analyzed latency and bandwidth between two separate data centers to determine if a stretched cluster was viable.\u00a0<\/span>\u00a0<\/span><\/li>\n
- Capacity planning<\/span><\/b> \u2013 We assessed our expected data growth, AI query patterns, and indexing rates to ensure that the infrastructure could scale efficiently.<\/span>\u00a0<\/span><\/li>\n
- Resiliency and failover readiness <\/span><\/b>\u2013 The network needed to handle automated failovers, ensuring uninterrupted data availability, even during outages.<\/span>\u00a0<\/span><\/li>\n<\/ul>\n
How Cisco\u2019s high-performance data center paved the way<\/strong><\/span><\/h2>\nCisco\u2019s <\/span>high-performance data<\/span><\/span> center networking<\/span> laid <\/span>a strong <\/span>foundation<\/span> for building the multi-data<\/span><\/span> center<\/span> stretch<\/span> single<\/span> database <\/span>cluster<\/span>.<\/span> The latency and bandwidth provided by Cisco data<\/span><\/span> centers <\/span>exceeded<\/span> our expectation to confidently move <\/span>on to the next step of designing a stretch cluster.<\/span> Our implementation <\/span>l<\/span>everaged<\/span>:<\/span><\/span><\/p>\n\n- Cisco Application<\/span> Centric Infrastructure (ACI) <\/span><\/b>\u2013 Offered a policy-driven, software-defined network, ensuring optimized routing, low-latency communication, and workload-aware traffic management between data centers.<\/span>\u00a0<\/span>\u00a0<\/span><\/li>\n
- Cisco Application Policy Infrastructure Controller (APIC) and Nexus 9000 Switches<\/strong> \u2013 Enabled high-throughput, resilient, and dynamically scalable interconnectivity, crucial for quick data synchronization across data centers.<\/span>\u00a0<\/span><\/li>\n<\/ul>\n
The Cisco data center and networking technology made this possible. It empowered Cisco IT to take this idea forward and enabled us to build this successful cluster which saves significant costs and provides high operational efficiency.<\/p>\n
Our<\/span> implementation \u2013 The multi-data center stretch cluster leveraging Cisco data center<\/span> and network power<\/span><\/span><\/strong><\/span><\/h2>\nWith the right network infrastructure in place, we set out to build a highly available, scalable, and AI-optimized database cluster spanning multiple data centers.<\/p>\n
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![\"\"](\"https:\/\/storage.googleapis.com\/blogs-images-new\/ciscoblogs\/1\/2025\/05\/Datacenter-blog-300x153.jpg\")
At Cisco Support Case Management \u2013 IT, we confronted this challenge head-on. Our team delivers a centralized IT platform that manages the entire lifecycle of Cisco product and service cases. Our mission is to provide customers with the fastest and most effective case resolution, leveraging best-in-class technologies and AI-driven automation. We achieve this while maintaining a platform that is highly scalable, highly available, and cost-efficient. To deliver the best possible customer experience, we must efficiently store and process massive volumes of growing data. This data fuels and trains our AI models, which power critical automation solutions to deliver faster and more accurate resolutions. <\/span>Our biggest challenge was striking the right balance between building a <\/span>highly scalable<\/span><\/b> and <\/span>reliable<\/span><\/b> database cluster while ensuring <\/span>cost and operational efficiency<\/span><\/b>.<\/span>\u00a0<\/span><\/p>\n Traditional approaches to high availability often rely on separate clusters per datacenter, leading to significant costs, not just for the initial setup but to maintain and manage the data replication process and high availability. However, AI workloads demand real-time data access, rapid processing, and uninterrupted availability, something legacy architectures struggle to deliver.<\/span>\u00a0<\/span><\/p>\n So, how do you architect a multi-datacenter infrastructure that can persist and process massive data to support AI and data-intensive workloads, all while keeping operational costs low? That\u2019s exactly the challenge our team set out to solve.<\/span>\u00a0<\/span><\/p>\n In this blog, we\u2019ll explore how we built an intelligent, scalable, and AI-ready data infrastructure that enables real-time decision-making, optimizes resource utilization, reduces costs and redefines operational efficiency.<\/span>\u00a0<\/span><\/p>\n In today\u2019s AI-driven world, customer support is no longer just about resolving cases, it\u2019s about continuously learning and automating to make resolution faster and better while efficiently handling the cost and operational agility.\u00a0<\/span>\u00a0<\/span><\/p>\n The same rich dataset that powers case management must also fuel AI models and automation workflows, reducing case resolution time from hours or days to mere minutes, which helps in increased customer satisfaction.<\/span>\u00a0<\/span><\/p>\n This created a fundamental challenge: decoupling the primary database that serves mainstream case management transactional system from an AI-ready, search-friendly database, a necessity for scaling automation without overburdening the core platform. While the idea made perfect sense, it introduced two major concerns: cost and scalability. As AI workloads grow, so does the amount of data. Managing this ever-expanding dataset while ensuring high performance, resilience, and minimal manual intervention during outages required an entirely new approach.<\/span>\u00a0<\/span><\/p>\n Rather than following the traditional model of deploying separate database clusters per data center for high availability, we took a bold step toward building a single stretched database cluster spanning multiple data centers. This architecture not only optimized resource utilization and reduced both initial and maintenance costs but also ensured seamless data availability.<\/span>\u00a0<\/span><\/p>\n By rethinking traditional index database infrastructure models, we redefined AI-powered automation for Cisco case management, paving the way for faster, smarter, and more cost-effective support solutions.<\/span>\u00a0<\/span><\/p>\n Building a<\/span> multi-data<\/span><\/span> center <\/span>modern <\/span>index <\/span>database <\/span>cluster<\/span> required<\/span> a <\/span>robust technological foundation<\/span>, capable of handling <\/span>high-scale data processing, <\/span>ultra-low latency for faster data replication<\/span>, and <\/span>careful<\/span> design approach to build a fault-tolerance to support high availability <\/span>without compromising <\/span>performance, or cost-efficiency<\/span>.<\/span><\/span>\u00a0<\/span><\/p>\n A key challenge in stretching an <\/span>index dat<\/span>abase <\/span>cluster across multiple data<\/span><\/span> centers is <\/span>network performance<\/span>. Traditional <\/span>high availability<\/span> architectures rely on <\/span>separate clusters per data<\/span><\/span> center<\/span>, often struggling with <\/span>data replication, latency, and synchronization bottlenecks<\/span>. T<\/span>o <\/span>begin with,<\/span> we conducted a <\/span>detailed network assessment<\/span> across our Cisco data<\/span><\/span> centers <\/span><\/span> focusing on:<\/span><\/span>\u00a0<\/span><\/p>\n Cisco\u2019s <\/span>high-performance data<\/span><\/span> center networking<\/span> laid <\/span>a strong <\/span>foundation<\/span> for building the multi-data<\/span><\/span> center<\/span> stretch<\/span> single<\/span> database <\/span>cluster<\/span>.<\/span> The latency and bandwidth provided by Cisco data<\/span><\/span> centers <\/span>exceeded<\/span> our expectation to confidently move <\/span>on to the next step of designing a stretch cluster.<\/span> Our implementation <\/span>l<\/span>everaged<\/span>:<\/span><\/span><\/p>\n The Cisco data center and networking technology made this possible. It empowered Cisco IT to take this idea forward and enabled us to build this successful cluster which saves significant costs and provides high operational efficiency.<\/p>\n With the right network infrastructure in place, we set out to build a highly available, scalable, and AI-optimized database cluster spanning multiple data centers.<\/p>\n \u00a0<\/p>\nRethinking AI-ready case management at scale<\/strong><\/span><\/h2>\n
How we solved it \u2013 The technology foundation<\/strong><\/span><\/h2>\n
Network Requirements<\/strong><\/h3>\n
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How Cisco\u2019s high-performance data center paved the way<\/strong><\/span><\/h2>\n
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Our<\/span> implementation \u2013 The multi-data center stretch cluster leveraging Cisco data center<\/span> and network power<\/span><\/span><\/strong><\/span><\/h2>\n