Cloud Asset Inventory
Engineering
The Real-Time Cloud CMDB - Why Ephemeral Infrastructure Killed Traditional Discovery
Your Kubernetes pods live for 3 minutes. Your Lambda functions for 300 milliseconds. Your CMDB updates weekly. See the problem?
In traditional IT environments, infrastructure was measured in months and years. A server lived in the data center for 3-5 years. Network equipment lasted a decade. CMDBs were designed for this reality, where a weekly discovery scan was more than sufficient. Then cloud happened. And everything changed.
Today, the majority of containers in production have short lifespans, often measured in minutes rather than hours or days. AWS Lambda functions execute in milliseconds and their instances are recycled after periods of inactivity. Kubernetes pods autoscale continuously, spawning and terminating hundreds of instances within minutes. Infrastructure has become ephemeral, and traditional discovery models have become obsolete.
TL;DR
Traditional CMDBs miss the vast majority of short-lived cloud resources because they rely on scheduled discovery (daily or weekly scans). With containers living just minutes and Lambda functions executing in milliseconds, ephemeral infrastructure demands a new approach. Modern cloud CMDBs use tiered sync strategies (critical resources every 15-30 minutes, important resources hourly, baseline daily) for SQL queryability and scalability. CloudQuery extracts data from cloud APIs into your database of choice, enabling flexible sync schedules and on-demand queries when you need current state.
The Ephemeral Reality: How Cloud Infrastructure Actually Behaves #
Cloud infrastructure operates on a completely different timescale than traditional IT. According to Datadog's container research, the majority of containers in production have short lifespans, often measured in minutes. This isn't a bug, it's a feature. Kubernetes treats containers as disposable units of compute.
A typical autoscaling scenario: traffic spikes, the Kubernetes Horizontal Pod Autoscaler spawns 50 new pods, traffic subsides 10 minutes later, pods terminate. The entire lifecycle happens faster than most CMDB discovery scans run. A single microservice might cycle through hundreds of container instances daily.
Serverless functions take ephemerality even further:
- Lambda cold starts: 400-700 milliseconds
- Function execution: Often completes in under 1 second
- Instance lifecycle: Recycled after minutes of inactivity, depending on resource availability
An API Gateway triggering a Lambda that queries DynamoDB completes the entire request in under one second. From a CMDB perspective, these resources effectively don't exist.
The core problem: Infrastructure that exists for 5 minutes may never be discovered by tools designed to run hourly or daily scans. AWS spot instances can be terminated with just a 2-minute warning. The fundamental assumption underlying traditional CMDBs (that infrastructure is relatively stable) no longer holds in cloud environments.
Key Terms
Ephemeral infrastructure: Cloud resources with lifespans measured in minutes or seconds rather than months or years. Includes containers, serverless functions, and auto-scaled instances.
Tiered sync strategy: Syncing different resource types at different frequencies based on criticality. Critical resources (IAM, security groups) sync every 15-30 minutes, while less critical resources sync hourly or daily.
Infrastructure Lake: Data architecture pattern that extracts cloud data into queryable databases (PostgreSQL, BigQuery, Snowflake) rather than proprietary CMDB applications, enabling SQL analysis and time-series tracking. Used as the foundation for modern cloud CMDBs.
Dynamic configuration management: Tracking infrastructure that changes continuously rather than remaining static, using flexible sync strategies and time-series data.
The Speed Mismatch: When Discovery Can't Keep Up #
The math on traditional discovery simply doesn't work for cloud environments.
Traditional CMDB discovery runs on scheduled intervals: daily scans are common, weekly scans are not unusual, and even "aggressive" schedules rarely exceed hourly intervals. Each scan must enumerate all resources across all accounts and regions, querying dozens or hundreds of API endpoints.
This comprehensive approach is necessary because traditional discovery has no other way to detect changes. It checks everything, repeatedly. API rate limits from AWS, GCP, and Azure constrain how fast these scans can run. A thorough scan of a large AWS environment can take hours.
Consider a moderately large cloud environment generating 1,000 infrastructure changes per hour. With a 24-hour discovery window, you miss the vast majority of short-lived resources. A resource that exists for 30 minutes has little chance of appearing in a daily CMDB scan.
The Ephemeral Infrastructure Problem
With a 24-hour discovery window, you miss the vast majority of short-lived resources. Traditional discovery fundamentally cannot track infrastructure that appears and disappears between scans. Resources existing for minutes are effectively invisible.
Real-World Impact #
This speed mismatch has concrete consequences:
Security: A compromised Lambda function spins up, runs a crypto miner for 5 minutes, and terminates before your CMDB discovers it. The resource never appears in your security scanning tools.
Cost: Ephemeral GPU instances rack up charges but your FinOps tools never see them because they terminate between daily scans. You can't attribute costs to specific resources or teams.
Compliance: Auditors ask to prove infrastructure configuration on a specific date. Your CMDB has snapshots from the day before and after, but not the actual date. The resources existed between your scheduled scans.
Debugging: Engineers find references to pods that don't appear in the CMDB. These "ghost" resources existed during the incident but have no record.
Traditional vs. Modern Cloud CMDB: A Comparison #
What Modern Cloud CMDBs Actually Need #
The answer isn't simply "scan faster." Even with unlimited rate limits, the cost would be prohibitive and you'd still miss short-lived resources. Modern cloud CMDBs need a different approach.
How do you balance CMDB sync frequency with cost? #
Every CMDB implementation must balance competing concerns:
- How fresh does the data need to be?
- How complete does the inventory need to be?
- How much are you willing to spend on API calls?
There's no universal answer. Security teams might need IAM changes visible within 30 minutes. FinOps teams analyzing cost trends might be fine with daily snapshots. Development environments can update less frequently than production.
Flexible Sync Strategies #
One-size-fits-all sync schedules don't work. Modern CMDBs need tiered approaches where different resources sync at different frequencies:
Tier 1 (Critical, 15-30 minute sync):
- IAM policies, security groups, public-facing resources
- Immediate security implications
- Fast detection worth the API cost
Tier 2 (Important, hourly sync):
- EC2 instances, RDS databases, S3 buckets
- Matter for operations and cost
- Less urgency than security-critical resources
Tier 3 (Baseline, daily sync):
- Development environments, archival storage
- Sufficient for trend analysis and cost tracking
What database architecture works best for cloud CMDBs? #
Traditional CMDBs use application databases designed for ITSM workflows like ticket tracking and change requests. These weren't built to handle millions of time-series records across thousands of resource types.
The Infrastructure Lake architecture offers a better model: extract cloud data via purpose-built tools, load into scalable data warehouses (PostgreSQL, BigQuery, Snowflake, ClickHouse), and query using SQL. This provides time-series data, SQL queryability, integration with existing data infrastructure, and scalability to handle millions of resources.
The Technical Challenge: Why This is Hard #
If the solution seems obvious (just sync more frequently), why doesn't everyone do it?
API Rate Limits #
Every cloud provider implements rate limiting:
- GCP: Quota system with different limits per service
- Azure: Subscription-level throttling
In large environments, aggressive polling quickly hits these limits. Your sync jobs slow down or fail. There's a ceiling on how fast you can pull data. The only solutions are adding more API quota (expensive), distributing requests across time (slower), or being more selective about what you sync (reduced coverage).
Data Volume and Velocity #
Large cloud environments generate staggering amounts of configuration data. An organization with 1,000 AWS accounts might extract millions of records per sync. That data needs to be stored, indexed, and made queryable. Keep data for 2 years for compliance and you're storing billions of historical records. The infrastructure must handle thousands of changes per day, every day, continuously.
Why is filtering cloud CMDB data so challenging? #
Cloud providers launch new services constantly. AWS alone has over 200 services, each with multiple resource types. You can't instrument everything. You must prioritize: which resource types matter most for security? For cost analysis? For compliance?
Not all infrastructure changes matter equally. Different teams need different views. Security teams want IAM and network configuration. FinOps teams need compute and storage resources. Custom views and filtering become essential to make CMDB data useful rather than overwhelming.
Practical Approaches That Work #
Tiered Sync Strategy in Practice #
For an organization with 1,000 AWS accounts, a tiered approach balances visibility, cost, and API quota constraints:
- Critical resources (15-30 min): IAM, security groups, public-facing resources, certificates
- Important resources (hourly): EC2, RDS, S3, Kubernetes clusters
- Baseline resources (daily): Development environments, archival storage, historical metadata
Building a Modern CMDB with CloudQuery #
CloudQuery provides a modern approach to building cloud CMDBs. It extracts data from cloud provider APIs (AWS, GCP, Azure, Kubernetes) using purpose-built connectors that handle rate limiting appropriately. You load data into your database of choice and query using standard SQL rather than navigating proprietary CMDB application interfaces.
CloudQuery's key capabilities for tracking ephemeral infrastructure:
- Extensive cloud resource coverage across AWS, GCP, Azure, and Kubernetes with automatic updates as providers add new resource types
- Built-in incremental sync reduces API calls and costs by syncing only changed resources rather than full scans
- Automatic schema evolution handles cloud provider API changes without manual intervention, ensuring your CMDB stays current
Transform and model data using tools like dbt to create custom views for different teams. Security views, FinOps dashboards, and compliance reports all draw from the same underlying data. This architecture separates data collection from data analysis.
Query-on-Demand Pattern #
Combine scheduled syncs for baseline inventory with on-demand queries when you need current state. Scheduled syncs run at defined intervals to maintain historical inventory. On-demand queries hit cloud provider APIs directly during incidents or audits. This hybrid approach balances cost with freshness.
Use Cases and How to Address Them #
Security and Incident Response #
Requirement: Detect security-critical changes within minutes to hours.
Approach: Sync critical resources every 15-30 minutes (IAM, security groups, public-facing resources). Alert on changes via policies. Use on-demand queries during incident response.
Example: Detect unauthorized IAM role creation within 30 minutes. Alerting rules fire and security investigates while the trail is fresh.
Cost Optimization and FinOps #
Requirement: Understand resource usage and identify waste.
Approach: Daily snapshots are sufficient for most FinOps analysis. Track instance types and configurations over time. Join CMDB data with billing data to attribute costs.
Example: Even ephemeral resources show up in billing. Daily snapshots correlate billing line items with configurations, even if instances have terminated.
Compliance and Audit #
Requirement: Prove infrastructure configuration at specific points in time.
Approach: Regular snapshots (daily or more frequent) create audit trails. Point-in-time queries answer "what did infrastructure look like on October 15?" Historical retention addresses regulatory requirements (2-7 years).
Example: Auditor asks to prove S3 buckets had encryption enabled on a specific date. Point-in-time query provides the evidence.
Operational Visibility #
Requirement: Troubleshoot issues and track resource relationships.
Approach: Balance sync frequency with criticality. Production services sync hourly, development daily. Use on-demand queries during troubleshooting.
Example: Application fails. Engineers query what infrastructure changed in the last hour. Security group modification, new deployment, or database change appears. Historical data narrows the investigation.
Summary #
Traditional CMDBs were built for a world where servers lived for years and weekly discovery scans provided adequate visibility. That world no longer exists. Modern cloud infrastructure is ephemeral. With containers living just minutes and Lambda functions executing in milliseconds, traditional 24-hour discovery windows miss the vast majority of short-lived resources.
The solution isn't "scan faster." API rate limits, costs, and data volumes make that impractical. Instead, modern cloud CMDBs need tiered sync strategies: 15-30 minutes for critical resources like IAM and security groups, hourly for important resources like EC2 and RDS, and daily for baseline resources. The Infrastructure Lake architecture (extracting cloud data into scalable databases and querying with SQL) provides the scalability and flexibility modern cloud environments demand. CloudQuery enables this approach by syncing cloud infrastructure data into your database of choice.
Key takeaways: Use API-first approaches combining scheduled syncs with on-demand queries. Expect to handle millions of records per sync across 1,000+ accounts. Modern cloud CMDB architectures cost significantly less than traditional CMDB applications by using existing databases and avoiding proprietary licensing. Finding the right balance depends on your requirements, but the principle holds: ephemeral infrastructure killed traditional discovery, and modern approaches must account for that reality.
Ready to build a modern cloud CMDB? CloudQuery can help you extract and synchronize cloud infrastructure data at scale. Reach out for a demo or explore the documentation to get started.
Frequently Asked Questions #
What is a real-time cloud CMDB? #
A real-time cloud CMDB is a configuration management database that updates infrastructure inventory frequently enough to track ephemeral resources. Unlike traditional CMDBs that scan daily or weekly, real-time cloud CMDBs use tiered sync strategies (15-30 minutes for critical resources, hourly for important resources, daily for baseline) to maintain accurate visibility into cloud environments where containers live for minutes and serverless functions execute in milliseconds.
Why can't traditional CMDBs handle ephemeral infrastructure? #
Traditional CMDBs rely on scheduled discovery scans (daily or weekly intervals) that are too slow for cloud environments. With most containers living just minutes and Lambda functions executing in under one second, traditional discovery misses the vast majority of short-lived resources. These resources appear, execute, and vanish between scheduled scans, leaving security gaps, incomplete cost attribution, and missing audit trails.
What is the difference between a cloud CMDB and a traditional CMDB? #
Traditional CMDBs were designed for stable infrastructure (servers lasting 3-5 years) with manual or periodic discovery. Cloud CMDBs must handle ephemeral resources, millions of configuration changes per day, and time-series data across thousands of resource types. Cloud CMDBs use tiered sync strategies, Infrastructure Lake architecture with SQL queryability, and API-first approaches that combine scheduled syncs with on-demand queries. Traditional CMDB applications weren't built for this scale or velocity.
How often should a cloud CMDB sync infrastructure data? #
It depends on the resource criticality. Use a tiered approach: critical security resources (IAM, security groups, public-facing resources) should sync every 15-30 minutes, important operational resources (EC2, RDS, S3) should sync hourly, and baseline resources (development environments, archival storage) can sync daily. This balances visibility needs against API rate limits and costs.
What is dynamic configuration management for cloud infrastructure? #
Dynamic configuration management tracks infrastructure that changes continuously rather than remaining static. In cloud environments, resources autoscale, containers restart, and serverless functions execute on-demand. Dynamic configuration management uses flexible sync strategies, handles time-series data, and provides both historical snapshots for compliance and on-demand queries for current state. It replaces static inventory approaches that assume infrastructure is relatively stable.
Can CloudQuery handle ephemeral infrastructure? #
Yes. CloudQuery extracts data from cloud provider APIs (AWS, GCP, Azure, Kubernetes) and loads it into your database of choice. You control sync frequency per resource type, implementing tiered strategies that sync critical resources every 15-30 minutes while less critical resources sync less frequently. CloudQuery provides SQL queryability, time-series data for historical analysis, and scalability to handle millions of resources across thousands of accounts.
What are the biggest challenges with tracking ephemeral infrastructure? #
The main challenges are API rate limits (cloud providers throttle aggressive polling), data volume (millions of records per sync across 1,000+ accounts), coverage (AWS alone has 200+ services with new ones launching monthly), and filtering signal from noise (not all infrastructure changes matter equally). Solutions require tiered sync strategies, selective resource prioritization, scalable data storage, and custom views for different teams.
How much does it cost to build a cloud CMDB? #
Costs vary based on environment size and sync frequency. For an organization with 1,000 AWS accounts, expect database storage costs (hosting billions of historical records for 2-year retention), API call costs (though cloud providers don't charge for most metadata API calls), and infrastructure costs for running sync jobs. Using CloudQuery with your existing database is significantly more cost-effective than traditional CMDB applications, as you avoid proprietary licensing fees and leverage databases you already have.
