Managing cloud-native data systems harder than collecting it. The amount of information flowing through business systems continues to increase daily.Â
Every system generates data: web applications, mobile services, security tools, infrastructure platforms, and customer analytics dashboards.
Teams often build dashboards and alert systems, only to find it difficult to trace the root cause of issues, explain changes, or justify increased cloud costs.
Organizations are paying more for infrastructure, but often lack the insights that infrastructure is meant to provide. This disconnect between investment and clarity remains a major barrier.
Building a Data-Centric Architecture
A data-centric architecture is designed with one clear goal in mind: to make information usable across teams. It starts by treating data not as a by-product of applications, but as a central resource that needs structure, ownership, and reliability.
Image: Illustration of data silos vs data-centric architecture | Source: morrodata.com
In most large-size companies, different departments operate in silos. Marketing uses separate systems from engineering. Security operates independently from product development. Each group collects data differently. This fragmentation leads to duplication, missed dependencies, and uncertainty.
A data-centric approach assigns clear responsibility to every dataset. Teams know who manages the data, what format it follows, how often it gets updated, and how others can use it. This is often described as treating data like a product, meaning it has a well-defined owner and meets specific quality standards.
Scalability improves when data storage is separated from the tools that process it. This is called decoupling storage from compute. For example, data can be stored in Amazon S3, while analysis runs through services like Athena or Redshift. This avoids unnecessary duplication and reduces costs.
Observability is also built into the architecture. AWS Config, a service that tracks changes to cloud resources, records the full history of configuration settings in AWS Cloud infrastructure. If something stops working, teams can quickly review what changed, when it happened, and who made the change.
Image: Illustration of AWS Config workflow | Source: docs.aws.amazon.com
Cost management becomes more accurate when organizations track the specific cost of each workflow. Instead of looking only at total cloud spend, they calculate the cost of producing a report, running a model, or storing a particular set of data. The AWS Well-Architected Cost Optimization pillar guides these kinds of calculations.
Designing for Scale: How Billions of Configuration Changes Are Tracked Weekly
In a large-scale cloud environment, infrastructure updates happen constantly. Services start and stop. Applications deploy multiple times a day. Scaling mechanisms adjust resources based on demand. Security policies update automatically. Each of these changes affects the system’s configuration.
AWS Config captures these changes by generating what it calls configuration items (CIs). A CI records the full state of a resource, such as a server, database, or security group, at a specific point in time. It includes who made the change, what exactly changed, and how that resource is connected to other parts of the system.
The diagram below shows how AWS Config and Amazon Athena work together to find the number of Configuration Items (CIs) each month:
Image: Illustration of Amazon Athena integration with AWS Config | Source: aws.amazon.comÂ
- The process begins when the user saves a JSON template file. This file contains the setup instructions needed to create the AWS resources for the task.
- The user then launches an AWS CloudFormation (CFN) stack using the saved template.Â
- The user goes to the Athena Console and runs the first query. This query creates a table in Athena using AWS Config history files that are stored in an S3 bucket.
- Â After the table is created, the user runs the second Athena query. This query counts the number of Configuration Items (CIs) for each resource type and resource ID over a chosen time period.
In large enterprises, these changes can amount to billions over the course of a week. To manage this volume, AWS Config’s periodic recording groups update together once every 24 hours, which can reduce data volume and cost.
Aggregators combine configuration data from multiple accounts and regions, making it easier for centralized teams to monitor everything without needing to log into separate environments. Filtering rules help teams collect only the most relevant information, reducing unnecessary overhead.
AWS provides specific recommendations to reduce the cost of Config at scale. These include excluding low-risk resource types, using periodic recording where appropriate, and archiving old data.
Leveraging AWS Config for Observability and Compliance
Organizations are increasingly required to follow strict compliance frameworks. These might include data encryption, backup requirements, access controls, or geographic data residency rules. Auditing this manually is difficult, especially as systems grow.
AWS Config evaluates whether cloud resources match specific rules. These can include managed rules provided by AWS or custom rules created by security teams. For example, a rule might check that all databases are encrypted or that storage buckets are not publicly accessible.
Conformance Packs, a collection of AWS Config rules and remediation actions packaged together as a single deployable entity, simplify the setup by grouping multiple rules together for a specific standard. AWS provides packs for frameworks like CIS, PCI DSS, and NIST. These can be deployed quickly and updated over time.
When a resource violates a rule, AWS Config can trigger an alert. This alert can be connected to AWS Systems Manager, which performs automatic remediation. If a firewall rule changes unexpectedly, the system can reset it based on predefined instructions.
This approach creates a closed feedback loop between monitoring and correction. Security and operations teams no longer wait for audit cycles. They receive near real-time visibility and automated enforcement of policies.
Automation Through Generative AI: Using Amazon Bedrock for Data-Driven Decision-Making and Anomaly Detection
Generative AI is changing how teams interact with infrastructure and analytics systems. Instead of manually writing complex queries, teams can now ask questions in everyday language. Amazon Bedrock provides secure access to advanced AI models that support this capability.
Image: Illustration of Amazon Bedrock workflow | Source: aws.amazon.com
These models can summarize trends, explain unusual patterns, or help users understand root causes. For example, a team can ask why a service’s cloud cost increased and receive an answer that combines data from logs, performance metrics, and recent configuration changes.
Bedrock also supports AI agents, which are automated assistants that handle specific tasks. One agent might monitor for anomalies in cloud metrics. Another might analyze logs for security issues. A third might recommend responses based on prior incidents.
Organizations reduce costs by using multiple specialized agents instead of one large general-purpose model. AWS offers detailed guidance on how to manage token usage, select appropriate models, and configure limits.
When integrated into daily workflows, these tools allow faster, more informed decision-making without requiring users to understand complex code or systems.
Optimizing Cost and Performance: How Infrastructure Redesigns Enable Faster, Cheaper Insights
As more data systems are deployed, many organizations experience cloud bill increases without a matching increase in value. The 2025 Flexera State of the Cloud Report found that 84 percent of companies identified managing cloud spend as their top challenge. Most exceeded their planned cloud budget by about 17 percent.
Image: Data on top cloud challenges | Source: flexera.com
Cost-efficient design begins with accurate provisioning. Servers and analytics engines should scale with actual demand. Storage systems need to separate high-priority data from rarely used archives.
Processing efficiency also matters. Tools like Athena or Redshift Spectrum work better when data is organized by partition and compressed in formats like Parquet. Teams can reduce scanning time by applying filters before running queries.
Balancing Data Governance and Innovation in Enterprise-Scale Systems
Image: Data governance framework | source: ataccama.com
Large-scale systems span many teams, geographic regions, and specialized roles. Each group brings different priorities. Developers focus on speed and delivery. Security teams prioritize stability and risk reduction. Finance departments monitor spending and want visibility into how infrastructure is used.
To align these priorities, governance frameworks provide structure. Instead of relying on manual approval or case-by-case exceptions, policies are defined as code and embedded directly into development workflows. Engineers are notified when changes violate rules, allowing teams to correct issues early in the process. This approach reduces delays, minimizes rework, and builds trust between stakeholders.
Leaders reinforce this alignment by defining shared metrics. These include indicators such as incident recovery time, the number of policy violations prevented before deployment, or the operational cost of delivering a report or model. Measuring these consistently across teams creates accountability and helps identify areas where systems need improvement.
The Path Forward
Collecting more data no longer guarantees better outcomes. Without structure, ownership, and clarity, information becomes overwhelming. Organizations that focus on data usability, configuration tracking, cost transparency, and AI-driven insights are better equipped to respond quickly and accurately.
Building systems that support informed decisions requires design at every level. Teams that treat data as an active part of operations rather than passive storage will gain more from the infrastructure they already use.
About the Author
Pushpendra Singh is a Senior Engineering Manager with over 20 years of experience leading high-scale cloud, security, and AI initiatives at organizations such as Amazon and Microsoft. He has built and grown global engineering teams, driving significant cost, performance, and automation gains across enterprise systems.