Target state: the target architecture would define ingestion, transformation and loading pipelines using open-source tools like Apache Airflow, Apache Spark and AWS Glue. It would ingest raw data from various sources into an S3 data lake in its native formats. Standard ETL/ELT processes would then transform and load the data into structured data stores based on predefined schemas, ensuring privacy and regulatory compliance. Processed data would be made available for analytics through services like AWS Athena.
Gap analysis: the current architecture retrieves and processes data but there is no mention of standardized data ingestion or processing flows. This could lead to non-compliant or insecure data handling practices as the infrastructure scales. It also does not support analyzing data across different sources in a unified manner.
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Target state: The target architecture would involve creating separate VPCs - a "restricted" VPC to host only workloads dealing with sensitive data like PII, and a "general" VPC for other less sensitive workloads. Network access controls and security groups would be used to prevent direct communication and data sharing between the VPCs, restricting access to sensitive data.
Gap analysis: The current architecture deploys workloads containing both sensitive and non-sensitive data into the same VPCs. This poses a risk if a breach occurs, as it could potentially expose all data. The architecture does not sufficiently segregate access and restrict sharing of sensitive data as required.
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Target state: To address scalability, databases will be sharded so each shard can scale independently. Databases will be configured for auto-scaling so additional read replicas are added automatically based on usage. The data will be partitioned by date, customer or other dimensions to distribute load. Compute services like ETL will be designed to work with a sharded and partitioned architecture for distributed processing.
Gap analysis: Our current architecture uses single instances for databases and data warehouses. While this meets needs, it does not allow scaling out in a cost efficient and performant way as data and workloads increase time. The databases and data warehouse may encounter performance issues and become a bottleneck if scaled appropriately.
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Target state: The target architecture migrates relational databases to Amazon Aurora, which provides automatic scaling of storage and compute resources. Aurora replicas can scale out performance by adding more instances as load increases. Its self-healing capabilities also improve availability. This dynamic scaling ensures databases can adapt to varying workloads and support business growth in a cost-efficient manner.
Gap analysis: The current architecture uses multiple Amazon RDS deployments for relational databases. While RDS provides managed database services, it does not automatically scale compute and storage. This could limit the ability to dynamically scale databases as load increases. Manual scaling may not keep up with unpredictable growth.
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Archie is a conversational copilot with key capabilities:
- Natural language architecture queries: Users can ask questions about their architecture in plain English and get contextual responses
- Real-time architecture understanding: Archie has "full domain understanding of your architecture" and requirements, so it can provide specific insights rather than generic responses
- On-demand expertise: Provides "next level insight across the architecture that's on demand" for any team member's specific context
- Architecture-specific learning: Allows architects to "learn and introspect your existing systems at scale" and get recommendations for immediate, practical issues
Archie integrates deeply with Catio's other modules:
- Stacks integration: Has full understanding of the live architecture digital twin, so responses are grounded in actual infrastructure data
- Requirements module: Understands your governance standards and regulatory requirements, incorporating them into responses
- Recommendations module: Can surface and explain existing recommendations, helping users understand why certain changes are suggested
- Cross-module navigation: The demo mentioned that responses can help users "navigate to that part of your tech stack to learn more"
Catio integrates with major cloud platforms (AWS, Azure, GCP), developer and collaboration tools (GitHub, Jira, Confluence, Notion), and identity providers (Okta, Azure AD, SSO/SAML/LDAP). Exports are available in JSON/YAML, PDF, and image formats to fit seamlessly into your current workflows.
Yes. Catio allows you to snapshot your current architecture state, model proposed changes, and simulate their impact in a safe environment. Teams can validate outcomes, compare states, and reduce risk before committing to production.