By the CloudWise Team
In the world of cloud computing, understanding and managing your costs is just as crucial as the deployment of your applications. At CloudWise, we set out to build an alternative to AWS's Cost Explorer that not only visualizes spending but also provides actionable insights. In this post, we'll dive deep into our architecture decisions and the tradeoffs we faced along the way.
1. Understanding Requirements
Before jumping into design, we gathered requirements from our users. They needed:
- Real-time data: Users want to see their spending as it happens.
- Custom reports: Flexibility to analyze spending based on different dimensions (service, account, region).
- Alerts and recommendations: Proactive notifications for unusual spending patterns.
2. Architecture Overview
Our architecture is built around a few key components:
Data Ingestion: We use AWS Lambda functions to pull billing data from AWS Cost and Usage Reports (CUR) and the AWS Billing API every 6 hours.
Data Storage: We opted for a combination of Amazon S3 and Amazon Athena. S3 stores raw CUR files, and Athena allows for quick querying without needing to set up a complex database.
Data Processing: Apache Airflow orchestrates our ETL (Extract, Transform, Load) process. Data is cleaned, aggregated, and stored in a format optimized for analysis.
Frontend: For the user interface, we chose React.js, leveraging Chart.js for visualizations. This allows for dynamic and responsive charts that users can interact with.
3. Decision Points and Tradeoffs
A. Real-Time vs. Batch Processing
Decision: We initially considered a real-time streaming approach using Kinesis but opted for an hourly batch process to keep costs down.
Tradeoff: While this means users won’t see real-time data, it significantly reduces complexity and costs. The hourly updates still provide timely insights without the overhead of continuous data streaming.
B. S3 vs. Database
Decision: Using S3 combined with Athena instead of a traditional RDBMS.
Tradeoff: While querying with Athena can be slower than a direct database query, it allows us to avoid the complexities of database management and scaling. S3 is also cost-effective for large datasets.
C. Alerting Mechanism
Decision: Implement a simple alerting mechanism using SNS (Simple Notification Service) for budget thresholds.
Tradeoff: While it’s easy to set up, it lacks advanced features like predictive analytics. However, it meets our MVP needs and can evolve as we gather user feedback.
4. Future Enhancements
As we continue to develop our platform, we plan to implement features such as:
- Machine Learning for Anomaly Detection: Leveraging AWS SageMaker to identify unusual spending patterns.
- Enhanced Reporting: More customizable reporting options based on user feedback.
- Multi-Cloud Support: Extending our capabilities to support spending analysis across multiple cloud providers.
Conclusion
Building a Cost Explorer alternative has been a challenging yet rewarding journey. By focusing on user needs and making strategic architectural decisions, we’ve crafted a platform that not only tracks AWS costs but also empowers users to optimize their spending.
We’d love to hear your thoughts or experiences in building similar tools. What tradeoffs have you encountered in your projects?
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