Operational and analytical reports depend on timely, accurate data ingestion. Manual or unreliable data preparation leads to delayed reports, missed SLAs, and increased operational risk when failures go unnoticed.
This project focuses on automating data ingestion, validation, and monitoring to ensure reliable and timely data availability for business reporting.
Stakeholders faced challenges with data availability and reliability:
- Manual data ingestion delayed reporting cycles
- Failures were detected late or manually
- Reporting teams lacked visibility into pipeline status
- Inconsistent data availability increased operational risk
The issue was not analytics logic, but process reliability and monitoring.
Ensure that incoming data is:
- Automatically processed
- Validated before reporting use
- Monitored for failures
- Available on time for business analysis
- Reviewed manual ingestion steps and failure points
- Identified lack of monitoring as a key operational risk
- Assessed how delays impacted reporting SLAs and reviews
- Defined checkpoints required for reliable reporting consumption
- Automatically triggered data processing when new files are added, ensuring timely availability of data for reporting.
- Standardized processing and validation before reporting
- Implemented monitoring to detect failures early
- Ensured processed data is query-ready for reporting teams
- Reduced manual intervention in reporting workflows
- Improved reliability of data availability
- Faster detection of ingestion or processing failures
- More consistent reporting timelines
- Improved reporting SLA adherence
- Lower operational risk due to early failure detection
- Increased trust in data used for decision-making
- Better scalability as data volume grows
- Automated alerts when ingestion or processing fails
- Visibility into pipeline health for operational teams
- Reduced dependency on manual checks
- SQL-based analytics
- Cloud-based data processing and monitoring services
- Automated alerts and logging
Reliable reporting depends on process automation and monitoring. By automating ingestion and validation, business teams gain faster, more dependable access to data without increasing operational risk.
Cloud-based data storage and processing services, SQL-based analytics and querying, Automated workflow triggers, Monitoring and alerting for data reliability, Access control and permissions management
Project created and maintained by Vibin Krishna
With guidance from AWS Documentation and Perplexity AI