Production is going on.
Approximately 22 percent less active database storage
BCT supported a PostgreSQL migration to AWS Outposts and combined controlled archival, selective backups and automated monitoring to reduce the production database footprint and improve operational visibility.
Client
Challenge
Continuous data growth increased production storage requirements, backup volumes and operational overhead. Older records remained in premium production storage even when they were rarely accessed. The customer needed to reduce the active database footprint while preserving required historical information and its recoverability.
Database monitoring also lacked comprehensive coverage. Identifying high CPU utilisation, disk growth, long-running queries and blocking sessions required manual investigation across multiple sources. Intermittent issues could disappear before the team investigated them, leaving limited evidence for root-cause analysis. The migration therefore needed to address both infrastructure utilisation and the ability to detect and investigate performance problems earlier.
Database monitoring also lacked comprehensive coverage. Identifying high CPU utilisation, disk growth, long-running queries and blocking sessions required manual investigation across multiple sources. Intermittent issues could disappear before the team investigated them, leaving limited evidence for root-cause analysis. The migration therefore needed to address both infrastructure utilisation and the ability to detect and investigate performance problems earlier.
Solution
Over a three-month initiative, BCT worked with the database, infrastructure and application teams to support the PostgreSQL migration to AWS Outposts. The work included database preparation, data movement, testing and validation, followed by database and application checks before the new environment became the production platform.
BCT analysed large historical tables and obtained application-team approval before archiving eligible data. Required historical records were preserved for recovery while being moved out of active production storage. The team also reviewed backups and used selective retention of required data and tables in Amazon S3 to control unnecessary duplication and backup growth.
A database-level monitoring framework introduced automated alerts for CPU and disk utilisation, long-running queries, blocking sessions and other health indicators. Consolidated environment reports gave the support team a common view of database health, reducing repetitive checks across separate monitoring sources.
BCT captured blocking and long-running-query events in dedicated database tables so the team could investigate them after the original sessions ended. Automated retention and cleanup kept this monitoring history manageable. Regular query reviews and findings shared with the application team established an ongoing process for addressing potential performance issues.
BCT analysed large historical tables and obtained application-team approval before archiving eligible data. Required historical records were preserved for recovery while being moved out of active production storage. The team also reviewed backups and used selective retention of required data and tables in Amazon S3 to control unnecessary duplication and backup growth.
A database-level monitoring framework introduced automated alerts for CPU and disk utilisation, long-running queries, blocking sessions and other health indicators. Consolidated environment reports gave the support team a common view of database health, reducing repetitive checks across separate monitoring sources.
BCT captured blocking and long-running-query events in dedicated database tables so the team could investigate them after the original sessions ended. Automated retention and cleanup kept this monitoring history manageable. Regular query reviews and findings shared with the application team established an ongoing process for addressing potential performance issues.
Impact
Controlled archival reduced the production database from approximately 4.5 TB to 3.5 TB, removing around 1 TB from the active footprint, a reduction of roughly 22%. Required historical information remained recoverable, while selective backup practices helped control Amazon S3 storage consumption.
Automated monitoring and consolidated reporting reduced manual health-check effort and enabled earlier detection of resource pressure and query problems. Retained performance-event history improved the team’s ability to investigate intermittent issues even after the affected sessions had completed.
The initiative gave the customer a smaller active database and a more proactive support process. Lower production-storage consumption and more selective backups created opportunities for ongoing cost optimisation. Archival and performance reviews continue as the environment grows, supporting more efficient capacity use and reducing the risk of performance issues escalating into customer impact.
Automated monitoring and consolidated reporting reduced manual health-check effort and enabled earlier detection of resource pressure and query problems. Retained performance-event history improved the team’s ability to investigate intermittent issues even after the affected sessions had completed.
The initiative gave the customer a smaller active database and a more proactive support process. Lower production-storage consumption and more selective backups created opportunities for ongoing cost optimisation. Archival and performance reviews continue as the environment grows, supporting more efficient capacity use and reducing the risk of performance issues escalating into customer impact.
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