databases

Identify performance bottlenecks by analyzing MongoDB and PostgreSQL configurations, queries, and schemas. The skill applies indexing strategies and explain analyses to pinpoint gaps and optimize query execution across both database models.

Manage database migrations with rollback support using script-driven tooling and a lightweight tracking mechanism. This workflow ensures reliable schema changes and enables automated checks to maintain data integrity during production migrations.

Automate backups and restores across MongoDB and PostgreSQL using provided automation scripts for common maintenance tasks. These workflows ensure production readiness and enable reliable recovery operations for both document and relational models.

You need pymongo and psycopg2 dependencies to run the cross-database tooling and assessments. These drivers enable the script-driven tooling required to execute automated checks and analyze configurations for both database systems.

Schema design guidance balances flexibility and data integrity by applying model-specific best practices for both document and relational databases. This approach ensures scalable schemas while addressing the unique structural constraints of MongoDB and PostgreSQL.

Reliability gaps occur when MongoDB and PostgreSQL configurations, queries, or schemas lack proper indexing, backup readiness, or migration tracking. Analyzing these components with automated checks helps identify and resolve vulnerabilities in production deployments.