This project serves as a hands-on simulation of an enterprise SAP back-end environment. The goal is to demonstrate practical proficiency in Linux system administration, command-line operations, and relational database management—core competencies required for a Junior SAP Basis / TMS Engineer.

• Operating System: openSUSE Leap 16.0 (AArch64) - Headless Server setup
• Database Engine: PostgreSQL
• Virtualization: Parallels Desktop (running on Apple Silicon / ARM architecture)
• Core Tools: Bash CLI, zypper package manager, systemd

• Deployed an openSUSE environment without a Graphical User Interface (GUI) to simulate real-world server conditions.
• Navigated and administered the system exclusively via the Command Line Interface (CLI).

• Utilized SUSE's native package manager (zypper) to fetch, install, and configure the PostgreSQL server.
• Managed background services (daemons) using systemctl, ensuring the database engine is enabled for persistence (systemctl enable) and monitoring its health status (systemctl status).

• Switched user environments securely (su - postgres) to manage the database engine.
• Created a test database (sap_test) and structured tables to simulate an SAP user registry.
• Executed standard SQL queries (CREATE, INSERT, SELECT) to validate data integrity and operational success.

Note: The following screenshots validate the successful execution of CLI commands and database operations.

Demonstrating the PostgreSQL service actively running and enabled on the SUSE server.

Demonstrating successful connection to the DB engine, table creation, and querying synthetic SAP user data.

• Developed a Python script utilizing the psycopg2 adapter to automate the generation and injection of 500+ synthetic SAP user records.
• Demonstrated intermediate scripting capabilities and database connectivity without relying on external network protocols (Unix sockets).

Developing a custom Python automation script using the psycopg2 adapter to generate and structure 500+ synthetic SAP user records.

Executing the Python script via the command-line interface to securely inject the synthetic data volume directly into the PostgreSQL engine.

• Simulated a critical data loss incident by dropping the production database.
• Executed logical backups using pg_dump and successfully performed a full system restoration, guaranteeing data persistence and SLA compliance.

Performing a full logical backup of the populated database utilizing native PostgreSQL tools (pg_dump) to ensure data preservation.

Intentionally dropping the primary database to simulate a critical system failure and validate our disaster recovery protocols.

Demonstrating the successful restoration of 500+ records after a simulated database crash.

• Developed a custom Bash script (health_check.sh) to proactively monitor critical server resources, including available RAM, root disk capacity, and PostgreSQL daemon status.
• Configured automated background execution and log generation by scheduling a cronjob to run the health check every 5 minutes, ensuring continuous system observability.

Developing a custom Bash script via CLI to query system resources and database health status, outputting the results to an audit log.

Validating the script execution and reviewing the generated audit log containing timestamped resource metrics.

Scheduling the monitoring script as a background task using crontab for continuous, unattended execution every 5 minutes.

Created as a practical portfolio project - March 2026