🤖 Automating the Sierra Integration Project with Smart Scripting

When managing a large library system like Sierra, even small process delays can add up to hours of extra work. Manual steps, error-prone updates, and delayed data syncing often make things harder for both staff and patrons.

To solve this, we built a script-driven workflow that automates hold request extraction and tracking. This ensures faster processing, fewer errors, and better visibility of what’s happening in the system.

Here’s a step-by-step look at how the script works and why each piece matters.

Step 1: Setup & Configuration

The script starts by defining key settings:

• Database connection details.(use the db connection such as database name, port,host user etc)
• File paths for output CSVs and logs.
• Unique timestamps for each run (to prevent overwriting files).

This makes sure every execution is consistent and easy to trace.

Step 2: Tracking the Last Run

To avoid reprocessing old data, the script checks for a state file.

• If the file exists, it uses the stored timestamp to fetch only new hold requests.
• If it doesn’t exist, it falls back to a default start date.

This incremental approach means faster runs and no duplicate processing.

 Step 3: Logging

Every run creates a log file that captures:

• Start and end times.
• Each step of the process.
• Successes and errors.

Logs are written both to the file and the console, making it easy to monitor progress in real time.

 Step 4: Error Handling

If something goes wrong, the script:

• Records the error message, line number, and exit code.
• Stops execution immediately.

This makes troubleshooting simple and prevents incomplete or corrupted data exports.

Step 5: Data Extraction & Processing

Inside the database session, the script:

1. Creates a temporary table for new hold requests.
2. Filter them by location.
3. Matches pickup locations to stop codes.
4. Exports results (with book details + barcodes) into a CSV file.
5. It provides staff with the right data in a simple format, making their job easier.

Step 6: Updating State

After exporting, the script saves the latest processed timestamp.

Next time it runs, it knows exactly where to start, avoiding overlaps.

Step 7: Completion

At the end, the script clearly reports as:

• Success which shows the file path of the exported data.
• Failure which displays detailed error info.

 

Flow Chart