Showcasing Oracle DAG Orchestrator

🚀 Building Reliable Oracle Job Orchestration on AWS

In large-scale data systems, managing long-running SQL executions with complex dependencies can be a real challenge — especially when dealing with Oracle databases where some queries may take hours to finish, and where failure recovery needs to be bulletproof.

In one of my recent projects, I had the opportunity to design and implement the Oracle DAG Orchestrator, a system built to handle exactly these challenges with reliability, observability, and automatic recovery as key priorities.

🛠️ The Problem

• We needed to execute a series of Oracle SQL scripts with strict execution order and dependency management.
• Some scripts could run for longer than 15 minutes (up to several hours).
• If any step failed, the system had to resume from the last successful step, not start from the beginning.
• We required full monitoring and logging for every execution step.

🧭 Solution Overview: Oracle DAG Orchestrator

The system is designed as a DAG-based SQL job orchestrator running on AWS Cloud, leveraging:

• AWS Step Functions for workflow orchestration and automatic retries.
• AWS Glue Python Shell jobs for executing long-running Oracle SQL statements (supports up to 48 hours per job).
• AWS Lambda functions for lightweight tasks like dependency checks and status updates.
• Oracle Database as the target system for SQL execution.
• CloudWatch Logs and Metrics for observability and alerting.

Key features: ✅ Automatic failure recovery with resume-from-last-success. ✅ Supports long-running SQL jobs. ✅ Detailed execution history and monitoring via Step Functions and CloudWatch. ✅ Modular and configurable DAG structure per job set.

📂 Why This Approach?

Compared to traditional schedulers like Apache Airflow, this stack gave us:

• Simplified deployment (no scheduler cluster to maintain).
• Built-in reliability and easy state tracking with Step Functions.
• Cost-effective scaling thanks to serverless components.
• Native integration with AWS services like Secrets Manager, Redshift, and S3.

This solution fits well for Oracle workloads where SQL execution time is unpredictable, and where reliability and maintainability outweigh complex DAG branching needs.

🌱 Lessons Learned

• Designing for idempotency at each SQL script level is critical.
• Visibility into each step’s status and failure point significantly reduces operational overhead.
• Step Functions’ combination of state machine logic and built-in retries provided a strong foundation for reliable orchestration without the need for external scheduling infrastructure.

🙌 Thank You!

If you’re interested in reliable data pipeline orchestration, long-running SQL management, or AWS serverless workflows, feel free to connect — always happy to share and learn together!