Managing Design Trade Offs!

Problem statement:

Design a data warehousing job where the job has to load the execution date partition of a target table X depends on the execution date partition(n)th of table Y and (n+2)th partition of table Z.

Complexity:

Designing a job orchestration that depends on execution dates (n) and (n+2) might be challenging due to the dependency on future data (n+2), which is not available at the time of execution (n). Here are a few design trade-offs you might want to consider:

Tradeoffs:

1. Latency vs Consistency: If the job waits for the n+2th partition of table Z to be ready, it ensures consistency, as the job always uses the most recent data. However, it leads to increased latency because the job cannot start until the n+2th partition is ready. On the other hand, if the job is executed as soon as the nth partition of table Y is ready, it may reduce latency but compromise data consistency.
2. Complexity vs Performance: Implementing a complex system to handle such dependencies can ensure high performance as it could allow for parallel processing of the nth and n+2th partitions. However, the increase in system complexity can also lead to more difficult maintenance and higher chances of errors.
3. Resource Utilization vs Timeliness: If the job starts processing the nth day partition while waiting for the n+2th day partition, it might lead to a more efficient utilization of resources but could compromise the timeliness of the job, especially if the n+2th partition takes longer than expected to get ready.
4. Real-Time vs Batch Processing: Real-time processing could provide more up-to-date data but may add a lot of performance overhead and need more resources. Batch processing, on the other hand, might be less resource-intensive but result in less fresh data.

Strategies:

1. Dependency Management: Use a job scheduling or orchestration tool that can handle dependencies, such as Apache Airflow or Luigi. This can help ensure that the job is executed only when all the necessary data is ready.
2. Buffering: Implement a buffer mechanism where you keep two days of data in buffer for table Z. This ensures that when the job runs on the nth day for table Y, the n+2th day data for table Z is already available.
3. Data Versioning: Keep versions of your data in your warehouse so that you can always fetch the correct version depending on the job execution date.
4. Error Handling and Retry Mechanisms: Implement robust error handling and retry mechanisms to handle any failures during the job execution.
5. Monitoring and Alerting: Monitor the system and set up alerts for any delays or failures in the readiness of the data partitions.