Challenges with data policy

Implementing a retention policy in your Azure Data Lake Storage (ADLS) setup can have both advantages and potential challenges, especially when dealing with varying data volumes and the need for incremental loads.

Potential Challenges with Retention Policy:

1. Late-Arriving Data & Correction Loss: If you enforce a strict retention policy, late-arriving data may be discarded, leading to incomplete datasets.
2. Breaks Time Travel and Historical Queries: Retention policies can limit your ability to perform time travel queries, which are essential for historical analysis and auditing.
3. Incremental Load Reconciliation Failure: If your data ingestion process is not fully aligned with the retention policy, you might face reconciliation issues, especially with incremental loads.
4. Unavailability of Physical Delta Tables: If delta tables are not retained, it could hinder the analytics process, as these tables are often crucial for efficient querying and data transformations.

Recommendations for Scalability and Performance:

• Evaluate Data Volume: Since you are currently ingesting around 2 GB daily, ensure that your retention policy is flexible enough to accommodate fluctuations in data volume without risking data loss.
• Incremental Loading Strategy: Since you are moving towards an incremental load strategy, consider implementing a retention policy that allows for a grace period for late-arriving data while still maintaining a balance between data freshness and storage costs.
• Monitor Performance: Regularly monitor the performance of your ingestion pipeline and adjust the retention policy as needed. This can help in identifying any bottlenecks or performance issues that arise from the retention settings.
• Testing and Validation: Before fully implementing the retention policy, conduct tests to validate that your data processing and analytics workflows remain intact and efficient.

In summary, while implementing a retention policy can help in managing storage costs and data lifecycle, it is crucial to tailor it to your specific use case, particularly considering the challenges of late-arriving data and the need for historical insights. Regular monitoring and adjustments will be key to ensuring scalability and performance without breaking data insights.

References:

• Azure Data Explorer data ingestion overview
• Best practices for reliability

Hi experts, could you please help me with the above question. Thank you

Hi azure_learner,
Thankyou for Reaching Microsoft Q&A!
It looks like you're navigating some complex challenges with your data retention policies and ingestion processes in Azure Data Lake Storage (ADLS). Here’s a breakdown of your concerns and some suggestions on your approach!
For the daily ingestion of around 2 GB using retention policies on the ADLS landing, bronze, and silver layers, there are some important considerations to keep in mind. While retention policies help manage storage and improve query performance, they can sometimes lead to challenges such as losing late-arriving or corrected data, breaking historical queries due to time travel limitations, and causing reconciliation failures in incremental load processes. These risks are important but can be managed effectively with the right approach.

When working with larger volumes like 12 GB or more daily, it’s often necessary to switch to an incremental load strategy. This helps address throughput and performance issues more efficiently. Delta Lake’s MERGE operation combined with incremental loads allows handling updates and deduplication without losing data integrity. You can retain the benefits of retention policies by setting a reasonable grace period that captures late-arriving records and cleans up only older data to avoid storage bloat.

The best approach for your scenario is to continue using a retention policy for the smaller 2 GB daily append use case but ensure the retention settings allow enough time for late data adjustments. For the larger and more demanding source, adopt incremental loading with Delta Lake's capabilities to maintain high throughput and data accuracy. This balanced strategy supports scalability, performance, and uninterrupted data insights without risking data loss or analytics failures.

By carefully combining retention policies with incremental loading and Delta Lake’s features like time travel, vacuum, and schema enforcement, you can achieve a robust, scalable, and performant pipeline. This preserves historical data for auditing and analytics while optimizing resources and minimizing issues related to late-arriving data or reconciliation failures.

I hope this helps you strategize your approach effectively! If you need further assistance, feel free to ask!