Delta Live Tables (DLT) in a production environment

[serelk]

I’m keen to learn more about employing Distributed Ledger Technology (DLT) in a production environment. Databricks is indeed promoting Delta Live Tables (DLT) as a comprehensive framework for creating robust, maintainable, and testable data processing pipelines.

In our recent project, we've opted to use DLT to construct the bronze layer within a Medallion architecture. Our main concern comes from DLT’s lifecycle being closely tied to its associated data. Specifically, if a DLT pipeline is deleted, all the linked data risks being erased as well. Clearly, no customer can afford to lose months of valuable data due to a single error.

I’m interested to know how others are managing this scenario. Are there efficient recovery procedures in place to handle accidental deletions of a DLT pipeline? Any shared experiences or recommendations would be greatly appreciated."

[serelk]

Just a small remark: I definitely meant Delta Live Tables, not Distributed Ledger Technology (DLT)

[Kaniz_Fatma]

Hi @serelk, Managing disaster recovery for DLT pipelines is crucial, especially when valuable data is at stake.

Let’s explore some strategies and practices:

Delta Live Tables (DLT) and Disaster Recovery:

• DLT simplifies and streamlines disaster recovery on Databricks. It offers automatic retries in case of failures and ensures exactly-once data processing.
• When data is ingested using DLT, it is processed exactly once. This property is useful for disaster recovery because identical DLT pipelines will produce the same ....
• DLT provides benefits such as accelerating ETL development, automating infrastructure management, en....

Error Handling and Recovery with DLT:

• DLT can handle transient errors and recover from common error conditions during pipeline operation.
• It allows users to set the latency of data updates to target tables without complex stream processin....

Recovery Procedures:

• Refresh Failed Tables: DLT provides an option to recover from the point of failure. You can use the “Refresh Failed Tables” feature to address accidental deletions or other issues.

Designing Disaster Recovery Policies:

• Consider multi-region or multicloud architectures to shield against cloud and service outages.
• Implement failover and failback coordination for robust disaster recovery.
• Test and document your disaster recovery procedures thoroughly.

Remember that the ultimate implementation will depend on your specific needs, data sources, and RTO/RPO requirements. By leveraging DLT’s features and following best practices, you can ensure data resilience and minimize risks in your production environment. 🚀