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From Dlt To Lakeflow Declarative Pipelines: A Practical Migration Playbook

Delta Live Tables (DLT) has been a game-changer for building ETL pipelines on Databricks, providing a declarative framework that automates orchestration, infrastructure management, monitoring, and data quality in data pipelines. By simply defining how data should flow and be transformed, DLT allowed data engineers to focus on business logic rather than scheduling and dependency management. Databricks expanded and rebranded this capability under the broader Lakeflow initiative. The product formerly known as DLT is now Lakeflow Spark Declarative Pipelines (SDP), essentially the next evolution of DLT with additional features and alignment to open-source Spark.

The existing DLT pipelines are largely compatible with Lakeflow; your code will still run on the new platform without immediate changes. However, to fully leverage Lakeflow’s capabilities and future-proof your pipeline, it’s recommended that you update your code to the new API. This playbook provides a practical, engineer-focused guide to migrating from DLT to Lakeflow declarative pipelines with side-by-side code examples, tips, and coverage of edge cases. We’ll focus on the migration logic, the code changes, and pipeline definition adjustments, rather than tooling or deployment, assuming you’re using Databricks with Spark/Delta Lake as before.

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From Dlt To Lakeflow Declarative Pipelines: A Practical Migration Playbook

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