How do you migrate legacy ETL workloads to Databricks?
We inventory the estate, rank each job by agent suitability, then run supervised coding agents that convert legacy SQL, stored procedures and batch PySpark into Delta Lake pipelines governed by Unity Catalog. Every conversion lands as a pull request with generated tests and a human review gate; agents never touch production data directly.
How do you estimate Databricks migration timeline and TCO?
Through the fixed-fee Databricks migration cost assessment framework above. Week one inventories your estate and interviews your team; week two runs a measured proof slice on your real code. You receive an agent-suitability map, the projected human/agent split, effort and cost estimated both ways (manual and agent-assisted), and an execution plan you keep whether or not you hire us to execute.
Which source platforms can be migrated to Databricks?
Legacy data warehouses such as Teradata, Synapse and Snowflake, on-prem Hadoop and Spark clusters, and batch PySpark or SQL pipelines. The method is the same: inventory, agent-suitability scoring, automated Spark and SQL code conversion with human review, and Unity Catalog governance on the target.
What are the most common Databricks migration challenges and risks?
Undocumented legacy logic, missing test coverage, dialect differences in SQL and Spark, governance gaps when moving to Unity Catalog, and stalled timelines when senior engineers are pulled onto conversion work. We address them with automated test backfilling, agent-assisted conversion under review, and a governed target architecture defined before code moves.
Does agent-assisted migration expose our code or data to public models?
No. Agent planning stays in the vendor cloud, but all repo cloning, shell commands and builds run on your infrastructure via outbound-only connections, with least-privilege credentials from your vault and package installs restricted to internal registries. Details on the private-execution runtime for Devin.
