Databricks Migration Partner

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.

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.

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.

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.

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.

Yes. Legacy ETL migration is the core of our practice, and Informatica is our deepest tool. Our team has years of hands-on PowerCenter experience: mappings, workflows, mapplets, parameter files, and the undocumented tribal logic that accumulates in a decade-old repository. Our approach starts by inventorying your repository and classifying every mapping by complexity and agent suitability. Straightforward mappings are converted to governed Delta Live Tables pipelines with agent assistance and human review; the gnarly ones (complex transformations, embedded stored procedures, dependencies nobody remembers) get flagged for practitioner-led conversion. You see the full map before anything moves. The two-week Migration Assessment includes a proof slice: real mappings from your repository, converted and measured, so you know the timeline and cost before committing to the migration.

Yes, with the same methodology. We inventory the DataStage project, classify every job by complexity and agent suitability, then convert parallel and sequence jobs, stages, shared containers and parameter sets into governed Delta Lake pipelines with agent assistance and human review on every change. Our tool depth is deepest in PowerCenter, but our practitioners have worked inside DataStage estates and the surrounding IBM stack (Db2, Netezza, Red Hat OpenShift), and the Migration Assessment proof slice uses your real jobs so the estimate reflects your project, not a generic benchmark.

For the right mappings, yes; for the wrong ones, no, and knowing which is which is the job. Every mapping is classified before conversion. Straightforward, well-patterned mappings are converted by agents, and every conversion ships as a pull request with generated tests and a practitioner review before it merges. Complex transformations, embedded stored procedures and undocumented dependencies are flagged for practitioner-led conversion rather than forced through an agent. The human/agent split is measured in the assessment proof slice, so the reliability claim is tested on your repository before you commit to the migration.