# Agentic Data Platform Migration using Wire, Claude Code and Rittman Analytics URL: https://rittmananalytics.com/blog/2026/07/19/agentic-data-platform-migrations-using-wire-claude-code-and-rittman-analytics Description: Migrating an entire analytics platform including the data warehouse, data source extractions, semantic layer, BI reports, orchestration and reverse ETL syncs from one stack to another used to be the… Author: Mark Rittman Published: 2026-07-20T00:52:42+01:00 Modified: 2026-08-22T16:48:35+01:00 --- ![Agentic Data Platform Migration using Wire, Claude Code and Rittman Analytics](https://rittmananalytics.com/api/public/og-image?src=https%3A%2F%2Fi0.wp.com%2Fdtdtemp8.wpcomstaging.com%2Fwp-content%2Fuploads%2F2026%2F07%2FData-Pipeline-Diagram-selection-1-1.png%3Ffit%3D3840%252C1640%26ssl%3D1) Migrating an entire analytics platform including the data warehouse, data source extractions, semantic layer, BI reports, orchestration and reverse ETL syncs from one stack to another used to be the kind of project you hoped wouldn’t land while you were on the bench, because there was every chance you’d be asked to lead it. Large-scale data platform migrations are less about writing SQL than managing risk. Thousands of models, connectors, orchestration workflows, semantic objects, dashboards and reverse ETL syncs all need to be tracked, migrated, regression tested and reassembled into a platform that behaves exactly like the one it replaces. It’s the kind of work many engineers see as a chore rather than an opportunity to innovate, usually delivered under fixed-price contracts where keeping to the timetable and proving every change through exhaustive testing matters far more than moving fast or trying something new. A typical analytics platform you’d be asked to migrate would have one or more ingestion pipelines using technology such as Fivetran, Stitch or homegrown python scripts, loading data into a Snowflake data warehouse built-around a medallion-style architecture. Data transformations would be handled by dbt, orchestrated using dbt Cloud and data then synced into downstream CRM or other SaaS applications using reverse ETL technology such as Census or Hightouch. ![](https://rittmananalytics.com/api/public/og-image?src=https%3A%2F%2Fi0.wp.com%2Fdtdtemp8.wpcomstaging.com%2Fwp-content%2Fuploads%2F2026%2F07%2FData-Pipeline-Diagram-selection-1-2.png%3Fresize%3D3840%252C1640%26ssl%3D1) **The Perfect Use-Case for Agentic Delivery** Whilst a complex and thankless task for us humans to work-on, these types of projects are however a perfect use-case for AI-based agentic coding tools such as [Rittman Analytics’ Wire Framework](https://www.rittmananalytics.com/services/wire-framework). Why? Because unlike building a whole new platform or user application where users’ requirements can be interpreted in many ways and there’s no one “correct” answer to what they want, a data platform migration in the end just works, or it doesn’t. All of the individual Fivetran ingestions, dbt models, reports and reverse ETL syncs either provide the same output as before, or they’re defective and if they’re the latter, a coding agent can just fix and iterate and fix until it does get that same output, and then all of these migrated items outputting the same results as the originals adds-up to a completely migrated platform that’s functionally-identical to the one it’s replacing. And if there are tens, hundreds or thousands of these dbt models, reports or other migration inventory item to individually translate, test for equivalence and pass for migration, then technologies such as [Claude Code’s sub-agents](https://code.claude.com/docs/en/sub-agents) can chunk the work up into batches and parallel work streams, with your main Claude code agent orchestrating the process and reporting back to you when everything is done. The Wire Framework is Rittman Analytics’ AI-accelerated delivery system for data platform engagements. It uses **Claude Code** (Anthropic) or Gemini CLI (Google) as its runtime and encodes 20+ years of our data engineering and platform migration methodology as structured, executable agentic workflow specifications. Instead of one of our consultants’ manually writing dbt models, LookML, pipeline code or in this case, translated dbt models over several weeks, Wire directs the AI coding agent to produce all of these artefacts in a fraction of the time with embedded quality gates ensuring the output meets our standards. You can read more about Wire and our approach to agentic project delivery at: - [Wire Framework homepage](https://www.rittmananalytics.com/services/wire-framework) on rittmananalytics.com - [Wire Claude Code Plugin](https://github.com/rittmananalytics/wire-plugin) and [installation instructions](https://docs.rittmananalytics.com/en/latest/docs/getting-started/installation) - [Wire Documentation site](https://docs.rittmananalytics.com/en/latest/docs/intro) and [Data Platform Migration tutorial](https://docs.rittmananalytics.com/en/latest/docs/tutorials/platform-migration) - [Book a free discovery call with Mark Rittman](https://calendly.com/markrittman/initial-discovery-call-with-mark-rittman) to discuss a potential Wire project