How to migrate what you couldn't afford to migrate

The estate you shelved is now a 3 to 6 month project. Xebia Axis, migration to BigQuery, and Gemini Enterprise on the other side.

Somewhere in your organisation there's a migration plan that never got signed off. Oracle or Teradata, a few thousand ETL jobs, 18 months, and a number with too many zeroes and too little evidence behind it.That number came from a handful of proof-of-concepts and 20 years of collective gut feel. Nobody read the whole estate, because reading it by hand would take a team half a year before a single line of code moved.

Agents read it in 2 to 4 weeks. Every ETL job, every DDL schema, every pipeline dependency, scored for complexity and estimated file by file. The output is a migration blueprint with a fixed date and a fixed fee attached, and delivery that runs 3 to 6 months at roughly 40% less cost than the traditional migration.

The business case changes, and work that looked too expensive or too difficult to start is now deliverable in a few months.

On the morning of October 22 at Google's Warsaw office, we walk the whole path. How Xebia combines Google's migration products with its own internal accelerators, driven by an agentic approach, to move legacy data platforms to BigQuery faster and with verified quality. And what Gemini Enterprise gives your people once the data is there, including agents built on BigQuery and conversational access to it.

29% of organisations have a central data lake or warehouse fully in place today, according to the Data & AI Monitor 2026. Gartner expects more than 4 in 10 agentic AI projects to be scrapped by 2027, with weak data governance behind a growing share of those failures.

Meanwhile, the legacy warehouse keeps billing. License renewals, a team spending its week on breakages, and every AI use case waiting on data nobody trusts yet. Migration that would fix it stays shelved, because no one can put a credible date or price on it.

• CDO/Head of Data who owns AI readiness and needs a plan that survives a budget conversation
  
  
• CIO/CTO carrying the risk on a legacy platform: cost, exit timing, and whether the next migration attempt actually lands 
  
  
• Senior Data Platform Engineers & Principal Engineers responsible for target-state design on Google Cloud, dependency mapping and cutover 
  
  
• Data Programme & Delivery Leads who have been asked to commit to a date for a migration nobody has fully mapped

Xebia Axis, and what it changes about migration

Xebia Axis is an agentic data foundation built on 6 modules that cover the data lifecycle: Readiness, Platform, Knowledge, Migration, Observability and Operations. Agents read, convert and monitor. Engineers set the direction, review the output and sign off. Access control, audit trails and data residency are part of how the agents run from day 1, so the conversation with your CISO starts earlier than usual.

Most of the session goes to the 2 modules that carry a migration: Axis for Readiness and Axis for Migration. Moving off Teradata, Netezza, Hadoop or an on-prem warehouse to BigQuery succeeds or fails on three disciplines: a rigorous assessment that profiles workloads, lineage, and cost before a single line is moved; an automated code migration of SQL, ETL, and orchestration logic; and a systematic reconciliation that proves the target produces the same results as the source at scale. In the AI era all three matter more than they used to. Agents convert and remediate thousands of objects in parallel, and what turns that raw speed into a reliable migration is the combination of deterministic conversion rules, quality gates and a feedback loop the agents learn from.

Register now!

Register to join on October 22 and leave with the 3 answers you'd need before approving a migration: what's actually in your estate, what it costs to move, and what your people can do with it once it's in BigQuery.

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