Xtract Universal quietly becomes the most important undocumented system in the estate. This accelerator reads the server configuration, inventories every extraction and maps each one onto a SAP Datasphere ingestion pattern. You get a migration workbook with a verdict per extraction instead of a week of clicking through the Designer.
Over five or six years a team accumulates hundreds of extractions: tables, ODP extractors, BAPI calls, joins, custom ABAP report reads, each with its own filters, delta strategy and destination. The person who built them has usually left. When Datasphere lands on the roadmap, the first question is what do we have and what does each one become, and answering it by hand means opening every extraction one at a time.
Extraction definitions sit inside the Xtract Universal server configuration. No diff, no review history, no way to reason about several hundred of them in bulk. Every question about scope turns into manual inspection.
BAPI and RFC based extractions, custom ABAP report reads and destination side row filters need a functional redesign. Without an upfront classification you discover which ones halfway through the project, after the timeline is committed.
A pointer column, a timestamp watermark or an ODP delta queue each has to land on the right replication flow or CDS delta pattern. Getting it wrong produces data that looks correct until a month end reconciliation says otherwise.
The accelerator reads the Xtract Universal server configuration directly and builds one row per extraction, then classifies each row against a catalogue of Datasphere ingestion patterns. Everything it produces is a proposal: a human reviews the delta strategy, the ODP context choice, SAP authorisation impact and every extraction marked as needing redesign before anything is deployed.
Extraction, destination and scheduler definitions are parsed into a single table: type, source object, selected fields, filter and selection criteria, delta method, destination and schedule. Exported as a workbook and as structured data.
Rule based mapping puts each extraction into a Datasphere pattern: replication flow on an ABAP connection, ODP replication with the matching context, remote table, transformation flow, or no direct equivalent. The reason for the verdict is recorded per row.
Every extraction whose current delta mechanism has no clean equivalent gets flagged. The risky ones surface in week one of the project rather than during the first reconciliation after go live.
Each extraction is linked to its landing tables in SQL Server, ADLS or Fabric and, where the metadata is reachable, to the downstream models that read them. You know what breaks when a name changes.
Custom ABAP report reads and BAPI parameter sets resist rule based mapping. An LLM pass describes what each one is actually doing in business terms, so a functional consultant can confirm or correct it.
For the cleanly mappable cases: proposed space allocation, target entity naming, target structures and a build order. Skeleton definitions ready for review before anything is imported into Datasphere.
Source parsing covers the Xtract Universal server configuration and, where a connection is available, SAP side metadata for the objects being pulled. Target mapping covers Datasphere spaces, connections and flows. Landing zone inspection covers the places Theobald destinations usually point. The tooling itself is Python, running locally or as an Azure container job, with Claude used for the classification and interpretation steps.
If you run Theobald Xtract Universal and Datasphere is on the roadmap, get in touch for a discovery run against your server configuration. The inventory and classification pass is the fastest way to find out whether you are facing straightforward replication flows or a handful of extractions that need a functional redesign.