ORACLE → FABRIC
Bring Oracle estates into Microsoft Fabric with proof of parity.
Datachecks combines AI-driven Oracle estate analysis with migration experts to design the OneLake target, translate PL/SQL, validate migrated data and reconcile results before reporting moves across.
Supported objects and automation depth vary by source, target, and migration scope.
THE MIGRATION CHALLENGE
Oracle → Fabric crosses both vendor and architecture.
This is a move from a proprietary relational engine into a Delta-backed, capacity-priced platform. Oracle data types, PL/SQL and transaction behaviour all need target-aware redesign, not a driver swap.
1
Automated with review
Cross-vendor type mapping
Oracle NUMBER precision, VARCHAR2, DATE and TIMESTAMP semantics and LOB handling need deliberate Fabric mappings. Nothing carries across implicitly.
2
Expert review
PL/SQL has no Fabric analogue
Packages, cursors, autonomous transactions and shared package state assume Oracle's engine. Some logic converts to Fabric T-SQL or Spark; some has to be rebuilt as pipelines.
3
Architect-owned
Warehouse or Lakehouse per object
Every migrated object needs a decision about where it lives in Fabric, and that decision changes how it is written, queried and secured.
4
Expert review
Sequences and identity
Oracle sequences and trigger-driven identity patterns behave differently in Fabric, and anything assuming gapless numbering needs review.
5
Automated discovery
Long-lived dependencies
Mature Oracle estates accumulate views, jobs, extracts and downstream applications built on the same objects over many years. Missing one surfaces at cutover.
6
Automated
Parity before reporting moves
Fabric usually lands alongside a Power BI reporting change. Reconciliation has to prove the numbers match before the semantic layer is repointed.
HOW DATACHECKS HELPS
Understand. Map. Translate. Validate.
The migration is executed through controlled agent workflows, with migration experts reviewing ambiguity, unsupported patterns, business rules, and critical exceptions.
01 · UNDERSTAND
Assessment + Discovery
Build a Fabric-ready view of the Oracle estate.
Inventory schemas, tables, views, packages and procedures, profile the data, and classify which objects belong in a Warehouse, a Lakehouse, or neither.
Oracle estate mapped to Fabric items
02 · MAP
Source → Target Mapping
Map Oracle structures into OneLake.
Translate Oracle types into Fabric equivalents, decide Warehouse or Lakehouse placement per object, and route ambiguous modelling choices for review.
Reviewed Fabric mappings
03 · TRANSLATE
SQL + Procedural Translation
Convert PL/SQL for the Fabric surface.
Translate supported Oracle SQL and procedural logic into Fabric Warehouse T-SQL or Spark, and surface packages, sequences and transaction-dependent patterns that need redesign.
Fabric-ready logic with exceptions surfaced
04 · VALIDATE
Testing + Reconciliation
Reconcile Oracle and Fabric before cutover.
Generate tests, compare source and target results, validate transformations, reconcile critical datasets, and capture evidence before the legacy platform is retired.
Validated Fabric outputs with migration evidence
MIGRATION EVIDENCE
Every stage leaves behind something your team can review.
SOURCE → TARGET MAPPING
CUSTOMER_ID NUMBER(18) → CustomerId BIGINT
TRANSFORMATION: TRIM + UPPER · CONFIDENCE 97% · REVIEWED
TRANSLATION
Oracle PL/SQL → Fabric T-SQL
TRANSLATED · VALIDATED
EXCEPTION
Package-level state with no Fabric equivalent
EXPERT REVIEW REQUIRED
DELIVERY TIME
Compress months of Oracle modernization into weeks.
Automate estate analysis, target design, repetitive PL/SQL conversion, test generation and reconciliation while experts own architecture and capacity decisions.
TRADITIONAL MIGRATION
MONTHS
Understand
Map
Translate
Test & validate
Reconcile
Cutover
WITH DATACHECKS
WEEKS
Understand
Map
Translate + test
Validate + reconcile
Cutover
Why the timeline shrinks: automated estate analysis · generated mappings · accelerated SQL translation · generated tests · automated reconciliation. Bar lengths are illustrative, not project commitments.
MIGRATION CONFIDENCE
Validate Fabric before switching off Oracle.
Compare migrated datasets and transformed outputs against Oracle, reconcile critical measures, and resolve exceptions before production cutover.
TRANSLATE
TEST ✓
TARGET LOAD
VALIDATE ✓
RECONCILE ✓
EXCEPTIONS REVIEWED ✓
READY FOR CUTOVER
HUMAN IN THE LOOP
Automate the repeatable work. Keep experts on the decisions.
AGENT EXECUTION
Estate inventory · metadata analysis · profiling · standard mappings · common SQL translation · test generation · row-count checks · aggregate comparisons
EXPERT REVIEW
Ambiguous mappings · unsupported procedural patterns · complex logic · unusual transformation patterns · reconciliation discrepancies
HUMAN-OWNED
Business-rule interpretation · critical exception resolution · acceptance criteria · migration scope decisions · cutover approval
ENTERPRISE DEPLOYMENT
Run the migration engine where the data lives.
Deploy Datachecks within enterprise-controlled infrastructure, connect approved AI models, and keep migration data, metadata, execution, and evidence inside your security boundary.
Private Deployment
BYOM
SAML SSO
SOC 2
ISO 27001
RELATED RESOURCES
Go deeper on this migration.
FAQ
Frequently asked questions
Why move Oracle to Microsoft Fabric rather than another platform?
How is Oracle PL/SQL converted for Fabric?
Where should Oracle data land, Lakehouse or Warehouse?
What happens to Oracle indexes, partitions and materialized views?
How do Informatica or ODI pipelines move to Fabric?
How are Oracle and Fabric results reconciled?
PLANNING THIS MIGRATION?
Start with the estate you already have.
Share your source environment, target architecture, approximate object volumes, and migration goals. We’ll help identify complexity, scope, and where automation can remove manual delivery work.
Useful to bring: source schemas · object counts · procedural code volume · target architecture · timelines