ORACLE → DATABRICKS
Rebuild Oracle warehouse logic on the Databricks lakehouse.
Datachecks combines specialized AI agents with migration experts to inventory Oracle estates, generate Delta table mappings, translate PL/SQL into Spark SQL and PySpark, and reconcile results across Oracle-to-Databricks modernization programs.
Supported objects and automation depth vary by source, target, and migration scope.
THE MIGRATION CHALLENGE
Oracle → Databricks is a re-architecture, not a port.
Oracle's relational and procedural model has no direct lakehouse equivalent. Schemas, PL/SQL, transaction semantics, and dependency chains need target-aware redesign before Delta tables and jobs can be trusted.
1
Architect-owned
Delta table modelling
Oracle tables do not map one-to-one onto Delta. Partitioning, file sizing, and medallion layer placement are target decisions that determine whether the lakehouse performs, and they have no source equivalent to copy.
2
Expert review
PL/SQL with no Spark equivalent
Packages, cursors, autonomous transactions, and package-level shared state assume a relational engine. Some convert to Spark SQL or PySpark cleanly; others need to be re-expressed as jobs or dropped entirely.
3
Accelerated translation
Oracle-specific SQL
Hierarchical CONNECT BY queries, MERGE patterns, sequences, analytic functions, and Oracle date handling need Spark-aware translation rather than syntax substitution.
4
Expert review
Transaction semantics
Oracle guarantees row-level transactional behaviour that Delta provides differently. Logic depending on savepoints, rollback, or read consistency across statements needs deliberate redesign.
5
Automated discovery
Dependency chains
Views, procedures, jobs, and downstream extracts are interconnected. On a lakehouse those relationships become workflow dependencies, which must be discovered before anything is scheduled.
6
Automated
Validation against Delta
Correctness is proven by comparing Oracle and Delta outputs across values, aggregates, and transformations, not by confirming that a table loaded.
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
Map the Oracle estate against a lakehouse target.
Inventory schemas, tables, views, packages, procedures, and dependencies, then classify which objects become Delta tables, which become jobs, and which should not be carried across at all.
Oracle estate scoped to a lakehouse target
02 · MAP
Source → Target Mapping
Generate Oracle-to-Delta mappings.
Map Oracle types to Spark types, decide partitioning and file layout, define medallion layer placement, and route ambiguous modelling decisions to expert review.
Reviewed Delta table mappings
03 · TRANSLATE
SQL + Procedural Translation
Translate PL/SQL into Spark SQL and PySpark.
Convert supported Oracle SQL and procedural logic into Spark SQL or PySpark, choosing per object which execution model fits, and surface constructs with no clean lakehouse equivalent.
Spark-ready logic with exceptions surfaced
04 · VALIDATE
Testing + Reconciliation
Prove the lakehouse matches the Oracle result.
Generate tests against Delta outputs, compare row counts and aggregates, validate transformations, and reconcile Oracle and Databricks before cutover.
Validated Delta outputs with reconciliation evidence
MIGRATION EVIDENCE
Every stage leaves behind something your team can review.
SOURCE → TARGET MAPPING
CUSTOMER_ID NUMBER(18) → customer_id BIGINT
TRANSFORMATION: TRIM + UPPER · CONFIDENCE 97% · REVIEWED
TRANSLATION
Oracle PL/SQL → Spark SQL / PySpark
TRANSLATED · VALIDATED
EXCEPTION
Package-level state shared across procedures
EXPERT REVIEW REQUIRED
DELIVERY TIME
Compress months of Oracle-to-lakehouse rebuild into weeks.
Automate estate analysis, Delta mapping generation, repetitive PL/SQL conversion, test generation, and reconciliation while experts focus on medallion design and procedural logic with no direct Spark equivalent.
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
Don't cut over to Delta on assumption.
Validate translated Spark logic and migrated Delta tables as outputs become available, reconcile against Oracle, and resolve exceptions before the legacy warehouse is retired.
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
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