ORACLE → BIGQUERY
Move Oracle onto BigQuery without carrying the relational model across.
Datachecks combines specialized AI agents with migration experts to inventory Oracle estates, generate BigQuery-native mappings, translate PL/SQL into GoogleSQL, and reconcile results before cutover.
Supported objects and automation depth vary by source, target, and migration scope.
THE MIGRATION CHALLENGE
Oracle → BigQuery replaces tuning with design.
BigQuery has no indexes, no enforced constraints and no server to size. Performance and cost come from partitioning, clustering and how much data each query scans, so Oracle's tuning assumptions have to be unlearned rather than translated.
1
Architect-owned
No indexes, no tuning knobs
BigQuery has no indexes and no server to size. Query speed and cost come from partitioning, clustering and how much data each query scans, so Oracle tuning intent has to be re-expressed rather than ported.
2
Automated with review
Oracle data types and precision
NUMBER precision and scale, VARCHAR2, DATE and TIMESTAMP semantics, and LOB handling all need explicit GoogleSQL mappings, and several have no exact counterpart.
3
Expert review
PL/SQL and packages
Packages, cursors, autonomous transactions and shared package state assume a relational engine. Some convert to GoogleSQL scripting; others need to be rebuilt as pipeline steps.
4
Expert review
Constraints are not enforced
Primary and foreign keys are informational in BigQuery. Logic that relies on the database rejecting bad rows has to move into the pipeline or into validation.
5
Architect-owned
Nested and repeated fields
BigQuery models one-to-many relationships with STRUCT and ARRAY. Copying Oracle joins directly works but often costs more than remodelling, and that is a per-table judgement.
6
Automated
Validation against a scanned target
Correctness is proven by comparing Oracle and BigQuery values, aggregates and transformations, not by confirming a load completed.
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
Understand the Oracle estate against a serverless target.
Inventory schemas, tables, views, packages and procedures, profile volumes and access patterns, and identify which objects justify migration to a query-priced platform.
Oracle estate scoped for BigQuery
02 · MAP
Source → Target Mapping
Generate BigQuery-native mappings.
Map Oracle types to GoogleSQL types, decide partitioning and clustering per table, and flag where nested and repeated fields would model the data better than a direct relational copy.
Reviewed BigQuery table design
03 · TRANSLATE
SQL + Procedural Translation
Translate PL/SQL into GoogleSQL.
Convert supported Oracle SQL and procedural logic into GoogleSQL and scripting, and surface constructs that depend on sequences, hierarchical queries or transaction semantics BigQuery handles differently.
GoogleSQL with exceptions surfaced
04 · VALIDATE
Testing + Reconciliation
Prove BigQuery returns the Oracle answer.
Generate tests, compare row counts and aggregates, validate transformations, and reconcile Oracle and BigQuery results before production cutover.
Validated BigQuery outputs with reconciliation evidence
MIGRATION EVIDENCE
Every stage leaves behind something your team can review.
SOURCE → TARGET MAPPING
CUSTOMER_ID NUMBER(18) → customer_id INT64
TRANSFORMATION: TRIM + UPPER · CONFIDENCE 97% · REVIEWED
TRANSLATION
Oracle PL/SQL → GoogleSQL
TRANSLATED · VALIDATED
EXCEPTION
Hierarchical CONNECT BY query with no direct equivalent
EXPERT REVIEW REQUIRED
DELIVERY TIME
Compress months of Oracle-to-BigQuery conversion into weeks.
Automate estate analysis, mapping generation, repetitive PL/SQL conversion, test generation and reconciliation while experts own partitioning strategy, cost modelling and procedural logic that needs redesign.
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 discover cost or drift after cutover.
Validate translated GoogleSQL and migrated tables as outputs become available, reconcile against Oracle, and resolve exceptions before the legacy database 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
FAQ
Frequently asked questions
How is Oracle PL/SQL handled in BigQuery?
What replaces Oracle indexes and partitioning in BigQuery?
Should Oracle's normalised schema stay normalised?
How are Informatica or ODI pipelines migrated?
Do Oracle data types translate cleanly to BigQuery?
How are Oracle and BigQuery 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