ORACLE → SNOWFLAKE
Migrate Oracle to Snowflake without rebuilding everything by hand.
Datachecks combines specialized AI agents with migration experts to automate estate assessment, source-to-target mapping, Oracle SQL and PL/SQL translation, validation, and reconciliation across complex Oracle-to-Snowflake migration programs.
Supported objects and automation depth vary by source, target, and migration scope.
THE MIGRATION CHALLENGE
Oracle → Snowflake is more than moving tables.
The difficult work sits in Oracle-specific schemas, data types, procedural logic, dependencies, transformations, and the validation required to prove Snowflake behaves correctly after migration.
1
Automated with review
Data types and schema differences
Oracle structures do not always map directly to Snowflake. NUMBER precision and scale, VARCHAR2, DATE and TIMESTAMP behavior, LOB handling, defaults, constraints, sequences, and schema conventions all require target-aware mapping decisions.
2
Expert review
PL/SQL and procedural logic
Packages, stored procedures, functions, cursors, exception handling, triggers, and dynamic SQL often contain years of embedded business logic. Some patterns can be translated systematically; others require redesign or expert review.
3
Accelerated translation
Oracle-specific SQL
Oracle functions, hierarchical queries, MERGE patterns, sequence usage, date handling, analytical SQL, temporary structures, and other dialect-specific constructs need Snowflake-aware translation.
4
Automated discovery
Object dependencies
Tables, views, procedures, functions, jobs, and transformation logic are interconnected. Migration sequencing becomes risky when upstream and downstream dependencies are not understood before conversion begins.
5
Human-owned
Transformation and business rules
Legacy Oracle estates often contain cleansing logic, derived fields, defaulting rules, transformation patterns, and historical exceptions that need to be preserved or intentionally redesigned on Snowflake.
6
Automated
Validation and reconciliation
A successful migration requires more than row counts. Data values, aggregates, transformations, business rules, and source-to-target results need to be checked and exceptions resolved before cutover.
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 migration-ready view of the Oracle estate.
Inventory schemas, tables, views, procedures, functions, dependencies, and data patterns; profile the estate and identify high-complexity objects before conversion starts.
Migration-ready Oracle estate inventory
02 · MAP
Source → Target Mapping
Generate Oracle-to-Snowflake mappings at scale.
Create table, column, data-type, and transformation mappings while surfacing ambiguous or low-confidence decisions for migration-expert review.
Reviewed source-to-target mappings
03 · TRANSLATE
SQL + Procedural Translation
Accelerate Oracle SQL and PL/SQL conversion.
Translate supported Oracle SQL and procedural patterns into Snowflake-compatible target logic, validate generated outputs, and route unsupported or ambiguous constructs for expert review.
Target-ready Snowflake logic with exceptions surfaced
04 · VALIDATE
Testing + Reconciliation
Prove Snowflake matches the intended Oracle result.
Generate tests, compare source and target data, validate transformations, reconcile counts and aggregates, analyze mismatches, and capture migration evidence before production cutover.
Validated Snowflake outputs with reconciliation evidence
MIGRATION EVIDENCE
Every stage leaves behind something your team can review.
SOURCE → TARGET MAPPING
CUSTOMER_ID NUMBER(18) → CUSTOMER_KEY NUMBER(18,0)
TRANSFORMATION: TRIM + UPPER · CONFIDENCE 97% · REVIEWED
TRANSLATION
Oracle PL/SQL → Snowflake target logic
TRANSLATED · VALIDATED
EXCEPTION
Dynamic SQL with runtime object reference
EXPERT REVIEW REQUIRED
DELIVERY TIME
Compress months of repetitive Oracle migration work into weeks.
Automate estate analysis, mapping generation, repetitive SQL conversion, test generation, and reconciliation while migration experts concentrate on complex PL/SQL, business rules, and critical exceptions.
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 reach cutover hoping Snowflake matches Oracle.
Validate translated logic and migrated data as Snowflake outputs become available, reconcile Oracle and Snowflake results, and resolve discrepancies 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
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