MigryX parses every Oracle artifact — stored procedures, packages, triggers, views, and SQL scripts — with 2000+ built-in function mappings, full CONNECT BY rewriting, BULK COLLECT/FORALL translation, and complete package dependency resolution.
MigryX uses a purpose-built Oracle PL/SQL grammar — not a generic SQL parser. It resolves package dependencies, overloaded procedures, and schema-level references before conversion begins.
Generic SQL transpilers break on Oracle's proprietary syntax. MigryX was purpose-built to handle the constructs that make Oracle migrations difficult.
CONNECT BY, START WITH, NOCYCLE, LEVEL pseudo-column, PRIOR, SYS_CONNECT_BY_PATH, and CONNECT_BY_ROOT — all rewritten to equivalent recursive CTEs (WITH RECURSIVE) for Snowflake, BigQuery, and dbt.
Oracle array-processing patterns — BULK COLLECT INTO collections and FORALL SAVE EXCEPTIONS DML — converted to set-based SQL operations, batch upserts, or vectorized pandas operations.
Oracle packages with separate spec and body declarations fully resolved. Overloaded procedures, package-level variables, and cross-package references tracked with full dependency ordering.
EXECUTE IMMEDIATE with bind variables, DBMS_SQL cursor-based execution, and Native Dynamic SQL — analyzed for pattern, templated where possible, and annotated for manual review when fully dynamic.
Oracle built-in functions mapped to equivalents on every target. Covers string, date/time, numeric, conversion, analytical functions, and DBMS package procedures across all supported platforms.
BEFORE/AFTER row and statement triggers migrated to Snowflake Streams + Tasks, dbt hooks, or application-layer logic. Sequence .NEXTVAL patterns converted to AUTOINCREMENT or IDENTITY columns.
MigryX scans your Oracle schema — procedures, packages, triggers, views, synonyms, and sequences — building a complete object inventory with a dependency graph before any conversion begins.
Each object is parsed to an intermediate AST and converted using Oracle-specific transformation rules. Function mappings, syntax rewrites, and type adjustments are applied in a single deterministic pass.
Every converted object ships with a row-level validation test. MigryX runs converted code against the same inputs as the Oracle original and confirms output parity before project handover.
A sample of the 2000+ mappings MigryX applies automatically. Every mapping is documented in the lineage report.
| Oracle Function / Syntax | Snowflake | BigQuery | Databricks SQL |
|---|---|---|---|
| NVL(expr, val) | COALESCE(expr, val) | COALESCE(expr, val) | COALESCE(expr, val) |
| NVL2(expr, v1, v2) | IFF(expr IS NOT NULL, v1, v2) | IF(expr IS NOT NULL, v1, v2) | IF(expr IS NOT NULL, v1, v2) |
| DECODE(col, k, v, ...) | CASE WHEN col=k THEN v ... | CASE WHEN col=k THEN v ... | CASE WHEN col=k THEN v ... |
| ROWNUM | ROW_NUMBER() OVER(ORDER BY ...) | ROW_NUMBER() OVER(ORDER BY ...) | ROW_NUMBER() OVER(ORDER BY ...) |
| SYSDATE | CURRENT_TIMESTAMP() | CURRENT_TIMESTAMP() | CURRENT_TIMESTAMP() |
| seq.NEXTVAL | seq.NEXTVAL (Snowflake seq) | AUTO_INCREMENT / IDENTITY | IDENTITY / generated always |
| TO_DATE(str, fmt) | TO_DATE(str, fmt) | PARSE_DATE(fmt, str) | TO_DATE(str, fmt) |
| LISTAGG(col, sep) | LISTAGG(col, sep) | STRING_AGG(col, sep) | array_join(collect_list(col), sep) |
| TRUNC(date, fmt) | DATE_TRUNC(fmt, date) | DATE_TRUNC(date, fmt) | DATE_TRUNC(fmt, date) |
| CONNECT BY LEVEL <= n | GENERATOR(rowcount=>n) | GENERATE_ARRAY(0, n-1) | range(n) with explode |
MigryX generates code that is native to each target — each platform has its own transformation ruleset tuned for best practices and performance.
Snowflake Scripting for procedural logic, MERGE for SCD patterns, Snowpark Python for complex UDFs, and COPY INTO for bulk loads.
Standard SQL with BigQuery Scripting, partitioned and clustered tables, authorized views, and EXPORT DATA for Oracle file output equivalents.
Delta Lake MERGE INTO, Unity Catalog, Liquid Clustering, and Databricks SQL procedures for Oracle stored procedure equivalents.
PL/pgSQL stored procedures, PostgreSQL-native function equivalents, and schema-level object migration with full DDL output.
Dedicated SQL pool with distribution keys, PolyBase / COPY patterns, and Synapse stored procedures for Oracle procedure migration.
Redshift SQL with distribution styles, sort keys, COPY from S3, and Redshift stored procedures for Oracle PL/SQL equivalents.
Incremental models for MERGE patterns, snapshot models for SCD Type 2, source definitions, and generic tests for data quality.
Procedural Oracle logic translated to Python functions with SQLAlchemy for database operations and pandas for data transformation.
MigryX installs within your infrastructure. Your Oracle PL/SQL source code never leaves your network during analysis or conversion.
Windows Server or Linux. Connects to Oracle schemas via JDBC. No internet required during conversion runs.
Fully offline. Suitable for classified environments, regulated industries, and strict data residency requirements.
Azure Private VMs, AWS VPC, or GCP private network. Docker and Kubernetes deployment options available.
SOC 2, HIPAA, and FedRAMP-compatible patterns. Full audit log of every schema object parsed and converted.
Every engagement begins with a structured Oracle PL/SQL pilot — you see converted code and validation results before committing to full scope.
Whether you have 50 Oracle procedures or 50,000, MigryX has handled migrations of every scale. Tell us about your Oracle environment and we'll scope a pilot on your actual codebase.
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