SAS2PY converts SAS and SQL dialects directly to Google BigQuery — BigQuery ML, Dataform, Dataproc PySpark, Dataplex governance, Vertex AI, and BigLake — with +95% parsing accuracy and column-level lineage.
Google Cloud Targets
Every modernization generates production-ready Google Cloud artifacts — leveraging BigQuery's serverless architecture, Dataform SQL pipelines, Dataproc Spark, Cloud Composer orchestration, Vertex AI, and Dataplex governance.
Legacy SQL and ETL logic translated to BigQuery Standard SQL — partitioned & clustered tables, materialized views, authorized views, and row-level security policies.
ETL pipelines converted to Dataform SQL workflows — SQLX definitions, table declarations, incremental models, assertions, and Git-backed CI/CD deployment via Dataform repositories.
Heavy-lifting ETL converted to Dataproc PySpark jobs — serverless or cluster-based execution on Google Cloud, with BigQuery Spark connector for direct table read/write.
Streaming and complex batch pipelines modernized to Apache Beam / Dataflow — fully managed, auto-scaling, with native BigQuery sinks and Pub/Sub source integration.
Legacy scheduler and ETL orchestration replatformed to Cloud Composer — DAG-based scheduling, BigQueryOperator tasks, dependency management, and SLA monitoring.
SAS analytical and scoring models converted to BigQuery ML (CREATE MODEL SQL) and Vertex AI — AutoML, feature engineering, model registry, and online/batch prediction endpoints.
Legacy data lake assets modernized to BigLake external tables or BigQuery-managed Apache Iceberg tables — open format on GCS with BigQuery query performance and fine-grained access control.
Column-level lineage, STTM mappings, data quality rules, and business glossary terms published to Google Dataplex — automated data catalog tagging and policy propagation.
Legacy ETL visual workflows converted to Cloud Data Fusion pipelines — CDAP-based visual DAGs with BigQuery sinks, Wrangler transforms, and reusable plugin configurations for batch and real-time ingestion.
Modernization Sources
Purpose-built parsers for each source platform. Not generic scanners. Every conversion produces explainable, auditable, BigQuery-native code — Standard SQL, Dataform SQLX, or Dataproc PySpark.
Automate SAS Base, Macro, PROC SQL, and IML conversion to BigQuery Standard SQL and Dataproc PySpark. DATA step logic, FORMAT/INFORMAT, PROC SORT/MEANS/FREQ — with PROC MODEL landing in BigQuery ML or Vertex AI AutoML.
Modernize Oracle PL/SQL procedures, packages, and triggers with 2000+ function mappings, CONNECT BY → recursive CTE rewriting, BULK COLLECT → BigQuery ARRAY_AGG, and full package dependency resolution.
Transpile SQL from Oracle, T-SQL, Teradata, DB2, Netezza, Greenplum, Hive HQL, and Vertica to BigQuery Standard SQL — 500+ function mappings, ARRAY/STRUCT nested data handling, and partitioning advisories.
Modernize SAS DataFlux dfPower Studio jobs and DQ schemes — standardize/parse/match/validate patterns — to BigQuery SQL data quality routines and Dataplex data quality rules with Vertex AI anomaly detection.
Before you modernize, map your estate. Compass extracts column-level lineage, STTM, and dependency graphs from any source — and publishes them directly to Google Dataplex for catalog, tags, and data quality governance.
How It Works
The same proven methodology applies to every source — SAS — all landing natively on Google Cloud.
Upload source artifacts — SAS scripts — into SAS2PY for parsing.
Custom parsers build complete ASTs, expand macros, resolve dependencies, and produce column-level lineage — with BigQuery readiness and partition/cluster key scoring.
Parser-driven conversion to BigQuery Standard SQL, Dataform SQLX, Dataproc PySpark, or Cloud Composer DAGs — with BigQuery best-practice partitioning, clustering, and materialized view patterns.
Row-level and aggregate data matching between legacy and BigQuery outputs — Dataplex data quality scans for audit-ready parity evidence and go-live sign-off.
Publish lineage, STTM, and data contracts to Dataplex. Merlin AI recommends partition strategies, slot reservations, and BigQuery ML vs. Vertex AI routing per workload.
Platform Capabilities
Every SAS2PY modernization is engineered for the full Google Cloud stack — BigQuery serverless analytics, Dataform SQL pipelines, Dataproc Spark, Dataplex governance, and Vertex AI for analytical models.
Purpose-built for each source language. SAS macro expansion — full fidelity, deterministic output, no approximation.
Legacy SQL translated to BigQuery Standard SQL — partitioned tables (time, range), clustered tables, materialized views, authorized views, row-level security, and INFORMATION_SCHEMA metadata queries.
ETL workflows converted to Dataform SQLX — incremental table models, table declarations, data assertions, tags, and Git-backed CI/CD via Dataform repositories and workflow configurations.
Source-to-target column mappings and STTM tables published to Google Dataplex — automated Data Catalog tags, policy tags for column-level security, data quality scans, and Lineage API integration.
AI analyzes parsed metadata to recommend partition keys, clustering columns, and slot reservation sizing. SAS PROC MODEL and scoring logic land in BigQuery ML or Vertex AI AutoML automatically.
Full deployment behind your firewall. Source code and lineage never leave your network. Dataform Git integration for dev → staging → production promotion. SOX, GDPR, BCBS 239 ready.
Cloud Data Fusion
SAS2PY converts legacy ETL into Cloud Data Fusion pipelines — generating both SQL-based and idiomatic CDAP approaches depending on transformation complexity.
When legacy ETL logic is fundamentally SQL — SELECT, JOIN, GROUP BY, window functions, MERGE — SAS2PY generates Data Fusion pipelines that push SQL directly to BigQuery. The pipeline becomes a thin orchestration layer while BigQuery's serverless engine handles the heavy compute.
When legacy ETL involves visual dataflow logic — multi-branch routing, row-level transformations, conditional splits, custom functions — SAS2PY generates native CDAP plugins that map 1:1 to legacy transformation semantics inside Data Fusion's visual pipeline designer.
Each legacy source maps differently. SAS DATA step logic becomes Data Fusion Wrangler transforms. PROC SQL becomes BigQuery pushdown pipelines. SAS2PY chooses the right Data Fusion pattern for each source construct.
SAS2PY produces Data Fusion pipeline JSON (CDAP artifact spec) ready for import — sources, transforms, sinks, connections, and error handling pre-configured. No manual assembly. Deploy directly via the Data Fusion REST API or Studio UI.
Real-world ETL mixes SQL and procedural logic. SAS2PY generates hybrid pipelines that push SQL to BigQuery for set operations and use CDAP plugins for row-level transforms — combining the best of both approaches in a single Data Fusion pipeline.
Legacy batch ETL converts to Data Fusion batch pipelines. Event-driven or near-real-time sources convert to Data Fusion real-time pipelines with Pub/Sub sources and streaming BigQuery sinks — preserving the original processing semantics.
Every generated Data Fusion pipeline includes lineage metadata. Column-level source-to-target mappings are published to Dataplex Data Catalog automatically — ensuring governance continuity from legacy ETL to Data Fusion without manual documentation.
SAS2PY validates Data Fusion pipeline outputs against legacy system outputs — row counts, aggregate comparisons, and column-level data matching. Evidence-based parity reports prove the modernization is correct before cutover.
SAS2PY's parser analyzes each legacy workflow and classifies every transformation as SQL-expressible or plugin-required. Pure SQL flows get BigQuery pushdown pipelines. Visual dataflow logic gets CDAP plugins. Mixed workloads get hybrid pipelines. The decision is automatic, auditable, and overridable.
Measurable Results
Organizations using SAS2PY to land on BigQuery accelerate delivery, eliminate manual rewrite cost, and unlock BigQuery's serverless performance and Vertex AI capabilities from day one.
Automated lineage extraction and parser-driven analysis eliminate months of manual discovery and rewrite work.
Complete dependency visibility prevents production incidents and modernization-related data defects on BigQuery.
Automated conversion, accelerated time-to-value, and eliminated rework deliver 60%+ project cost savings.
Deterministic custom parsers deliver +95% accuracy out of the box. Optional AI augmentation pushes accuracy up to 99%.
Why SAS2PY
Generic ETL scanners approximate lineage. SAS2PY parses it exactly — every macro, every column, every dialect — then lands it natively on BigQuery.
| Capability | SAS2PY | Generic Tools |
|---|---|---|
| Custom parser per source (SAS, etc.) | ✓ | ✗ |
| 100% column-level lineage to Dataplex catalog | ✓ | ~ |
| Native BigQuery SQL with partitioning & clustering | ✓ | ~ |
| Dataform SQLX pipeline generation | ✓ | ✗ |
| Dataproc PySpark & Cloud Dataflow output | ✓ | ✗ |
| Cloud Composer DAG generation | ✓ | ✗ |
| BigQuery ML / Vertex AI analytical model modernization | ✓ | ✗ |
| SAS macro expansion & full dialect support | ✓ | ✗ |
| Dataplex data quality rules & policy tag generation | ✓ | ✗ |
| On-premise / air-gapped deployment | ✓ | ✗ |
| Row-level data validation & parity proof | ✓ | ✗ |
| Cloud Data Fusion pipeline generation (SQL + CDAP) | ✓ | ✗ |
| BigQuery slot reservation & partition key recommendations | ✓ | ✗ |
| Multi-target output (BigQuery + Snowflake + Databricks) | ✓ | ✗ |
| Deterministic AST-based parsing (not regex or AI-only) | ✓ | ✗ |
| Parser-driven risk analysis & BigQuery optimization | ✓ | ✗ |
| STTM export & Dataplex catalog registration | ✓ | ~ |
✓ Full support ~ Partial / approximate ✗ Not supported
Schedule a technical deep-dive on your SAS estate. We'll show you parsed lineage and BigQuery SQL output from code.