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Migrate SAS to Databricks, PySpark, Snowflake, BigQuery & More

Base SAS · Enterprise Guide (EGP) · DI Studio · SAS Viya10X SPEED

Databricks PySpark Snowflake BigQuery Polars Snowpark More
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SAS · Base SAS · EGP · DI Studio · SAS Viya · Parser Engine

Everything MigryX ingests from the SAS ecosystem, the engine that converts it, and every target it produces.

SAS Programs & Code
  • Base SAS programs (.sas)
  • DATA steps & SET / MERGE
  • PROC SQL & pass-through
  • PROC steps (SORT, MEANS, FREQ, TRANSPOSE)
  • SAS Macros & %INCLUDE
  • Formats, Informats & PROC FORMAT
  • ODS output & report procedures
  • SAS/IML & SAS/STAT
SAS Platform & Tools
  • SAS DI Studio (ETL jobs)
  • SAS Enterprise Guide (EGP)
  • SAS Viya & CAS engine
  • SAS Management Console
  • Stored Processes & web services
  • SAS Grid Manager & scheduling
SAS Data Sources
  • SAS datasets (.sas7bdat)
  • Oracle, SQL Server, Teradata
  • DB2, Netezza via SAS/ACCESS
  • Flat files, CSV, Excel & XML
  • ODBC & bulk-load connections
MigryX parser engine
MigryX Parser
Deployment
  • dbt
  • Airflow
  • Openflow
  • Git / CI
Python Ecosystem
  • Databricks
  • PySpark
  • Snowpark
  • Dataproc
  • Fabric
  • EMR
  • CLCloudera
Modern Warehouse
  • Databricks
  • Snowflake
  • BigQuery
  • Fabric
  • Redshift
  • TDTeradata
  • ICIceberg

Migration Process

Analyze and Insights
  • Automatic code assessment for rationalization and migration planning
  • Comprehensive dependency mapping with data and file lineage
  • Development of required frameworks and standards
  • Code complexity analysis, block labels, and LoC assessment
  • Rationalize and standardize current ETL
Convert and Migrate
  • Automated SQL and ETL code translation with modernization
  • Multi code conversion with enhanced optimization and unit testing
  • Metadata preservation and comprehensive documentation
  • Visual execution on Databricks, PySpark, Snowflake, BigQuery, and Polars
  • Native integration with DBT, Airflow & Git
Test and Validate
  • End to end automated testing of data pipelines
  • Comprehensive data validation and schema mapping
  • Side by side output comparison and metrics validation
  • Test data generation and cut over preparation
  • Partitioned validation with automated error detection
🚀 Go Live and Hyper Care Streamlined transition with dedicated support and monitoring to ensure optimal performance
🧭
Compass
Migration Intelligence Platform

Understand Your Estate Before You Migrate

Compass scans your entire legacy environment — every file, program, and dependency — then classifies each asset as MIGRATE, ARCHIVE, or DELETE. Convert only what matters. Archive the rest. Delete the noise.

The Migration Challenge

Organizations face massive legacy environments with no clear path forward.

📊 Massive Scale

Hundreds of thousands of files with no visibility into what's actively used.

💸 Mounting Costs

Legacy licenses and storage costs growing — much of it stale, unused data.

❓ Unknown Dependencies

Complex web of programs and datasets. What connects to what is a major challenge.

⚠️ Migration Risk

Can't migrate everything at once. Need data-driven decisions, not guesswork.

🕒 Time Pressure

Manual analysis takes months. Business needs a clear, prioritized plan — fast.

📉 No Visibility

Which assets drive value? Which are technical debt? Unknown without automation.

Compass Solves This

One system to scan, score, and classify your entire estate automatically.

🔍

Complete Inventory

Automated scanning of every file, every program, every execution log. Build a comprehensive catalog with metadata, dependencies, and usage patterns.

🎯

Smart Recommendations

Intelligent scoring engine evaluates each asset on multiple criteria. Get clear MIGRATE, ARCHIVE, or DELETE decisions with phased priorities.

📊

Dependency Mapping

Parse code to extract relationships and external references. Understand full impact before making any change.

💰

Cost Optimization

Identify archival and cleanup opportunities. Project savings across storage, licensing, and cloud migration costs before committing.

Intelligent Classification Logic

Multi-factor scoring balances cost reduction with migration speed

✅ Increases Migration Priority

  • Recent access — used in last 6 months
  • High frequency — accessed regularly in execution logs
  • Small size — easy to migrate quickly and cheaply
  • SQL-heavy — simpler, faster conversion path
  • Low complexity — fewer dependencies, lower risk
  • Error-free — clean execution history, no fixes needed

⚠️ Decreases Migration Priority

  • Stale data — no access in 2+ years → ARCHIVE candidate
  • Never used — zero execution in logs → DELETE candidate
  • Large files — higher migration cost and risk, later phases
  • High complexity — many dependencies, careful planning needed
  • Execution errors — must be fixed or reviewed before migration
  • Orphaned — no dependents found → safe to delete
Classification Output
5
MIGRATE — Critical
High value, low effort → Phase 1
Daily reports, frequently accessed data, SQL-heavy code, business-critical assets
3–4
MIGRATE — Standard
Active workloads → Phase 2–3
Regular usage, moderate complexity, important but not critical
A
ARCHIVE
Move to cold storage — reduce licensing costs
Not accessed recently, historical/compliance data, low business value
D
DELETE
Safe to remove — immediate savings
Duplicates, orphaned files, test data, never executed

Typical Customer Outcomes

What organizations discover when they truly understand their legacy environments

60–80%
Data Reduction
Archive or cleanup candidates
5–10×
Analysis Speed
vs. manual assessment
Hours
To Complete Scan
Not weeks or months
100%
Asset Coverage
Complete inventory visibility
Built for Enterprise Scale Production-ready. Handles real-world complexity.
01

File Scanner

High-performance parallel processing. Hashing detects duplicates automatically.

02

Code Parser

Extracts datasets, deps, libraries, SQL, and relationships with high accuracy.

03

Log Analyzer

Parses execution logs to track usage patterns, errors, and performance.

04

Usage Tracking

Evidence-based decisions: which assets are active vs. never accessed.

05

Migration Scoring

Multi-factor algorithm: usage, recency, size, complexity, execution quality.

06

Phased Planning

Priority-based phase assignment optimized for risk and business continuity.

07

Rich Reports

Executive dashboards, migration plans, cost projections, exportable data.

08

Queryable Database

All analysis stored structurally. Run custom queries for any ad-hoc need.

Simple Process. Powerful Results.

From deployment to actionable migration intelligence — fast.

1

Configure & Scan

Point Compass at your SAS directories. Automated scanning begins immediately.

2

Analyze & Score

Parse code, analyze logs, build dependency graph. Score every file for priority.

3

Review & Decide

Interactive reports show exactly what to migrate, archive, or delete.

4

Execute & Save

Follow the phased plan. Track progress and realize immediate cost savings.

Analyze. Inventory. Lineage.

Scan SAS, DataStage, Informatica, Teradata BTEQ, PL1, and JCL to auto build a complete inventory. Discover dependencies, macro chains, external calls, data sources, and fan in or fan out hot spots. Produce visual lineage and impact maps that guide the entire modernization.

  • Inventory all workflows, macros, and configurations
  • Dependency mapping with visual lineage (file + data)
  • Code complexity analysis, block labels, and LoC assessment
InventoryLineageComplexityValidationRisk
Visual lineage map
Visual lineage. Precise dependency graph.

Convert. Generate modern code.

Parser conversion into Databricks notebooks, PySpark, Snowflake, BigQuery, Polars, and SQL for Redshift and Fabric. All translations are explainable and auditable.

  • Interprets and converts legacy code structures to deliver the same output every time.
  • Translated workflows to Databricks notebooks and PySpark scripts
  • Auto documentation for each converted artifact
DatabricksPySparkSnowflakeBigQueryPolarsAuto docs
Targets we generate
Databricks, PySpark, Snowflake, BigQuery, and Polars.

Execute. Orchestrate pipelines.

Run converted workloads in the right order with a driver notebook or job runner. Standardize on Delta and cloud storage, schedule, monitor, and auto retry with centralized logs and metrics.

  • Visual execution on Databricks, PySpark, Snowflake, BigQuery
  • Native integration with DBT, Airflow, Git
  • Validate results and capture lineage
Visual orchestrationSchedulingRetriesLogsCI ready
Execution orchestration
Visual execution with centralized logs.

Validate. Prove parity.

Partitioned validation compares row level and aggregate outputs between legacy and modern systems. Automatic schema checks, data matching reports, and exception trails give confidence to go live.

  • Visual execute to Snowflake and Databricks. Shows Visual lineage along with the live code in a direct session. You see each step and the exact stop point.
  • Streamlines troubleshooting, cuts retesting, provides audit ready logs, lowers engineering and compute costs.
  • Lower risk. Visual Lineage shows upstream and downstream impact, so teams retest only what matters.
Row countsCommon columnsMismatched columnsEvidence
Data matching validation
Data matching. Evidence your stakeholders trust.

Merlin AI. Assist and accelerate.

Context aware assistance that knows your inventory, lineage, and conversion plans. Generate unit tests, explain diffs, suggest mappings, and draft notebooks with your rules applied.

  • Inline explanations for converted modules
  • Debug errors, and improve efficiency
  • Enterprise safe. Runs in your environment
Inline explainsMapping assistTest scaffoldSecure in your env
Merlin AI assistant
Developer assist powered by your context.
Execution

Visual Execution

Visual execution runs directly on Snowflake and Databricks, combining lineage and live code in one workspace with a direct warehouse session and step-by-step visibility to any failure point.

  • Visual execute to Snowflake and Databricks. One view shows visual lineage along with live code with a direct session. You see each step and the exact stop point.
  • Streamlines troubleshooting, cuts retesting, provides audit ready logs, lowers engineering and compute costs.
  • Lower risk. Visual Lineage shows upstream and downstream impact, so teams retest only what matters.
Visual Execution on Snowflake and Databricks
Modules

Modernize faster across the full migration lifecycle

SAS Code Analysis dashboard
Code Analysis

Quickly assess thousands of scripts, map complexity and dependencies, and flag readiness. Get clear scope, a prioritized plan, safer cutovers, and faster production.

SAS Lineage visualization
Visual Lineage

Visualize code across jobs, tables, and SQL to see sources, flows, and changes. Speeds impact checks, lowers migration risk, supports audits, and proves outputs match.

Automated SAS conversion to Python and Snowpark
Code Conversion

Convert legacy SAS, DataStage, BTEQ, and more into Python, PySpark, Snowpark, or SQL with matched outputs. Modernize faster, keep logic intact, and avoid risky rewrites.

Jupyter notebooks for validation and development
Data Mapper

Automatically map legacy schemas to Snowflake or Databricks with clear mappings. Cut migration risk, enforce naming and data types, and get audit-ready visibility.

Generated documentation example
Auto Docs

Automatic documentation captures your legacy code and the new target code, detailing working components, parameters, and dependencies for clear traceability.

Data Matching reports and reconciliation
Data Matching

Compares source and target outputs at scale using configurable keys and rules. Flags mismatches, duplicates, and gaps with actionable reports for fast fixes.

Sources we modernize

SAS (Base, DI Studio, EG/EM, Viya), IBM DataStage, Oracle ODI, Teradata BTEQ, Informatica, and Alteryx. These are fully supported inputs for automated conversion.

SAS
Data steps. Procs. Macros. Formats.
IBM DataStage
Jobs. Stages. Parameters. Sequences.
Oracle ODI
Mappings and procedures.
Teradata BTEQ
Batch scripts and controls.
Informatica
Workflows and mappings.
Alteryx
Workflows and packaged exports.
Qlik or Talend
ETL or ELT pipelines and orchestrations.
VBA
Excel or Access automations and macros.
SAS DataFlux
Data quality rules and jobs.
Mainframe JCL
Job control scripts and utilities.
PL1
Procedural programs and batch utilities.

Targets we generate

Databricks, PySpark, Snowflake, BigQuery, Polars, and cloud platforms. (617) 512-9530

Databricks
Delta Lake pipelines and notebooks
PySpark
Distributed DataFrame and SQL workloads
Snowflake
Snowpark and Snowflake SQL
BigQuery
Google Cloud analytics and SQL
Polars
High-performance DataFrame library
Fabric
Microsoft Fabric Lakehouse and pipelines
EMR
AWS EMR Spark and Hive workloads
Cloudera
On‑prem or hybrid Hadoop distributions
Dataproc
Managed Spark on Google Cloud
Deployment

Simple, secure, on premise deployment

Everything runs inside your network. No external connections. No data leaves your environment in any scenario.

Security posture

  • Fully air gapped operation supported.
  • Outbound connections none. External API calls none.
  • All processing occurs inside the container and host network.
  • SSL for VS Code, Jupyter, nginx proxy, and backend API.
  • Local PostgreSQL only. Logs stored on local disk.
DIY & Self-Service

No consultants. No waiting.

Deploy MigryX yourself, run the pilot on your own schedule, and keep every byte of source code inside your network.

One-command install
Docker or Windows VM — running in under an hour. No vendor hand-holding required.
Air-gapped & private
No outbound connections. No external API calls. Source code and data stay entirely on your infrastructure.
Run on your own timeline
Start, pause, and iterate the conversion yourself. No engagement contracts needed to see real results.
Engagement Options

Discovery & Conversion — At Any Scale

MigryX handles SAS estates from 100K to 100 million lines of code. Start with a comprehensive Discovery in 1 to 8 weeks, then prove conversion with a targeted POC — all running inside your environment.

100K to 100M lines of SASDiscovery scales from small estates to enterprise portfolios
10 structured deliverablesInventory, risk, wave plan, RACI, SOW and more
Conversion POC in 2 weeks10K lines converted, validated, and running on Databricks

Discovery

1 – 8 weeks

100K to 100M lines of SAS code

  • SAS Estate Inventory & Classification Report — Catalogue every SAS program, macro, data step, PROC, and configuration across the estate. Classify by type, function, and business domain.
  • Data Landscape & Dependency Mapping Pack — Map all data sources, sinks, and inter-program dependencies. Produce visual lineage diagrams showing upstream/downstream data flows.
  • Complexity & Risk Assessment Register — Score every program by lines of code, cyclomatic complexity, macro nesting, and external dependencies. Flag high-risk conversion candidates.
  • Workload Segmentation & Migration Strategy Matrix — Group workloads into migration waves by complexity, business criticality, and target platform affinity (Databricks, Snowflake, BigQuery).
  • Wave-Based Migration Plan (High-Level) — Define sequenced migration waves with dependencies, milestones, and resource estimates. Ready for executive sign-off.
  • Target Databricks Readiness Assessment — Evaluate your Databricks environment for compute, storage, Unity Catalog, and workspace configuration against the migration requirements.
  • Validation & Reconciliation Framework — Define row-count, aggregate, and sample-based reconciliation rules to prove data equivalence between SAS and target output.
  • Ownership & Accountability (RACI) — Assign Responsible, Accountable, Consulted, and Informed roles across MigryX, your IT team, data owners, and business stakeholders.
  • Estimate & SOW — Deliver a fixed-scope Statement of Work with effort estimates, pricing, timeline, and acceptance criteria for the full conversion engagement.
  • Business Change & Implementation Plan — Outline organizational change management activities, training needs, comms plan, and go-live cutover steps.

Conversion POC

2 weeks

10K lines — Proof of Conversion

  • Scope: 10,000 lines of SAS code selected from the Discovery inventory — representative of real production workloads.
  • Automated Conversion: MigryX converts selected programs to Databricks notebooks, PySpark scripts, Snowflake SQL, or BigQuery — with full lineage preserved.
  • Data Validation: Row-count and aggregate reconciliation between SAS and target output. Data equivalence proven before sign-off.
  • Execution on Target: Converted code deployed and executed in your Databricks, Snowflake, or BigQuery environment — not a simulation.
  • Conversion Report: Detailed report covering conversion accuracy, exceptions, manual adjustments, and confidence score per program.
  • Go / No-Go Recommendation: Clear recommendation with evidence to proceed to full-scale migration based on POC results.
Dimension Discovery Conversion POC
Scope 100K – 100M lines of SAS 10K lines of SAS
Duration 1 – 8 weeks 2 weeks
Deliverables 10 structured deliverables
Inventory, lineage, risk
Wave plan & SOW
RACI & change plan
Converted code
Data reconciliation
Execution on target
Go / No-Go report
Target Platform Platform-agnostic assessment Databricks, Snowflake, or BigQuery
Execution In your environment In your environment

Discovery and Conversion POC run entirely in your environment — air-gapped capable, zero data exfiltration. Scope and timeline scale with estate size and complexity. Contact us for enterprise pricing and custom engagement structures.

Reports

Project Reports and JCL Reports

Project Reports

A compact view of what exists, how it connects, and where risk lives.

Inventory Lineage Complexity Validation Risk
  • Inventory summary. Files and jobs counted. Macros and includes detected. Datasets referenced.
  • Dependency map. Fan in and fan out. Critical hubs identified. External calls flagged.
  • Complexity and risk. Pattern difficulty score. Unsupported items. Remediation priority.
  • Validation status. Errors and warnings. Coverage progress. Open issues.

JCL Reports

StepsPROCsDD statementsSchedulesDatasetsReadiness

End to end view of JCL structure, datasets, and run control with conversion readiness.

  • Job flow. Step order. PROC usage. Condition codes.
  • Datasets and lineage. Reads and writes. Temporary and persisted. Upstream and downstream.
  • Control and schedule. Triggers and dependencies. Calendars if present. Restart points.
  • Conversion readiness. Unsupported patterns. Parameterization needs. Proposed target control.

Datasheets

Architecture

How MigryX fits in your environment

Deployment

Install on your servers or VMs. Optionally deploy inside Kubernetes or OpenShift. Use private cloud networks only.

Connectors

Secure connectors to Databricks, Snowflake, BigQuery, Redshift, and Polars. Keys managed by you.

Storage

Project data stored inside your boundary. Logs and evidence live in your storage accounts.

Security and compliance

Private by design. You hold the keys.

Data residency

Run on premise or inside your private cloud. No data leaves your boundary.

Access control

Role based access. SSO and MFA integration. Fine grained permissions.

Auditability

Every action is logged. Evidence packs for internal and external reviews.

Governance

Templates, naming, and coding standards enforced at generate time.

Backups

Project backup and restore under your policies.

Isolation

No shared services. Your environment only.

FAQ

Answers to common questions

Where does MigryX run

Inside your environment. On your hardware or private cloud. You hold the keys.

What code is produced

Databricks notebooks, PySpark, Snowflake SQL, Snowpark, BigQuery, Polars, DBT models, and Python with comments and mapping sheets.

How do we prove results

Validation reports and Data Matching show parity. Approval records provide evidence for audits.

Can I see a demo?

Absolutely. Book a live walkthrough where we parse your own SAS programs in real time and show you converted output, lineage, and validation results.

What about orchestration

Integrate with Airflow, ADF, Composer, or Control M. Keep existing schedules or modernize them.

How do we start

Begin with the pilot. Load a sample of code. Review lineage, conversion, runs, and validation. Scale with confidence.

Get Licensed

Start Your SAS Migration Today

Ready to migrate SAS to Databricks, PySpark, Snowflake, BigQuery, or Polars? Talk to us about licensing and get started.

(617) 512-9530
Call now for licensing, pricing, and pilot options

Schedule a Demo

See MigryX parse your own SAS code live. Pick your target: Databricks, PySpark, Snowflake, BigQuery, or Polars.

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Buy a License

Get a license for your team. Self-service deployment in your environment. On-premise or private cloud.

Call Sales →

Start a Pilot

Run a self-service pilot. Install, convert real code, validate results — all inside your network.

See Pilot Options →
hello@migryx.com (617) 512-9530 Indianapolis • Boston • Hyderabad
MigryX
Get Licensed. Start Migrating.
Deploy MigryX in your environment and convert SAS to modern platforms today.
Databricks PySpark Snowflake BigQuery Polars On-premise
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Migration guides: Databricks PySpark Snowflake BigQuery Polars pandas