Lane 1 — SAS → Databricks
4–6 weeks for a first slice of about 10K lines. Built for regulated teams facing a SAS renewal or a Databricks platform decision.
AI
Proof
Pricing
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AI
Proof
Pricing
Book a demo
Scan your code free
Hard sources — not covered by free tools
Also parsed — certify what free tools miss
Runtimes
After migration
Lakebridge does not read SAS. MigryX does — Base, macros, IML — and ships row-level proof. Mainframe batch (JCL, COBOL + copybooks, DB2 SQL) is Lane 2 inside the same accounts. Not CICS or online.
Two lanes
4–6 weeks for a first slice of about 10K lines. Built for regulated teams facing a SAS renewal or a Databricks platform decision.
8–10 weeks for 2–3 job streams. In scope: JCL, COBOL with copybooks, and DB2 SQL. Online CICS and VSAM are out of scope.
Against free platform tooling
Lakebridge, Cortex conversion, and BigQuery Migration Service win on dialects they already read. We lead on the sources they do not fund, the evidence auditors accept, and lineage that outlives the vendor tool.
| Capability | Lakebridge (Databricks) | Cortex conversion (Snowflake) | BigQuery Migration Service | MigryX |
|---|---|---|---|---|
| SQL dialects → target | Yes, free | Yes, free | Yes, free | Yes — not the lead |
| Informatica, Teradata, DataStage | Yes (config-driven) | Informatica | Teradata, partial | Yes, deterministic parser |
| SAS Base / macros / IML | No | No | No | Yes |
| COBOL / copybooks / JCL | No | No | No | Yes — batch data only |
| Alteryx, Qlik, ODI, DataFlux | No | No | No | Yes |
| Row-level parity + exception report | Reconcile module | Limited | Limited | Audit-grade — the deliverable |
| Signed evidence pack | No | No | No | Yes (Atlas) |
| Cross-platform, as-of lineage | Unity Catalog only | Snowflake only | Dataplex only | Yes (Atlas) |
| Air-gapped / on-prem | No | No | No | Yes |
| Target-neutral output | Databricks only | Snowflake only | BigQuery only | Any |
Already mid-migration on a free tool? Certify the output and keep Atlas after the vendor tool is gone.
The first engagement
Fixed scope, fixed exit., so the only thing you are really spending is calendar time.
Mainframe batch is a separate lane: 8–10 weeks, 2–3 job streams, . JCL, COBOL and copybooks, DB2 SQL. Not CICS or online.
Already part-way through with Lakebridge? The certify pilot proves that output instead of redoing it, and leaves you with lineage that survives the vendor tool.
Proof, not claims
The pilot you are being offered is the first four weeks of the method behind our seven SAS-to-Databricks engagements.
See the SAS → Databricks engagements → · See the emitted PySpark, Delta, DLT, and Workflows output →
Atlas holds the lineage, the parity evidence, and the as-of history after the conversion is done and the tooling is gone. It attaches to a conversion license or stands alone. It is the only thing on this page you still need in year three.
Databricks Targets
Every modernization generates production-ready Databricks Lakehouse artifacts — following Medallion Architecture (Bronze → Silver → Gold), optimized for Photon engine, and governed by Unity Catalog.
Production-grade PySpark code with Auto Loader ingestion, Change Data Feed (CDF) CDC patterns, and Medallion Architecture layering — Bronze raw, Silver cleansed, Gold aggregated.
ACID-compliant Delta tables with MERGE INTO upserts, OPTIMIZE & Z-ORDER compaction, Liquid Clustering, schema evolution, time travel, and Change Data Feed enabled.
Column-level lineage, STTM mappings, attribute-based tags, and fine-grained access controls registered in Unity Catalog — full data contract governance across the Lakehouse.
ETL pipelines converted to Databricks Workflows via Asset Bundles (DABs) — dependency-aware multi-task DAGs, serverless compute, job clusters, and triggered/scheduled orchestration.
Streaming and batch ETL converted to DLT pipelines — declarative @dlt.table definitions, Auto Loader CDC ingestion, data quality expectations, and enhanced autoscaling.
Converted code delivered as annotated Databricks Notebooks — with %sql / %python cells, Databricks Connect compatibility, lineage comments, and inline validation cells.
SAS analytical models and scoring logic converted to Python — MLflow experiment tracking, model registry, Feature Store integration, AutoML baselines, and Model Serving endpoints.
Legacy SQL dialects transpiled to Databricks SQL — Photon-optimized queries, serverless SQL Warehouses, ANSI SQL compliance, and 500+ dialect function mappings.
Modernization Sources
Purpose-built parsers for each source platform. Not generic scanners. Every conversion produces explainable, auditable, Databricks-native code.
Automate SAS Base, Macro, PROC SQL, and IML conversion to PySpark and Databricks SQL. Full macro expansion, DATA step logic, FORMAT/INFORMAT handling, and PROC SORT/MEANS/FREQ translation.
Parse Talend project exports (ZIP/Git), .item artifacts, tMap joins, metadata, contexts, and connections — converted to PySpark jobs and Databricks Workflows with full component-level lineage.
Parse Qlik Sense .qvf apps, QlikView .qvw documents, and .qvs includes — load scripts, ApplyMap, JOIN/RESIDENT, QVD stores, and Section Access — converted to PySpark jobs and Databricks Workflows with full script-level lineage.
Convert Alteryx Designer workflows (.yxmd/.yxwz), macros, and apps to PySpark and Databricks SQL — tool-by-tool translation with full lineage preservation and Databricks notebook output.
Modernize IBM DataStage parallel and server jobs, sequences, shared containers, and XML definitions to PySpark, Delta Live Tables, and Databricks Workflows — transformer logic fully preserved.
Modernize Informatica PowerCenter (.xml exports) and IDMC/IICS mappings — sources, targets, transformations, and workflows — to PySpark jobs with Unity Catalog lineage registration.
Parse Oracle ODI repository exports — mappings, interfaces, knowledge modules, packages, and load plans — converted to PySpark and Delta Lake with full column-level lineage.
Parse SQL Server Integration Services .dtsx packages and .ispac archives — data flow, control flow, SSIS expressions, C#/VB.NET script tasks — to PySpark and Databricks Workflows.
Modernize Teradata BTEQ, FastLoad, MultiLoad, and Teradata SQL — QUALIFY → window function rewriting, BTEQ command translation, and PRIMARY INDEX advisory — to Databricks SQL and PySpark.
Modernize Oracle PL/SQL stored procedures, packages, and triggers with 2000+ function mappings, CONNECT BY → recursive CTE rewriting, BULK COLLECT/FORALL — targeting Databricks SQL.
Parse COBOL programs with their copybooks — packed decimal, implied decimals, REDEFINES overlays, and OCCURS DEPENDING ON — to PySpark and Delta Lake. DB2 embedded SQL becomes Databricks SQL, and JCL job steps become orchestrated Workflows tasks with condition codes preserved.
Transpile SQL from Oracle, T-SQL, Teradata, DB2, Netezza, Greenplum, Hive HQL, and Vertica directly to Databricks SQL — with 500+ function mappings and dialect-aware query rewriting.
Modernize SAS DataFlux dfPower Studio jobs, DMS Data Jobs, and Real-time Services — standardize/parse/match/validate schemes — to Python on Databricks with Great Expectations integration.
How It Works
The same proven methodology applies to every source — SAS, Talend, Qlik, Alteryx, DataStage, Informatica, or ODI — all landing on Databricks.
Upload source artifacts — SAS scripts, Talend exports, Qlik .qvs/.qvf, DataStage XML, .dtsx packages — into MigryX.
Custom parsers build complete ASTs, expand macros, resolve dependencies, and produce column-level lineage maps.
Parser-driven conversion to PySpark, Delta Lake, Databricks SQL, Workflows, or DLT — with full documentation.
Row-level and aggregate data matching between legacy and Databricks outputs — audit-ready evidence for sign-off.
Publish lineage, STTM, and data contracts to Unity Catalog. MigryX AI surfaces risk and recommends optimization paths.
Platform Capabilities
Every MigryX modernization is engineered for the full Databricks Lakehouse — Medallion Architecture, Photon-optimized SQL, Unity Catalog governance, Delta Lake storage, and Asset Bundle deployment.
Purpose-built for each source language. SAS macro expansion, DataStage XML, Talend .item files, Qlik .qvs/.qvf, SSIS .dtsx, COBOL copybooks — full fidelity, deterministic output, no approximation.
Legacy pipelines restructured into Bronze (raw ingestion via Auto Loader), Silver (cleansed, deduplicated), and Gold (aggregated, business-ready Delta tables) layers automatically.
Tables generated with MERGE INTO upserts, OPTIMIZE & Z-ORDER compaction, Liquid Clustering, Change Data Feed, schema enforcement, and time travel — production-ready from day one.
Source-to-target column mappings, STTM tables, and data contracts published to Unity Catalog — fine-grained access, attribute tags, and Databricks Lineage API integration.
AI analyzes parsed metadata to recommend Photon optimization, Z-ORDER keys, and partition strategies. SAS models land in MLflow Feature Store with AutoML baseline generation.
Full deployment behind your firewall with Asset Bundle (DAB) packaging for CI/CD. Source code and lineage never leave your network. SOX, GDPR, BCBS 239 ready.
Deep Platform Integration
MigryX isn't a generic modernization tool retrofitted for Databricks. Every output is built for Databricks-native execution — Photon-optimized, Unity-governed, serverless-ready, and deployed via Asset Bundles.
Generated SQL and PySpark leverage Photon-compatible patterns — vectorized column operations, predicate pushdown hints, and join strategies optimized for Photon's C++ execution engine.
Photon RuntimeModernized workloads target Serverless SQL Warehouses and Serverless Jobs compute — auto-provisioned, zero-management clusters with instant startup and cost-efficient scaling.
ServerlessSource system connections mapped to Databricks LakeFlow Connect ingestion pipelines — replacing legacy source connectors with managed, incremental CDC ingestion into Delta Lake.
LakeFlowSAS analytical models (PROC LOGISTIC, PROC GLM, PROC MIXED) converted to Python and registered in Mosaic AI — with Model Serving endpoints, A/B testing, and Feature Engineering tables.
Mosaic AIAll modernized artifacts packaged as Databricks Asset Bundles — version-controlled YAML definitions, CI/CD-ready deployment, environment promotion (dev → staging → prod), and git integration.
DABs / CI/CDColumn-level lineage, STTM mappings, data classification tags, row-level security policies, and attribute-based access controls published directly to Unity Catalog — not sidecar metadata.
Unity CatalogLegacy reports (SAS PROC REPORT, Crystal Reports, SSRS) converted to Databricks SQL queries with AI/BI Dashboard definitions — parameterized queries, scheduled refreshes, and alert triggers.
AI/BI DashboardsCross-organization data sharing patterns preserved during modernization — legacy file-based data exchange converted to Delta Sharing recipients, providers, and shares with fine-grained access control.
Delta SharingModernization status dashboards, lineage explorers, and validation reports deployed as Databricks Apps — custom Streamlit/Gradio applications running natively inside the Databricks workspace.
Databricks AppsModernization Architecture
Every MigryX modernization follows a deterministic pipeline that lands production-ready artifacts directly on the Databricks Lakehouse — governed, validated, and deployment-ready.
Measurable Results
Organizations using MigryX to land on Databricks accelerate delivery, reduce risk, and eliminate manual rewrite costs across every modernization program.
Automated lineage extraction and parser-driven analysis eliminate months of manual discovery and rewrite work.
Complete visibility into dependencies prevents production incidents and modernization-related data defects.
Generated code your team can read, with validation before go-live.
Deterministic parsers handle 95%+ of typical code out of the box.
Frequently Asked Questions
Common questions from teams evaluating MigryX for Databricks modernization programs.
A SAS to Databricks pilot runs 4–6 weeks on about 10K lines of production SAS, validated row by row. Mainframe batch is a separate lane, 8–10 weeks. A one-week readiness scan is the smaller first step.
Lakebridge converts SQL dialects and it is free, so keep using it for that. It does not read SAS Base, macros, or IML, and it does not read COBOL or JCL. It also does not leave you with an evidence pack an auditor will accept. MigryX covers the sources Lakebridge does not fund and produces the parity proof. If Lakebridge has already converted a workload, the certify pilot validates that output rather than repeating the work.
A representative SAS workload, agreed inputs with the original outputs (or a run in your environment so we can compare), and one named owner for weekly checkpoints. No code upload is needed to start the conversation — the intake form only asks what you are modernizing and when your platform renews.
Databricks-native. MigryX generates PySpark that leverages Databricks-specific APIs — Delta Lake MERGE INTO with CDC patterns, Auto Loader for ingestion, Unity Catalog references for table governance, DLT @dlt.table decorators, and Databricks Workflows YAML definitions. It is not generic Spark code adapted for Databricks.
MigryX automatically restructures legacy pipelines into Bronze (raw ingestion via Auto Loader or COPY INTO), Silver (cleansed, deduplicated, schema-enforced Delta tables), and Gold (aggregated, business-ready views and tables) layers. The layering is deterministic based on parsed source logic — not manual mapping.
Yes. MigryX produces column-level STTM (Source-to-Target Mapping) tables and publishes them to Unity Catalog via the Lineage API. This includes data classification tags, attribute-based access policies, and data contract definitions — providing full governance from day one of the modernization.
Yes. SAS PROC LOGISTIC, PROC GLM, PROC MIXED, and PROC MODEL are converted to equivalent Python (scikit-learn / statsmodels) with MLflow experiment tracking, model registry, and Feature Store integration. Model serving endpoints and AutoML baselines are generated automatically.
Legacy job schedulers (Control-M, Autosys, SAS batch flows, Talend triggers, DataStage sequences) are converted to Databricks Workflows with multi-task DAG dependencies, cluster policies, retry logic, and cron-based scheduling. Orchestration logic is preserved, not approximated.
Yes. Streaming and batch ETL patterns are converted to DLT pipelines with declarative @dlt.table and @dlt.view definitions, APPLY CHANGES for CDC, data quality EXPECT constraints, and enhanced autoscaling. DLT is the recommended target for continuous ingestion workloads.
Yes. MigryX supports full on-premise and air-gapped deployment. Source code, lineage data, and metadata never leave your network. Output artifacts are packaged as Databricks Asset Bundles (DABs) for secure CI/CD deployment into your Databricks workspace.
MigryX generates row-level and aggregate-level data comparison reports between legacy system output and Databricks-produced output. Validation includes row counts, column checksums, business rule assertions, and statistical parity proofs — producing audit-ready evidence for sign-off.
Three ways to start
Every route below ends with a person who has done this before, not a sales sequence.
Not ready to name a workload? The free sample assessment converts a representative piece of SAS at no cost — request one here.