PowerCenter · IDMC · mappings & taskflows

From Informatica to modern pipelines

PowerCenter repository & IDMC CDI — lineage and automated conversion.10X SPEED

Python PySpark Snowpark Snowflake Databricks More
Short demo video

See It In Action

From IDMC Mapping to MigryX Analysis — Instantly

Upload your Informatica IDMC mapping and MigryX parses every source, expression, joiner, filter, and target — producing a clean, interactive dependency graph with full column-level lineage.

🔴 Informatica IDMC Mapping
Informatica IDMC mapping with sources, expressions, joiners, filters, and targets

Sources, expressions, joiners, filters, and targets — complex visual mapping logic.

MigryX
Analyzes
🔍 MigryX Analyzer — Dependency Graph
MigryX analyzer showing parsed IDMC mapping as interactive dependency graph with color-coded nodes

Clean dependency graph — every source, expression, filter, joiner, and target parsed and color-coded.

Every transformation, every join condition, every expression — parsed with +95% accuracy and mapped to production-ready code.

Informatica PowerCenter · IDMC · Parser Engine

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

PowerCenter & Repository
  • Mappings & mapplets (XML)
  • Workflows & worklets
  • Sessions, batches & tasks
  • Transformations & expressions
  • Source Qualifier & lookups
  • Connections & relational objects
  • Parameter files (.prm)
  • Folders, shortcuts & lineage
IDMC & Cloud Integration
  • Cloud Data Integration mappings
  • Taskflows & schedules
  • Connection assignments
  • Secure Agent & runtime scope
  • INFACMD / REST exports
  • Org & project metadata
Data Sources & Platforms
  • Oracle, SQL Server, Snowflake
  • Teradata, Db2, Netezza
  • Files, JDBC, ODBC & bulk load
  • Redshift, BigQuery, ADLS/S3
  • Lineage across joins & lookups
MigryX parser engine
MigryX Parser
Deployment
  • dbt
  • Airflow
  • Openflow
  • Git / CI
Python Ecosystem
  • PySpark
  • Snowpark
  • Databricks
  • Dataproc
  • Fabric
  • EMR
  • Cloudera
Modern Warehouse
  • Snowflake
  • BigQuery
  • Fabric
  • Databricks
  • Redshift
  • Teradata
  • Iceberg

Migration Process

Analyze and Insights
  • Automatic assessment of Informatica PowerCenter mappings and IDMC CDI assets for rationalization and migration planning
  • Dependency mapping across mappings, workflows, sessions, and taskflows with data and file lineage
  • Development of required frameworks and standards for PowerCenter and IDMC cutovers
  • Mapping complexity analysis, SQL overrides, and transformation-path assessment
  • Rationalize and standardize PowerCenter and IDMC ETL before cutover
Convert and Migrate
  • Automated translation of Informatica mappings (PowerCenter & IDMC) to Python, SQL, and Spark with modernization
  • Multi code conversion with enhanced optimization and unit testing
  • Metadata preservation and comprehensive documentation
  • Visual execution on Databricks, Snowflake, and cloud platforms
  • 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

Analyze. Inventory. Lineage.

Scan Informatica PowerCenter repository exports and IDMC Cloud Data Integration assets to build a complete inventory. Discover mapping dependencies, workflow chains, session tasks, connection usage, and fan-in or fan-out hot spots. Produce visual lineage and impact maps that guide the entire migration.

  • Inventory mappings, mapplets, workflows, sessions, and IDMC taskflows
  • Dependency mapping with visual lineage (mapping + data)
  • Complexity signals from transformations, SQL overrides, and tasks
InventoryLineageComplexityValidationRisk
MigryX IDMC mapping analysis — parsed dependency graph with color-coded sources, expressions, joiners, filters, and targets
IDMC mapping parsed into interactive dependency graph with full lineage.

Convert. Generate modern code.

Parser conversion turns Informatica mapping logic — PowerCenter XML and IDMC CDI definitions — into Python, PySpark, Snowpark, and SQL for Snowflake, Databricks, BigQuery, Redshift, and Fabric. All translations are explainable and auditable.

  • Interprets transformations, expressions, and pushdown SQL for matched outputs
  • Translated workflows to notebooks
  • Auto documentation for each converted artifact
PythonPySparkSnowparkSQLTemplatesAuto docs
Targets we generate
Python and PySpark. Snowpark and SQL.

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, Snowflake
  • 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

Informatica migration across the full lifecycle

Informatica mapping analysis dashboard
Code Analysis

Assess thousands of PowerCenter mappings and IDMC CDI tasks, map complexity across transformations and workflows, and flag readiness. Get clear scope, a prioritized plan, safer cutovers, and faster production.

Informatica lineage visualization
Visual Lineage

Visualize mappings, sessions, and pipelines across PowerCenter and IDMC to see sources, joins, and SQL pushdown. Speeds impact checks, lowers migration risk, supports audits, and proves outputs match.

Automated Informatica conversion to Python and Snowpark
Code Conversion

Convert Informatica mappings and taskflows into Python, PySpark, Snowpark, or SQL with matched outputs — including expression logic and SQL overrides where appropriate. 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 Informatica mappings and the new target code, detailing transformations, mapplets, 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.

Source: Informatica

This page is dedicated to migrating Informatica PowerCenter and Informatica Intelligent Data Management Cloud (IDMC) — mappings, mapplets, workflows, sessions, and Cloud Data Integration (CDI) taskflows — into modern Python and cloud targets. Need other legacy engines? See the full platform.

PowerCenter
Mappings, workflows, sessions, mapplets, repository XML
IDMC / CDI
Cloud mappings, taskflows, connection assignments
Runtime & ops
Schedules, pmcmd, session logs, statistics

Targets we generate

Python (Pandas), PySpark, Snowflake/Snowpark, Databricks, and cloud platforms.

PySpark
Distributed DataFrame and SQL workloads
Snowpark
Python APIs for Snowflake compute
Databricks
Delta Lake pipelines and notebooks
Dataproc
Managed Spark on Google Cloud
Fabric
Microsoft Fabric Lakehouse and pipelines
EMR
AWS EMR Spark and Hive workloads
Cloudera
On‑prem or hybrid Hadoop distributions
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.
Pilot options

Start your Journey Today

Assess, convert, and validate PowerCenter and IDMC migrations safely inside your environment.

Runs in your environmentData never leaves
Convert. Generate modern code.Document & Understand
Execute. Orchestrate pipelines.Visual execution on Databricks, Snowflake

Migration Readiness

1 week

Discovery & Insights

  • Scope: 100K LoC - Unlimited
  • Deliverables: Inventory workflows, macros, and configs. Map dependencies with visual data and file lineage. Analyze complexity with block labels and LoC.
  • Reports: Inventory, visual lineage, and risk assessment. share via HTML reports
  • Access: Enterprise safe. Runs in your environment

Full Pilot

4 to 6 weeks

End-to-end

  • Scope: Discovery, plus 10K LoC across legacy programs or workflows.
  • Deliverables: Discovery, plus pilot code conversion and data matching to the target system.
  • Reports: Discovery, plus data matching, validation and enterprise data workflows.
  • Access: Enterprise safe. Runs in your environment

Large Scale Pilot

2 to 4 months

Enterprise

  • Scope: Same as end-to-end, but with larger sets of legacy data and programs for discovery, convertion, validation and execution to modern workloads.
  • Deliverables: Same as end-to-end
  • Reports: Same as end-to-end
  • Access: Enterprise safe. Runs in your environment
Type Migration Readiness Full Pilot Large Scale Pilot
Discovery 100,000 LoC 100,000 LoC 1 Million LoC
Conversion N/A 10,000 LoC 100,000 LoC
Duration 1 week 4 to 6 weeks 2 to 4 months
Deliverables Project reports
Risk analysis
Full reports
Executed code
Full reports
Executed code
Reports Inventory,lineage,risk Full project Full project and JCL
Execution In your environment In your environment In your environment

These pilots run securely within your environment. Pricing and scope can be adjusted to match complexity and urgency.

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 for Informatica migration

Deployment

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

Connectors

Secure connectors to Snowflake, Databricks, BigQuery, and Redshift. 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

Python, PySpark, Snowpark, SQL, DBT models, and Databricks notebooks 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 Informatica mappings 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 PowerCenter mappings or IDMC CDI assets. Review lineage, conversion, runs, and validation. Scale with confidence.

Contact

Talk to our team

Questions about PowerCenter repository exports, IDMC CDI mappings, or cutover planning? Tell us about your estate and target platform.

Schedule a Demo

See MigryX parse your own code in a live walkthrough.

Book Time →

Request a POC

Submit your migration details and get a free proof of concept.

Start POC →
hello@migryx.com (617) 512-9530 Indianapolis • Boston • Hyderabad
MigryX
Modernize faster
  • Start a pilot
  • Runs in your environment
  • End-to-end pilot