InfaBridge converts Informatica XML mappings and any XML-based ETL/ELT into production-ready cloud pipelines automatically. 80% faster, ~50% lower cost, zero manual rewriting.
The market moment
Informatica isn't going away overnight, but the ground underneath it is shifting fast. Every quarter you wait, the migration gets more expensive and the AI opportunity cost compounds.
Informatica licence and maintenance spend climbs every renewal cycle, with no new capability to show for it.
Enterprises are consolidating data platforms onto the modern Lakehouse stack Databricks, Snowflake, Fabric and legacy ETL is the last thing left behind.
Boards are asking for AI outcomes. Data still locked in proprietary Informatica pipelines can't feed modern ML and GenAI workloads.
Migration itself has become the blocker every AI and analytics initiative gets queued behind a multi-year ETL re-platforming programme.
The challenge
Enterprise Informatica environments contain hundreds sometimes thousands of mappings encoding years of mission-critical business logic. Every one requires manual analysis, SQL rewriting, and validation. The cost and timeline spiral quickly, and most programmes stall. The result: data modernisation programmes that should deliver competitive advantage become multi-year drains on engineering capacity and budget.
Manual re-engineering stretches to 6–18 months. Business units wait. AI initiatives stall. Competitor advantage accumulates.
Hand-rewritten SQL introduces subtle errors that pass unit tests but break in production often surfacing only after go-live in critical financial or regulatory pipelines.
Large teams of senior engineers at $35–45/hr offshore (or $90–150/hr onshore) make manual migration one of the most expensive infrastructure programmes in the data roadmap.
Engineers who know both Informatica internals and Databricks-native PySpark are extremely rare. Most teams must choose one or train from scratch.
The cost of doing nothing
Delaying migration isn't a neutral choice every quarter of inaction has a measurable cost, even before the migration itself begins.
Continues to accrue for as long as the estate stays on Informatica
Cost of keeping a manual migration team staffed and stalled
Value realised from AI/ML initiatives while data stays locked in legacy ETL
Typical extra time added once a manual migration programme stalls
How InfaBridge works
InfaBridge chains a specialised AI pipeline from raw XML parsing to validated, production-ready cloud code without any manual rewriting.
Full XML parsing of Informatica mappings, sessions, workflows, and dependency graphs
Rule engine decodes joins, filters, aggregators, routers, and complex branching logic
Structured LLM prompts produce optimised, platform-native code PySpark, SQL, DBT, and more
End-to-end source-to-target lineage for governance, audit, and impact analysis
Automated fidelity checks confirm output parity between legacy ETL and generated cloud code
Raw Informatica XML export files mappings, workflows, sessions, parameter files. No manual preparation required. InfaBridge reads the repository export directly.
Production-ready, platform-native code: PySpark notebooks for Databricks, SQL for Snowflake/BigQuery/Redshift, DAG files for workflow orchestration, lineage documentation, and a reconciliation report.
Reviews the output, handles edge cases, and signs off on UAT. InfaBridge handles 80% of the effort; your team finishes the last mile with deep contextual knowledge where human judgment adds real value.
Every pain, answered
Here's exactly how each pain point maps to an InfaBridge capability and the outcome your team actually feels.
| The pain | The InfaBridge answer | The client outcome |
|---|---|---|
| 6–18 months for a single migration programme | AI-automated conversion, validated in weeks | Faster time-to-value, earlier ROI on the platform move |
| $1.5M–2.5M in engineering spend at enterprise scale | ~50% lower total cost, working from your existing Informatica spend | Budget freed for AI and analytics initiatives |
| Manual SQL rewrites introduce silent logic errors | Automated output-parity validation on every mapping | Confidence in financial, regulatory, and audit-critical pipelines |
| 8–10 senior engineers tied up for 6+ months | 2–3 engineers reviewing AI-generated output | Engineering capacity freed for higher-value work |
| Separate migration effort required per target platform | One XML input, multiple native cloud outputs | Platform flexibility without re-work if strategy changes |
| Weeks of manual lineage documentation | Auto-generated source-to-target lineage on completion | Audit-ready governance from day one, not months later |
Capabilities
InfaBridge covers the complete migration surface from simple column maps to deeply nested multi-source pipelines with custom SQL overrides.
Handles all Informatica transformation types: Source Qualifier, Filter, Expression, Aggregator, Joiner, Lookup, Router, Sorter, Normalizer, Sequence Generator, Update Strategy, Stored Procedure, and Custom Transformation objects.
1,000+ rulesStructured LLM prompting converts decoded ETL logic into syntactically correct, performance-optimised, platform-native code. Not generic output code engineered for each target platform's best practices.
Zero manual rewritingStatistical output-parity testing compares legacy ETL results against generated pipeline results on equivalent input data. Discrepancies are flagged with a confidence score not silently passed.
Data integrity guaranteedAutomated source-to-target lineage documentation for every converted mapping audit-ready, governance-compliant, and available as structured metadata for your data catalogue from day one.
Informatica session and workflow definitions are automatically reconstructed as equivalent DAGs for Databricks Workflows, Apache Airflow, or Azure Data Factory preserving execution order and error handling.
Every mapping is scored 0–100 by a weighted engine across transformation type, business logic, dependencies, and data behaviour, then classified into Simple, Medium, or Complex enabling precise scoping, pricing, and phased delivery.
See the full model ↓One XML input. Multiple possible outputs: Databricks PySpark, Snowflake SQL/Snowpark, BigQuery SQL, Amazon Redshift, Microsoft Fabric, Azure Synapse, DBT, Apache Spark, AWS EMR, Google Dataproc.
Before committing to full migration, InfaBridge analyses your entire Informatica estate and produces a detailed assessment: mapping count by complexity, dependency graph, estimated effort, and a phased migration roadmap.
Deploy via SaaS (fastest, no setup) or run entirely inside your VPN your data never leaves your environment. Hardware-locked, time-limited licence keys with built-in auto-expiry. Full compliance for regulated industries.
Migrate in phases. Re-run InfaBridge on updated XML when source mappings change. No one-shot commitment migration can be paused, resumed, and re-scoped without starting over.
Each migration batch produces a structured reconciliation report showing which mappings converted automatically, which required manual review, and evidence of output parity ready for client sign-off.
Every engagement adds to InfaBridge's rule library. Edge cases become rules. Complex patterns become templates. The engine gets measurably smarter with each migration, reducing manual intervention over time.
ROI calculator
Adjust the sliders to match your Informatica estate. Savings are calculated using published industry labour benchmarks every number is auditable.
Supported platforms
InfaBridge generates native, optimised code for every major cloud data platform not generic SQL that you then have to adapt.
PySpark · Delta Lake · Workflows
SQL · Snowpark · Tasks
Spark · Lakehouse · Pipelines
SQL · Spectrum · AWS Glue
Standard SQL · Dataform
SQL Pool · Spark Pool · Pipelines
dbt Core · dbt Cloud · YAML models
PySpark · AWS EMR · Glue
PySpark · Dataproc Serverless
Complexity framework
Every mapping is scored by a weighted complexity engine across transformation type, business logic, dependencies, and data behaviour not a raw object count. The result is a defensible, auditable classification your procurement and delivery teams can verify line by line.
Substantial custom logic embedded in the mapping
Combined pattern used for SCD / upsert logic
Explicit commit / rollback boundaries mid-mapping
Dependency chains 3+ levels deep
Vendor-specific syntax (CONNECT BY, MERGE, hints)
Shell / Python / Perl or DB procedure dependencies
Change-data-capture feeding slowly changing dimensions
Mixed RDBMS / file / API sources, near-real-time window
Every mapping is scored 0–100 across four weighted dimensions, each normalised independently:
| Dimension | Weight | Includes |
|---|---|---|
| Structural | 30% | Transformation count, diversity, source/target count, mapping size & connectors |
| Logical | 30% | Expression, lookup, join, aggregation & update/CDC complexity |
| Dependency | 20% | Mapplets, worklets, workflow dependencies, parameter files, external scripts |
| Operational | 20% | Data volume, SLA, error handling, restartability, reconciliation |
Before and after
The same estate, two very different paths.
| Metric | Manual migration | With InfaBridge |
|---|---|---|
| Time per Simple mapping | ~30 hrs | ~2 hrs |
| Time per Medium mapping | ~45 hrs | ~3 hrs |
| Time per Complex mapping | ~60+ hrs | ~5 hrs |
| 600-mapping estate: total hours | 18,000 hrs | ~3,000 hrs |
| 600-mapping estate: total cost | $1.5M–2.5M | ~$750K–$1.2M |
| Team size required | 8–10 engineers | 2–3 engineers |
| Typical timeline (600 maps) | 6–12 months | 6–10 weeks |
| Risk of logic errors | High manual SQL rewrites | Low automated + validated |
| Lineage documentation | Manual compilation, weeks of work | Auto-generated on completion |
| Re-runnability | Difficult changes cascade | Fully re-runnable on updated XML |
| Multi-target output | One target, start over for others | Multiple targets from single run |
Engagement model
A structured, low-risk path with measurable outcomes at every step. No new budget required we work from your existing Informatica spend.
InfaBridge scans your full Informatica estate and produces a complexity report mapping counts, tier distribution, dependency graph, estimated effort, and phased roadmap.
We run InfaBridge on your 10 most expensive or complex mappings in your environment, on your data. You see the automation rate, the output quality, and the time saved. Measurable before you commit.
We present a client-specific TCO and ROI model built on your actual POC results, not industry averages. Designed to present to your CFO, CTO, or steering committee with confidence.
Milestone-based, fixed-price delivery with your chosen delivery partner. Parallel runs maintain existing SLAs. Hypercare support through go-live and 90 days post-migration.
Deployment options
InfaBridge supports whichever deployment model your security and compliance team approves with no compromise on performance or protection.
Why InfaBridge
Everything above adds up to one thing: a faster, safer, cheaper path off Informatica backed by evidence, not promises.
1,000+ pre-built transformation rules. Structured LLM prompting, not generic AI output. Improves with every engagement.
Statistical output-parity testing on every mapping. Reconciliation report per batch. Legacy vs. cloud compared automatically.
One XML → Databricks, Snowflake, BigQuery, Redshift, Fabric, Synapse, DBT, Spark, EMR, Dataproc. Native code, not generic SQL.
SaaS for speed or In-VPN for compliance. Hardware-locked auto-expiry licensing. Source code never exposed.
FAQ
Everything you need to know before you start.
Start your migration
We run a free estate assessment on your XML no commitment, no cost. You get a complexity report, phased roadmap, and a fixed-price quote. The POC runs on your actual mappings so you see the savings before you sign anything.