Benza converts your mappings, expressions, workflows, and orchestration to the platform of your choice — with zero data access, deterministic translation, and a complete audit trail.
Snowflake, BigQuery, Redshift, Databricks, Postgres, DuckDB
Databricks notebooks with runtime library
Snowflake stored procedures + Tasks
GlueContext, Catalog, Step Functions
Data Flows, Pipelines, ARM templates
SQLX, native Google Cloud + BigQuery
Benza never connects to your databases or PowerCenter repositories. It operates exclusively on exported XML metadata — on your premises, in your VPC, with no network egress.
For classified workloads, banking, healthcare, and government environments, this is non-negotiable. We don't see your data. We don't need to.
Every PowerCenter expression — IIF, DECODE, TO_DATE, NVL, and 111 documented functions in total — is mapped via pre-validated, deterministic rules. The output is predictable and repeatable, not a "best guess" from a language model.
All semantic decisions are made by a shared translation engine before any target-specific code is generated. Code generators are pure formatters — they cannot alter semantics or confidence levels.
Benza's shared translation engine applies all rules to a target-agnostic intermediate representation before generating code for your chosen platform. You get the same correctness guarantees whether you choose dbt, Snowpark, or any other target.
Every translated element carries a confidence level derived from pre-computed rule metadata — not a runtime guess, not a statistical score. Your team knows exactly what needs review.
| Level | Meaning | Action |
|---|---|---|
| 1 | Functionally identical for all inputs | Deploy with confidence |
| 2 | Same result, different syntax (e.g., LTRIM(RTRIM(x)) → TRIM(x)) | Safe — review optional |
| 3 | Identical for normal data; edge cases may differ (nulls, precision) | Review edge cases |
| 4 | Best-effort translation | Manual review required |
| 5 | Cannot translate — stub with full context generated | Implement manually |
Source, Target, Expression, Filter, Lookup, Joiner, Aggregator, Router, Update Strategy, Union, Sequence Generator, Normalizer, Mapplet, Stored Procedure — all handled.
Every documented Informatica function. String, date, numeric, conversion, aggregate, conditional, null-handling — mapped to semantically equivalent constructs in each target platform.
Control flow, conditional branches, worklets, email tasks, timer tasks — captured and translated to your target's orchestration: Airflow DAGs, Dagster jobs, Prefect flows, Databricks Workflows, Snowflake Tasks, Step Functions, ADF Pipelines, or Cloud Workflows.
For update strategies, filters, and routers that discard rows, Benza generates paired outputs: main target + rejects with reason codes. Consistently, across all six platforms.
Every conversion produces a per-node audit record. Because all semantic decisions are made before code generation, the audit trail is complete — not reconstructed after the fact.
Your validation and compliance teams get the full picture for every node in every pipeline — no need to reverse-engineer the translation or trust a black box.
"We migrated 500+ PowerCenter mappings to dbt. Benza’s audit trail streamlined compliance review by making every transformation traceable."
Benza detects unmappable elements at every stage of the pipeline: parsing, rule resolution, and code emission. You get an explicit degradation report before conversion.
| Category | Meaning | Example |
|---|---|---|
|
Implementation gap
|
Addressable in a future update — proceed with a workaround | Sequence generator with custom cycling |
|
Platform limitation
|
Your target doesn't have this feature — redesign needed | Dynamic lookup (uncached) → dbt |
|
Manual required
|
Stub generated with full context — your team completes it | Stored procedure with database calls |
Silence is not allowed. Every successfully converted element is explicitly marked. Every unconverted element is explicitly listed. The absence of degradation is never implied by omission. Delivered before you authorise the full migration — you decide whether to proceed.
Conversion runs in seconds per mapping. The time you spend is on review and validation — not waiting for the tool. A 500-mapping estate converts in minutes; the migration completes in weeks.
If your target platform needs helper functions (like PySpark's runtime library), Benza gives you a complete specification — including type stubs — so your team knows exactly what to implement. Compile-time verification ensures it matches.
Re-run conversion with the same inputs — deterministic output. Diff to see changes. Same input + same converter version = same result, every time.
Benza generates validation queries alongside your translated code. When you're ready, the reconciliation engine connects to both your legacy and modern databases and produces per-pipeline pass/fail reports. Conversion stays airgapped; validation runs when you choose.
If your PowerCenter estate uses undocumented or proprietary patterns, the custom rules engine lets your team add translation rules immediately — with the same audit trail and confidence classification as built-in rules.
Deploys in minutes. Python 3.9 and pip install.
No infrastructure, no servers, no configuration. Runs on your laptop, your build server, or your VPC —
wherever your XML exports live.
Install Benza on your own machine. Run it against your own PowerCenter exports. See the converted code, the audit trail, and the degradation report — before you talk to us.
No data access. No network calls. No sign-up. Evaluate on your terms.
When you're ready to convert at scale, request a licence.
Choose your target: dbt, Snowpark, Glue, Dataform, ADF, or PySpark.