Question 1

What is Novuz?

Accepted Answer

Novuz converts legacy data warehouse logic into production-ready cloud pipelines, deterministically, and proves every object. It converts schemas, stored procedures, ETL and orchestration, and runs inside your environment.

Question 2

Is Novuz a tool or a service?

Accepted Answer

Novuz is software, not a services team. Your engineers run it inside your environment. The resolver handles edge cases with engineer approval, and Novuz engineers back every engagement. Partners can run the program around it.

Question 3

How much manual work is left?

Accepted Answer

Typically 95% or more of objects convert with no manual edit. A one-line fix counts as manual, and dead code is excluded from the count. The reconciliation report lists every open item with its cause and fix.

Question 4

What does Novuz migrate?

Accepted Answer

Database schemas, stored procedures and functions, ETL packages, scripts, and workflow and orchestration, plus the data itself. Converted code is native to your target, with no Novuz runtime in it.

Question 5

Which sources and targets are supported?

Accepted Answer

Sources: SQL Server (T-SQL), SSIS, Teradata including BTEQ, Hadoop (Hive, Impala) and Snowflake. Targets: Databricks, Microsoft Fabric, Snowflake, Apache Iceberg and Delta Lake. Not today: Oracle, mainframe, Informatica, DataStage, Netezza and Redshift. Coverage is confirmed per estate during the assessment.

Question 6

Is the conversion AI-based?

Accepted Answer

No. The conversion is rule-based. Fixed, versioned rules, selected by inference over each object’s patterns, convert about 95% of objects with no model calls. The same input gives the same output every time; run it twice and the diff is empty.

Question 7

How is a language model used, and where does my code go?

Accepted Answer

Only the resolver uses a language model, for the roughly 5% of objects fixed rules do not cover. It runs on a model you approve: Claude or Gemini under your own agreement, or a local model through Ollama with no external calls. An engineer approves every proposal. No outbound connection is required; if you choose a hosted model, only the edge-case fragments go to it. There is no shared learning across customers.

Question 8

Does Novuz use AI tokens?

Accepted Answer

Rule-converted objects use none. The resolver uses 700 to 1,000 tokens per edge case. The price is fixed before work starts, so token use never changes what you pay.

Question 9

What results have customers seen?

Accepted Answer

On delivered estates, typically 95% or more of objects convert with no manual edit, and reconciliation is above 95% on first pass. Estates of 1,000 to 1,600 objects take 4 to 8 weeks. Novuz is about 40% cheaper than an SI-led rewrite, at a fixed price set before work starts. Definitions are on the Methodology page.

Question 10

Can Novuz handle mission-critical systems?

Accepted Answer

Yes. Novuz is built for regulated and audit-heavy estates. Every object is checked at schema, row-count and column level and ships with a reconciliation report your auditor can read.

Question 11

Do I need to install anything?

Accepted Answer

Not to start. The scorecard and the assessment need nothing installed; for the assessment you can run the discovery container yourself and send only the report. For the migration, Novuz runs as a Docker container or VM inside your network, air-gapped if you need it.

Question 12

How is Novuz different from platform-native tools?

Accepted Answer

Platform-native tools are a good start for SQL translation to their own platform. Novuz converts the procedures, ETL and orchestration they leave behind, to five targets. It proves every object with a reconciliation report and prices the estate before work starts. Many teams run the free tool first and bring us the remainder.

Question 13

Is Novuz agentic?

Accepted Answer

The conversion is not; it is rule-based and gives the same output every run, with the small share rules cannot resolve approved by an engineer. What is agentic is the access: agents can drive the platform through its APIs and operate the delivered pipelines through MCP.

Question 14

What is logic debt?

Accepted Answer

Logic debt is the business logic locked in legacy stored procedures, ETL packages and schedules. Data migration is solved. Logic migration is not: the logic sets the cost and the risk of a migration.

Question 15

How much does it cost?

Accepted Answer

A fixed price set before work starts, based on object count and complexity, never hours. The estate assessment is $3,000, credited. A First Domain is $25,000 to $40,000, credited. A full estate is fixed from the assessment, typically $40,000 to $200,000 for 1,500 to 10,000 objects.

Question 16

Who is Novuz not for?

Accepted Answer

Estates under about 300 objects; Oracle-only or mainframe estates; teams with no target and no date inside 18 months; buyers who want staff augmentation rather than software; and organizations that cannot run software inside their environment or share code for an assessment.

Question 17

How do I evaluate Novuz?

Accepted Answer

Take the ten-minute logic debt scorecard. Or get the $3,000 estate assessment: a fixed price on your own code in three business days. Or book a call with the people who built it.

Informatica asset	What it becomes
IDMC / IICS Cloud Data Integration mappings	SQL or PySpark pipelines on the target, one per mapping, with the transformation order preserved
Mapplets and reusable transformations	Shared functions or modules, called from every pipeline that used them
Source, target, filter, router, joiner, aggregator, expression, sorter, union, normalizer and sequence transformations	Set-based SQL or DataFrame operations
Connected and unconnected lookups	Joins or cached lookups that return the same rows, including first-match and last-match behavior
Update Strategy and slowly changing dimension patterns	MERGE statements with the same insert, update and delete rules
Mapping tasks, taskflows and schedules	Workflows on the target with the same steps, branches and dependencies
Parameter files, in-out parameters and $$ variables	Job parameters and secrets on the target, with the same defaults
Informatica Data Quality rules, rule specifications and mapplets	SQL or PySpark data quality checks, with results written to tables you can report on
Data Engineering Integration (Big Data Management) mappings that run on Spark or Hive	PySpark or Spark SQL on the target
Connections	Target connections and secrets, set up in your environment

Informatica	Databricks (AWS, Azure or Google Cloud)	Microsoft Fabric	Snowflake	Google BigQuery	Apache Iceberg or Delta Lake
Mappings and mapplets	PySpark notebooks or Databricks SQL	Fabric notebooks or Warehouse SQL	Snowflake SQL or Snowpark	GoogleSQL queries and stored procedures	Spark SQL or PySpark jobs
Taskflows and schedules	Databricks Workflows	Fabric Data Factory pipelines	Snowflake tasks	The orchestrator agreed in the assessment	The orchestrator agreed in the assessment
Data Quality rules	SQL checks or pipeline expectations	SQL checks in notebooks or the Warehouse	SQL checks or data metric functions	SQL checks with results tables	SQL checks in the pipeline
Data Engineering Integration mappings	PySpark or Spark SQL	Fabric Spark notebooks	Snowpark	GoogleSQL, or Spark on Databricks on Google Cloud	PySpark or Spark SQL

Informatica migration: IDMC and IICS, Data Quality and Data Engineering Integration

Why are teams moving logic out of Informatica now?

What does Novuz convert in an Informatica estate?

What breaks in an Informatica migration?

How does Informatica map to each target?

What do other tools do with Informatica, and what do they leave?

How does an Informatica migration with Novuz run?

Where to read next

Frequently asked questions

Sources and further reading

Find out what your logic debt is costing you.