ARCXA (NNX) ToolBoX

ARCXA - Neural Network Exchange (NNX) ETL ASSIST - One graph, one lineage model, one audit artifact — regardless of whether you're moving from DB2 to Snowflake or Oracle to Databricks.

Traditional legacy migrations typically rely on a fragmented stack of tools. 

ARCXA NNX provides a common lineage model, insuring organizations do not often end up with "broken" data history and massive manual overhead for reconciliation "Day 2" Problems.

ARCXA Reduces Procurement conversations: Scope of Systems covers: PostgreSQL, MySQL, Oracle, DB2, SAP HANA, Snowflake, and Databricks in a single governed pipeline is significant for procurement conversations.

 

ARCXA tools identify "dark data," map legacy dependencies, and inventory what actually exists in the source system.

Legacy migration projects typically require separate tooling for the source crawl, the transformation layer, and the target validation — each with its own vendor relationship and none of them sharing a lineage model. ARCXA collapses that stack. 

Three governance artifacts ARCXA produces automatically

1. Field-level lineage certificates — for every field in the target schema, a complete chain of custody from source to destination, including every intermediate transform and the identity of whoever approved it. This is the document regulators actually ask for.
2. Deprecation registry — every legacy asset that was excluded from migration is recorded with a reason code (arcxa:deprecated, arcxa:redundant, arcxa:out_of_scope) and a timestamp. When an auditor asks "what happened to the legacy user_events_2019 table" — the answer exists.
3. Transform audit log — every transformation function applied during migration is versioned and linked to the fields it touched. If a transform contained a bug that was later corrected, the graph shows which fields were affected and when the correction was applied.

SI Simplification Argument: fewer tools, fewer integrations, fewer points of failure, one throat to choke. For the enterprise buyer, it's a risk argument: the lineage graph spans the entire migration regardless of how heterogeneous the source landscape is.

Separate tools by name, type, and primary user that ARCXA collapses into a single graph:

1. Source Crawl & Discovery

Tool Name	Type	Primary User
Kodesage	AI Legacy Knowledge Platform	Solutions Architect / Dev Lead
AppDynamics	Forensic Performance/Dependency Mapping	Infrastructure Engineer
Datadog	Service Dependency Mapping	DevOps Engineer
AWS Schema Conversion Tool (SCT)	Heterogeneous Schema Discovery	Cloud Architect
Informatica Enterprise Data Catalog (EDC)	Metadata Discovery & Cataloging	Data Steward / Architect

2. Transformation Layer (ETL/ELT)

These tools perform the "heavy lifting" of moving data from Point A to Point B while applying business logic and schema changes.

Tool Name	Type	Primary User
Informatica PowerCenter	Enterprise ETL Tool	ETL Developer
Fivetran	Automated ELT Pipeline	Data Engineer
Matillion	Cloud-Native ETL/ELT	Data Engineer
AWS Glue	Serverless Data Integration	Cloud Data Engineer
SnowConvert AI	Automated Code/SQL Conversion	Migration Specialist
dbt (data build tool)	In-Warehouse Transformation	Analytics Engineer

3. Target Validation & Audit

These tools run after the migration to prove that the data arrived intact, matches the source counts, and follows compliance rules.

Tool Name	Type	Primary User
QuerySurge	Data Warehouse Validation	QA / Test Engineer
DataGaps ETL Validator	Automated Data Reconciliation	Data Quality Analyst
iCEDQ	Cross-Platform Data Testing	Compliance/Audit Officer
Great Expectations	Data Quality Framework	Data Engineer / Data Scientist
Concentrus (ROI Roadmap)	Financial Reconciliation Services	Finance / Project Lead

Why the "ARCXA Collapse" Matters

In the "Old Way" above, an Audit Officer using iCEDQ has no visibility into the transformation logic applied by a Data Engineer in dbt, who in turn has no visibility into the "Dark Data" discoveries made by the Architect using Kodesage.

ARCXA eliminates these gaps by using a single Triple Store architecture:

• One Graph: Merges discovery, movement, and validation into one model.

• One Lineage: Tracks a data point's "DNA" from the legacy DB2 server all the way to a Snowflake dashboard.

• One Audit Artifact: Generates a single proof-of-integrity that satisfies both technical QA and regulatory auditors simultaneously.

Feature	Traditional ETL (The Mover)	ARCXA (The Meaning)
Data Model	Relational: Rows/Columns (Fixed)	Semantic: Triples (Subject-Predicate-Object)
Lineage	Technical: "Table A moved to Table B."	Atomic: “Customer X is Influenced by Policy Y.”
Logic	Hidden: Buried in SQL/Python scripts.	Explicit: Part of the Triple Store Graph.
Auditability	Snapshot-based (Hard to see evolution).	Immutable History: Every change is a new triple

The compounding angle — where the multiplier really lives

The single most underrated aspect of the ROI argument is that the NNX graph doesn't reset between projects. Every Snowflake migration ARCXA touches adds to the knowledge graph. The second engagement starts with the intelligence from the first. By the third migration, your team has a proprietary mapping library — field-level transform patterns, common schema equivalences, known data quality issues in legacy systems — that competitors building fresh spreadsheets every time simply don't have.

A typical enterprise Snowflake migration runs $400k–$800k in platform licensing, compute, and SI fees. The hidden follow-on cost that most teams don't budget for is the data catalog and governance retrofit — usually a separate 4–6 month project that costs another $150k–$300k and starts from scratch because nothing was instrumented during the migration itself.

ARCXA eliminates that second project entirely. When attached at roughly 10% of migration cost, it produces the catalog as a byproduct of the migration — every schema move, transformation, and lineage relationship is recorded in the NNX graph in real time. The catalog isn't a post-project deliverable. It's a side effect.