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
- 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.
- 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 legacyuser_events_2019table" — the answer exists. - 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
2. Transformation Layer (ETL/ELT)
ARCXA tools perform the "heavy lifting" of moving data from Point A to Point B while applying business logic and schema changes.
The transformation layer generates more lineage events than any other part of the migration — and currently captures almost none of them in a governed, queryable form.
ARCXA's position is simple: the transforms are already happening, the events are already occurring, the business logic is already being applied.
The only question is whether those events get recorded in a way that survives the project and compounds into organizational knowledge, or evaporate the moment the pipeline finishes running.
ARCXA resolves the three failure modes in the transformation layer;
1. Logic drift. Transformation pipelines evolve. A dbt model gets updated, an Airflow DAG gets refactored, a Fivetran connector applies a new normalization rule. Without ARCXA, there's no record of what changed when, and no way to know which downstream fields were affected by the change. With the NNX graph, every version of every transform is recorded, and the impact surface of any change is a query.
2. Undocumented business logic. The most dangerous transformations are the ones that encode business decisions — revenue recognition rules, customer deduplication logic, currency conversion assumptions — inside pipeline code where no governance process can see them. ARCXA surfaces these by recording the transform as a governed artifact with metadata, making them visible to data stewards and auditors who would otherwise never know they existed.
3. Testing without traceability. Most transformation layers have some form of data quality testing — dbt tests, Great Expectations checks, custom assertions. But the test results aren't linked to the lineage. ARCXA closes this by attaching test outcomes to the relevant triples, so the governance record shows not just what transformed a field but whether that transformation was validated, and what the validation result was.
3. Target Validation & Audit
ARCXA doesn't replace target validation tooling. It makes validation results permanent, field-level, and connected to the lineage graph that already exists from the transformation layer.
These tools run after the migration to prove that the data arrived intact, matches the source counts, and follows compliance rules.
The three validation failure modes ARCXA resolves
1. Validation without lineage context. A row count mismatch is alarming, but without knowing the lineage of the affected table it's nearly impossible to diagnose quickly. Did the mismatch originate in the source extract, the transformation, or the load? With ARCXA, the validation failure is linked to the transform that produced the field, which is linked to the source it came from. The diagnosis surface shrinks from the entire pipeline to the specific triple that failed.
2. Compliance validation that can't be proved. Regulated industries don't just need data to be correct — they need to prove it was validated by a specific person, at a specific time, against a specific rule, and that the rule itself was approved. ARCXA records all of this. The validation isn't just a result, it's a governed artifact with authorship, timestamp, threshold, and linkage to the compliance rule it satisfies. That's the difference between a validation report and a validation certificate.
3. Validation decay. A migration is validated on go-live day. Three months later, a pipeline change silently affects a field that was previously certified. Without ARCXA, nobody knows the certification is now stale. With the NNX graph, any transform that touches a certified field automatically flags the linked validations as requiring re-certification — because the graph knows which validations are downstream of which transforms.
Why the "ARCXA Collapse" Matters
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.
The Neural Network eXchange (NNX) ROI compounding angle — where the multiplier really lives; 10% of migration cost - Migration Insurance
The single best 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.
No comments:
Post a Comment