Accelerate transition with a predictablepath to modernization.

Production-proven tooling that compresses a typical 18-month cloud or lakehouse migration to 10-16 weeks per workload cohort. It inventories the source, migrates the pipelines, reconciles cell-by-cell, and generates synthetic data when production cannot be exposed. Built and used by our own delivery teams across Cloudera, Databricks, Snowflake, and hyperscaler migrations.

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10-16

Weeks per workload cohort

100-250

ELT jobs migrated per engagement

99.9%+

Cell-level reconciliation match

The Migration Suite four-step flow: ProbeX inventory + lineage, KodeX auto pipeline rewrite, ReconX cell-level reconciliation, SynthX synthetic test data

Step 01 · Inventory & Dependency Mapping

ProbeX

Day-one visibility before the first line gets migrated.

Discovers and catalogs every pipeline, dataset, and dependency across the source estate, including the ones nobody documented. Builds a full lineage graph linking ELT jobs to source tables, downstream consumers, and business owners.

Automated scan across Informatica, SSIS, Talend, Hadoop, Oracle, Teradata, SAS
Full lineage graph, source-to-consumer with business-owner attribution
Output feeds directly into KodeX as a migration backlog

Pipeline discoveryLineage graphOwnership mapping

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Step 02 · Automated Pipeline Migration

KodeX

Automatically convert legacy pipelines into optimized cloud-native formats, whether you are moving to Azure, AWS, GCP, Databricks, or Snowflake. The platform cleanly separates standard automation from complex edge cases, using engineer-in-the-loop tooling to handle nuanced patterns safely without silent errors.

Broad Enterprise Coverage: Move away from legacy giants with native translation for Teradata, Oracle (including PL/SQL), Hadoop, Netezza, and SQL Server directly to modern architectures like Azure Fabric, Databricks, Snowflake, Redshift, BigQuery, or Synapse.
Optimized Cloud-Native ELT: Shift from rigid, compute-heavy legacy pipelines to scalable ELT patterns that leverage the full analytical power of your chosen target cloud estate.
Dual-Engine Precision: Maximize efficiency by running automated rewrites for standard ingestion and transform logic, while routing highly complex or legacy-bound patterns through intelligent engineer-in-the-loop guardrails to guarantee zero broken code.

ELT conversionCloud-native targetsPattern libraryHuman-in-the-loop

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Step 03 · Cell-Level Reconciliation

ReconX

99.9%+ match. Auditable. Business-signed-off.

The reason cutovers get rolled back is almost never the migration itself, it is the reconciliation step nobody planned for. ReconX runs source-vs-target comparison at row and cell level with configurable tolerance bands for known transformations.

Row-count and cell-level comparison
Configurable tolerance bands for documented transformations
Auditable written reconciliation report per workload

Tolerance configurationAudit reports

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Step 04 · Synthetic Test Data

SynthX

High-Fidelity Synthetic Test Environments.

Generates schema-aware, relationship-preserving synthetic test data when production data cannot be exposed for development or testing, which is the default in regulated industries. Distributions, cardinalities, and referential integrity are preserved across tables.

Schema-aware generation with referential integrity preserved
Distribution and cardinality matching against profiled source
Scale-controllable, small seed produces large synthetic dataset
Compliance-friendly, no production data exposure at any point

Schema-awareReferential integrityDistribution matchingScale-controllable

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Let's talk.

Twenty-five minutes. Straight to the point.

Tell us what's in your data and AI stack, what's stalled, and what would change if it worked. We'll share what we've shipped against similar patterns in production, and what makes sense as a first step.

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