3X Data Engineering FAQs

AI-augmented data engineering uses purpose-built AI accelerators, metadata intelligence, and codified engineering patterns to compress repetitive work across the data engineering lifecycle. It applies to discovery, complexity scoring, architecture design, data modeling, code conversion, testing, validation, and documentation. Engineers stay in the loop on architecture decisions and edge cases. The accelerators handle the volume work. (more)

The Modernization Canvas is the deliverable from a 3XDE Modernization Assessment, produced in 8 business days. It includes source-connected estate inventory, granular complexity scoring per object, target architecture for Microsoft Fabric, Snowflake, Databricks, BigQuery, or Redshift, a detailed roadmap with work breakdown structure and effort estimates, and target data models with auto-generated ETL and bulk-converted sample code. Every Canvas is reviewed by a senior architect before delivery. (more)

Best-fit projects include enterprise cloud migration to Microsoft Fabric, Snowflake, Databricks, BigQuery, or Redshift; greenfield platform builds; legacy ETL modernization including SSIS, BTEQ, and PL/SQL conversion; reverse engineering of undocumented stored procedures; metadata discovery on post-acquisition estates; and migration assessments where teams need a fact-based plan before scoping execution. Engagements run from 5 business days to 24 weeks depending on scope. (more)