DXC Data Integration Framework (DIF) modernizes traditional ETL by combining a metadata-first foundation with AI agents that automate the end‑to‑end data engineering lifecycle across Microsoft Azure and hybrid data platforms. AI DIF helps customers get started on Azure faster—or extend existing Azure data platforms—by shifting from hand-built, one‑off pipelines to automated, governed, and observable data solutions delivered in days, not months.

Why AI DIF Most enterprise data teams are still slowed down by: • Too much manual effort: Nearly 80% of pipeline work goes into repeatable tasks—metadata setup, configuration, deployment, testing, and monitoring • Fragmented platforms: Teams often run across Snowflake, Databricks, dbt, Microsoft Fabric, and more • Operational overhead: Governance, observability, and testing get added late, which raises cost and increases risk AI DIF tackles this by using an agentic approach: AI agents handle the execution work across the data lifecycle, while people set intent, review what’s generated, and approve changes before they go live.

Core Concept AI DIF is built on three core building blocks:

Agentic Swarm A central controller coordinates a set of specialist agents, each focused on one job: • Design the architecture • Model the data • Generate code • Deploy assets • Run tests • Set up observability Teams define the goal and validate outcomes; the agents do the execution. Every action is tracked, reviewable, and reversible.
Metadata-Driven Core At the center of AI DIF is a single metadata model that acts as the system of record for every pipeline. The metadata model is organized into three layers: • Design-time (Governance): Data products, lineage, ETL definitions, and dependencies • Run-time (Tech Stats): Execution metrics, quality KPIs, and reconciliation • Debug-time (Logging): Traces, logs, and telemetry

Because the metadata is the “single source of truth”, AI DIF can: • Generate platform-specific code automatically • Orchestrate pipelines dynamically using dependency graphs • Provide built-in governance and observability • Onboard new data faster by capturing metadata instead of hand-coding

Multi-Platform Code Generation AI DIF stays platform-agnostic at the metadata layer, then generates native artefacts for: • Snowflake: SQL, stored procedures, and native automation • dbt: Projects, models, tests, and semantic layer • Databricks: Notebooks, workflows, and Unity Catalog • DLT-meta: Declarative pipelines at scale

Key Capabilities

Simplified Data Engineering - AI DIF reduces the boilerplate work, so teams can ship enterprise-grade pipelines with far less manual effort.
Accelerated Delivery - Teams can stand up data platforms and production pipelines in days, making it easier to scale new use cases quickly.
Embedded Governance - Governance is built in from the start. AI DIF supports: • Metadata-driven lineage • PII detection during development • Row-level security by design • End-to-end auditability from day one
End-to-End Automation - AI DIF automates the full pipeline lifecycle:
Connect to data sources
Discover schemas and data products
Generate code and configuration
Deploy tables and workflows
Run pipelines
Monitor and optimize Automation is paired with human checkpoints at key stages, so teams can move fast without losing control.
Observability and FinOps - AI DIF brings cross-platform observability in one place, including: • Pipeline run tracking and data quality metrics • Anomaly detection and alerts • Incident routing into enterprise systems • Cost attribution and optimisation insights (FinOps)
Data Mesh Enablement - AI DIF supports data mesh ways of working by treating data products as first-class assets and making ownership explicit. • Data products are first-class entities • Domain ownership and dependencies are captured in metadata • Lineage and impact analysis work across products • Teams can operate independently with governed reuse

Differentiation AI DIF is different because it combines: • An agentic execution model that reduces manual data engineering work • A single metadata model that can drive multiple platforms • Governance and observability built in, rather than added later • End-to-end automation with human-in-the-loop approval • Rapid scaling through metadata-based onboarding

Business Impact In practice, AI DIF can help enterprises: • Cut manual data engineering effort by up to ~80% • Reduce time to production from months to days • Roll out pipelines at scale with consistent standards • Improve data quality, governance, and audit readiness • Lower operational and platform costs through automation

AI DIF marks a shift in data engineering—from code-heavy, manually maintained pipelines to metadata-driven platforms run with agent assistance helping build scalable, well-governed, future-ready data ecosystems with less effort—and in far less time.

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