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    DBiz DataCompass – AI-Infused Data Engineering Framework

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    DBiz DataCompass is an AI-infused data engineering framework that orients every data journey — from siloed, ungoverned raw data to autonomous, AI-ready intelligence. Built around a five-layer architecture (Migration, Analysis, Ingestion, Transform, Consumption) and anchored by a metadata catalog as its magnetic core, DataCompass meets organisations wherever they are on the data maturity axis and navigates them north — regardless of platform. It works across Databricks, Microsoft Fabric, hybrid, and legacy environments, and integrates AI tooling at every layer to accelerate delivery and shorten the path from data to business outcomes.

    Overview

    DataCompass is a navigation framework — a structured methodology and delivery accelerator that orients every data engagement from raw, siloed data toward AI-ready intelligence. Its compass metaphor is deliberate: True North is AI-ready intelligence at every touchpoint. Every organisation is somewhere on the maturity axis. DataCompass shows the way and provides the tooling, methodology, and AI integrations to move there faster.

    The framework is platform-agnostic. Whether the organisation runs Databricks, Microsoft Fabric, a hybrid estate, or a legacy on-premises environment, DataCompass meets them where they are and navigates the same north — through the same five layers, the same maturity model, and the same AI-infused delivery approach.

    Associated AWS Products This professional services listing relates to the following AWS services and products:

    • Amazon S3 — primary object storage layer for raw, curated, and governed data assets

    • AWS Glue — ETL and data cataloguing across the Ingestion and Transform layers

    • Amazon Redshift — data warehousing for analytics and consumption layer workloads

    • Amazon Athena — serverless SQL querying across the data lake for Analysis layer workloads

    • AWS Lake Formation — data lake governance, access control, and catalog management

    • Amazon Bedrock — foundation models powering AI-infused data engineering across all five layers

    • Amazon SageMaker — model training, versioning, and serving for AI & ML maturity stages

    • Amazon QuickSight — BI and visualisation for the Consumption layer

    • AWS IAM — identity and access management enforced across the governed data estate

    • Amazon CloudWatch — pipeline monitoring and alerting

    • AWS CloudTrail — audit logging and lineage trail across the data estate

    The Five-Layer Architecture

    DataCompass structures every data engagement across five functional layers, each with defined capabilities, AI tooling integrations, and a clear relationship to the metadata catalog that underpins them all.

    The Metadata Catalog — The Magnetic Core The metadata catalog is the invisible force that keeps the needle pointing north across all five layers. It feeds and orients every layer simultaneously — capturing metadata as each layer runs, and enriching AI tool context as engagement matures. Supported catalogs: Unity Catalog (Databricks), OneLake Catalog / Microsoft Purview (Fabric), Open Metadata, Apache Atlas, and Custom Catalog implementations.

    The Data Maturity Model — Five Stages DataCompass maps every organisation on a five-stage maturity axis across five dimensions: Data Quality & Pipelines, Governance & Cataloguing, Analytics & Reporting, AI & ML Maturity, and Organisational Adoption. Platform Coverage: Databricks, Microsoft Fabric, Hybrid & Legacy

    AI Tooling Integrated Across All Five Layers DataCompass integrates AI tools natively at the Migration, Analysis, Ingestion, Transform and Consumption layers of the framework — not as an add-on, but as an accelerant built into the delivery methodology.

    Engagement Operating Model — Five Phases DataCompass engagements follow a structured five-phase operating model that maps directly to the data maturity stages. Each phase has a defined goal, delivery scope, and maturity advancement target.

    Problems DataCompass Eliminates

    • No structured way to understand the current data maturity state or what the next step looks like

    • Platform sprawl — different teams making different choices with no common methodology

    • AI initiatives that fail because the data foundation underneath them is not ready

    • Governance gaps — ungoverned data, unclear ownership, no lineage visibility

    • Long journeys from raw data to production models because each stage is reinvented from scratch

    • Data engineering teams spending time on plumbing instead of intelligence delivery

    • Business teams locked out of data — waiting on IT for every report and insight

    Business Value: Data You Can Trust, Maturity Visibility in Days, Not Weeks Structured Path to AI Readiness, Faster Time to Value, Platform Flexibility, Foundation That Supports AI, Governed by Default, Compounding Advantage.

    Highlights

    • Navigation Framework for Every Data Maturity Stage Five-stage Data Maturity Model: Data Works → Data Shows → Data Decides → Data Acts → Data Leads
    • AI Infused at Every Layer AI tools integrated natively across Migration, Analysis, Ingestion, Transform, and Consumption layers
    • Platform-Agnostic, Enterprise-Ready Works across Databricks Lakehouse, Microsoft Fabric, hybrid, and legacy on-premises environments

    Details

    Delivery method

    Deployed on AWS
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    Support

    Vendor support

    For enquiries, scoping, and support related to DBiz DataCompass, please contact: Email: sales@dbizsolution.com  Website: https://www.dbizsolution.com  DBiz provides full engagement support for all DataCompass deliveries, covering maturity assessment, platform-specific delivery guidance, AI tooling integration, and ongoing managed services under the Retain phase of the operating model.