AIDQ for Databricks

As organizations modernize their data platforms on Microsoft Azure, many adopt the Azure Databricks Lakehouse Platform to power scalable analytics. However, they often struggle with fragmented ingestion pipelines and inconsistent data quality. The complexity of today’s enterprise data estates—with their growing volume of sources, evolving schemas, and governance demands—can hinder speed, increase manual rework, and erode trust in analytics. WinWire’s Automated Data Ingestion and Data Quality (AIDQ) solution is purpose-built to solve these challenges. Designed natively for Azure Databricks using Spark, Delta Lake, and Unity Catalog, AIDQ automates data onboarding and enforces quality at scale through a metadata-driven framework. By leveraging Azure-native tools and services, the solution helps customers accelerate time-to-insight, reduce manual rework, and build governed, high-performing Lakehouse architectures on Microsoft Azure.

WinAIDQ Solution Approach

Leverage the Databricks-native AIDQ accelerator to enable a robust Lakehouse ingestion and quality framework.

Business Value

• Up to 50% reduction in development and rework time
• Source & Schema-agnostic ingestion pipelines leveraging PySpark
• Automated, rule-based data quality enforcement
• Unity Catalog integration for lineage and access governance
• Accelerated time-to-insight with trusted gold-layer data

Key Deliverables

• Architecture and Design assets for Databricks ingestion and data quality
• Metadata configuration templates and validation rule library
• Pipelines and Notebooks to automate ingestion
• A working AIDQ pilot for 2–3 datasets
• Unity Catalog governance setup
• Scale-out roadmap to cover full enterprise ingestion needs

Kickstart your journey with our 4-week pilot to realize the value of a metadata-driven ingestion and data quality framework optimized for the Databricks Lakehouse.