Enterprise Reporting Platform: Power BI, Synapse Analytics, and Data Governance

Executive Summary

[3-4 sentence overview of integrated analytics stack, ingestion, modeling, visualization, and governance value.]

Business Challenge

[Fragmented reporting silos, inconsistent data definitions, lack of lineage & security segmentation.]

Solution Architecture

flowchart LR src[Operational Sources] --> adf[Data Ingestion (ADF / Synapse Pipelines)] adf --> bronze[(Bronze - Raw)] bronze --> silver[(Silver - Cleaned)] silver --> gold[(Gold - Curated Semantic)] gold --> pbi[Power BI Semantic Models] pbi --> reports[Power BI Reports / Apps] gold --> lineage[Purview Catalog] pbi --> lineage

Prerequisites

Azure Subscription
Synapse Workspace
Power BI Premium / Fabric capacity
Azure Purview (Data Catalog)

Part 1: Ingestion Layer

Step 1: Pipeline Design

[Landing patterns, incremental loads, watermarking]

Step 2: Raw Zone Governance

[Access control & auditing strategy]

Part 2: Transformation & Modeling

Step 1: Spark / SQL Transformations

[Medallion pattern application]

Step 2: Semantic Modeling for Power BI

[Star schema, role-level security]

Part 3: Data Governance & Lineage

Step 1: Purview Classification

[Scan sources, apply sensitivity labels]

Step 2: Data Contracts & Ownership

[Stewardship model]

Part 4: Visualization & Distribution

Step 1: Power BI Apps & Workspaces

[Workspace segmentation: Dev/Test/Prod]

Step 2: Performance Optimization

[Aggregations, incremental refresh, query caching]

Security & Compliance

Encryption at rest + in transit
Row-level security (RLS) definitions
Access reviews & Just-In-Time elevation

Performance Optimization

Materialized views for heavy joins
Aggregations for large fact tables
Optimize delta table file sizes

Cost Management

Auto-pause SQL pools
Reserved capacity for predictable workloads
Monitor Power BI dataset refresh durations

Monitoring & Observability

Synapse metrics (DTU, pipeline duration)
Power BI refresh failures alerts
Purview scan health

Best Practices

Establish semantic layer governance early
Separate compute vs storage responsibilities
Enforce naming conventions (schema.table)

Troubleshooting

Issue: Slow BI report queries
Solution: Introduce aggregations + optimize DAX measures

Issue: Data drift between environments
Solution: Automate deployment with CI/CD & data validation checks

Key Takeaways

Layered architecture improves reliability & performance.
Governance (Purview + semantic models) enables trust.
Optimized refresh & aggregations reduce cost.

Next Steps

Implement automated data quality checks
Add Fabric Lakehouse integration

Additional Resources

What reporting challenge are you solving this year?