The Two Pillars That Determine Success

Data Quality Cannot Be an Afterthought

The most common mistake we see is treating data quality as something to address after the pipeline is running. This approach creates technical debt that compounds over time and erodes trust in your data.

Your CDC pipeline will ingest millions of events daily. Without proper validation at each layer, small issues become major problems. A single source system changing a column from integer to string can silently corrupt downstream analytics for days before anyone notices.

What you need to implement from day one:

• Validation at the Bronze layer should focus on structural integrity. Every record landing in your raw layer needs verification that required fields exist, timestamps are valid, and CDC operation types are recognized.
• The Silver layer is where business validation happens. Here you check referential integrity, apply domain specific rules, and flag anomalies. A customer ID that does not exist in your customer master table needs to be caught here.
• Schema drift detection deserves special attention. Source systems change without warning. Your pipeline needs to detect these changes before they break downstream processes.
• The quality score approach works well in practice. Rather than binary pass or fail checks, calculate a quality score for each batch. A score above 95 percent proceeds normally. Between 90 and 95 percent sends a warning. Below 90 percent halts processing.

Replication Lag Requires Active Management

The second pillar is understanding and managing replication lag. In a real time pipeline, the value of data degrades rapidly with age. A five minute delay might be acceptable for daily reporting but catastrophic for fraud detection or inventory management.

Lag accumulates at multiple points in your pipeline. There is capture lag between when a change occurs in the source database and when the CDC mechanism detects it. Processing lag occurs within Eventstream as events are transformed and routed. Ingestion lag happens between Eventstream and your destination tables. Each component adds latency, and under load, these delays compound.

Building effective lag management:

• Monitor each stage independently. Knowing your total lag is useful, but knowing where lag accumulates is actionable.
• Establish baselines before setting alerts. Collect at least two weeks of baseline metrics before configuring alert thresholds.
• Implement automatic recovery procedures. When lag exceeds acceptable thresholds, your system should respond without waiting for human intervention.

Operational Foundations You Cannot Skip

Capacity Planning Prevents Expensive Surprises

Microsoft Fabric uses a capacity unit model where all workloads draw from a shared pool. Underprovisioning leads to throttling and failed jobs. Overprovisioning wastes budget. Start with realistic estimates based on your data volumes.

The F4 SKU handles most development and small production workloads comfortably. Medium deployments with 10 to 25 sources typically need F8. Large enterprise deployments should start at F16 and scale based on observed utilization. Watch for sustained utilization above 70 percent as a signal to consider scaling up.

Network Security Shapes Your Architecture

For production deployments handling sensitive data, network isolation is not optional. Private endpoints keep traffic on the Microsoft backbone network, eliminating exposure to the public internet. Plan your network architecture before building pipelines. Retrofitting private connectivity into an existing deployment is significantly more complex than designing it from the start.

Logging Enables Troubleshooting

When something goes wrong at 2 AM, your ability to diagnose the problem depends entirely on what information you captured beforehand. Centralized logging using Eventhouse gives you a queryable record of everything that happened across your pipeline. Log more than you think you need initially. Storage is inexpensive compared to the cost of troubleshooting without adequate information.

Key Decisions You Need to Make

Before proceeding with implementation, your team should align on several important decisions.

• Data retention requirements affect storage costs and query performance. How long do you need to keep Bronze layer data versus aggregated Gold layer data?
• Recovery time objectives determine how you architect for resilience. If the pipeline can be down for four hours without business impact, your approach differs from a scenario where even 15 minutes causes significant problems.
• Who owns data quality shapes how you design validation and alerting. If source system teams are responsible, your pipeline detects and reports. If your team owns quality, you implement correction and enrichment.

Moving Forward

The patterns and practices in this guide reflect lessons learned from real implementations. Every organization has unique requirements, but the fundamentals of data quality, lag management, capacity planning, and operational readiness apply universally.

Start with the foundations. A pipeline that handles one source reliably is more valuable than one that theoretically handles fifty but fails unpredictably. Build observability from day one. Automate responses to common problems. Document what you learn.

Your data is a strategic asset. The pipeline that delivers it reliably deserves the same careful engineering you would apply to any critical business system.