Problem Statement

Enterprises that run SODA checks lack an automated framework for data ingestion, structure, governance and quality results across platforms. Manual API extraction and JSON transformation create inconsistencies with duplication and reporting gaps.

Why It Matters

• Cost: Manual ingestion increases engineering overhead and is prone to error.

• Risk: Incomplete rule tracking weakens data trust.

• Reliability: No standardised history or deduplication logic reduces reliability.

• Compliance: Lack of traceability impacts audit readiness and raises compliance issues.

• Velocity: Delayed insights slow remediation cycles.

  
  

What Cloudaeon Delivers

In such scenarios, Cloudaeon implements SODA data quality integration using Prophecy to automate ingestion, transformation and governance of quality metrics.  

SODA APIs are executed via Databricks notebooks, where landing structured JSON/CSV outputs into Blob Storage. Prophecy Low Code ETL pipelines ingest data into Delta Lake tables with batch control and deduplication logic. Analytics models apply SCD Type 2 for rule and dataset dimensions, while partitioned fact tables capture rule execution  

Airflow DAGs are used to orchestrate end-to-end workflows. Curated datasets are loaded into Azure Synapse Analytics with trace logging in Azure SQL DB. This enables enterprise reporting and governance. It also establishes scalable data quality automation across domains. 

Ideal For

Data Engineering, data governance & quality, analytics platforms teams, enterprise BI leadership 

Pain Signals

• “We can’t track SODA results historically.”

• “JSON ingestion is messy and manual.”

• “No audit trail for quality rule execution.”

• “Synapse reporting lacks quality metrics.”

Conclusion

For data quality and automation while SODA checks. Cloudaeon transforms fragmented SODA outputs into governed, analytics-ready quality intelligence, automated, traceable, and enterprise-aligned.