
Real-time updates with continuous data flow
Eliminated need for expensive migration projects
Dynamic SCDs improved data quality and accuracy
Challenge
Qrious needed to dynamically ingest data from multiple sources using a metadata-driven approach.
Qrious needed to dynamically ingest data from multiple sources using a metadata-driven approach.
Solution
The team created a utility that utilized parameters and metadata stored in Snowflake and dynamically extracted and ingested data from various sources, including SQL Server and SharePoint.
The team eliminated the need for costly data migration processes, enabled regular data ingestion, including Change Data Capture, and the creation of dynamic SCDs.
The team created a utility that utilized parameters and metadata stored in Snowflake and dynamically extracted and ingested data from various sources, including SQL Server and SharePoint.
The team eliminated the need for costly data migration processes, enabled regular data ingestion, including Change Data Capture, and the creation of dynamic SCDs.
Results
- Substantial cost savings in data management operations- avoiding complex data migration.
- Continuously extracting and ingesting data into target systems led to a smoother and more streamlined data pipeline.
- Reduced downtime and quality assurance.
- Creating dynamic Slowly Changing Dimensions (SCDs) has improved data quality and accuracy, ensuring Qrious maintains a comprehensive and reliable dataset for analytics.
- Substantial cost savings in data management operations- avoiding complex data migration.
- Continuously extracting and ingesting data into target systems led to a smoother and more streamlined data pipeline.
- Reduced downtime and quality assurance.
- Creating dynamic Slowly Changing Dimensions (SCDs) has improved data quality and accuracy, ensuring Qrious maintains a comprehensive and reliable dataset for analytics.
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