Centralized Repository:
Data is ingested into a secure data lake house for storage and access.
Cleaning and Preparation:
Raw data often needs cleaning and transformation to ensure consistency, accuracy, and usability for analysis.
Adding Context:
In some cases, data can be enriched by combining it with external data sources or additional internal data points. This can provide richer context and unlock deeper insights.
Accessibility for Users:
The processed data needs to be delivered to the intended users in a clear and accessible format.
Proactive Approach:
A successful pipeline actively monitors for errors, data quality issues, and system performance. Robust error handling mechanisms ensure timely identification and resolution of problems.
Scalability:
As data volumes and business needs evolve, the pipeline should be scalable to accommodate growth and adaptable to integrate new data sources or analysis tools.