In one of my projects, I designed a data architecture to handle data coming from multiple sources such as application databases, third-party APIs, event logs, and flat files. The main challenge was that the data volume was large, it arrived at different speeds, and different teams needed it for different purposes.
I started by setting up a cloud-based data lake where all incoming data was stored in its raw form. This acted like a safety net — if something broke downstream, we could always go back to the original data. From there, I built automated pipelines to clean, standardize, and transform the data before loading it into a data warehouse for reporting and analytics.
To make the system reliable, I added validation checks at every stage, like row counts, schema checks, and null value detection. If anything looked off, alerts were triggered so we could fix issues before they affected dashboards or reports. I also separated the architecture into layers (raw, processed, and curated) so data stayed organized and easy to manage.
Performance and cost were also important. I optimized pipelines to run only when needed, used partitioning for large tables, and scaled compute resources based on workload. This helped keep processing fast without wasting money.
The final setup allowed business users to access clean dashboards, while data scientists could work with trusted, well-structured data for modeling and analysis. As a data engineer, the focus was on building a system that could grow with the business, stay stable over time, and make data easy for everyone to use.
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