Learning note / Reflections on my studies
Data Engineering
Data engineering makes me think about the work that has to happen before an analysis is useful. A result depends on how data arrives, how it changes, and whether the process can be trusted again next time.
Following the data through the system
I want to understand a record's journey from its source to the place where someone uses it. Each transformation makes assumptions about format, meaning, and completeness.
That is why I think of a pipeline as something to explain and validate, rather than simply a script that moves values between two locations.
When the work becomes larger
Distributed storage and processing introduce questions that a small local program can hide. Splitting work is only useful if the pieces can be combined into a result whose meaning stays clear.
MapReduce and Spark are useful reference points for that shift in scale. Understanding the processing model matters before making claims about what a tool will solve.
Connecting infrastructure to analysis
I see a direct connection to operational summaries: the person reading the result needs consistent definitions, not just a completed job. I want the pipeline to make failures visible and preserve enough context to understand an unexpected number.
What I take forward
- Make transformations and assumptions explicit.
- Understand how distributed work is combined.
- Treat data quality as part of the delivered result.