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List down items you would like/need to do if given more time.
Next Steps
The analytical queries are not tuned for massive scale. They would need to be tuned for scale and leverage the power of Redshift's query optimization techniques like carefully chosen distkey and sortkey.
Use Amazon Spectrum to directly query S3 data and have Redshift act as a conduit between the business application and the OLAP engine.
Tweak the data model that mimics the actual GC business model more closely. There are opportunities to merge certain entities to facilitate better OLAP queries.
Do a UI that floats up data and presents powerful visualizations.
Implement a more advanced algorithm for breakage forecast.
An ambitious goal would be to develop a robust breakage forecastMachine Learning model trained on actual production data. This is of significant commercial value.
Thoughts on cost
Point 1 requires a massive amount of actual test data, and requires some effort.
Points 2 through 4 in the thoughts mentioned above, are fairly straightforward.
Point 5 requires effort and a more comprehensive data model and business research.
Point 6 is necessary to build production worthy code. It takes a non-trivial amount of time and effort.
Point 7 is a significantly complex effort, but has the highest commercial value. It's probably a valuable goal for the GC business and can be a real income generator.
The text was updated successfully, but these errors were encountered:
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List down items you would like/need to do if given more time.
Next Steps
distkey
andsortkey
.breakage forecast
Machine Learning model trained on actual production data. This is of significant commercial value.Thoughts on cost
The text was updated successfully, but these errors were encountered: