Data Warehouse VS Data Lakes
Similarities:
- Stores data
Differences:
- Processed VS Raw data
- Users: BI VS DS
- Purpose VS No Purpose
- Costly & hard to change data VS Easy accessibility
Tools
DW: AWS Redshift, Snowflake, Google BigQuery
DL: AWS Data Lake, Azure Data Lake
When not to use #MachineLearning / #AI:
*you're not automating
*no data
*you can look up the answer
*there's no pattern that connects inputs & outputs
*you can find a recipe today but it won't apply tomorrow (nonstationarity)
#ML#rstats#DataScience
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