60 lines
1.5 KiB
Markdown
60 lines
1.5 KiB
Markdown
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## Data Maturity Model
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Maslow hierarchy of needs, data version:
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### Data collection
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You get data from places.
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You place it as is in some staging area.
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### Data wrangling
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Cleaning stuff up, deduplicating, yadi yada.
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### Data integration
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Place everything together nicely in the same place in a usable model.
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## ETL
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ETL made sense before. Storage was expensive and you only wanted to load the strictly necessary data into the target DWH.
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But this comes with problems:
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- You have a lot of statefulness.
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- Debugging and testing pipelines is a pain in the ass.
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- You need to do transformations outside of your target database.
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- Schema changes were nightmares.
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ELT is the new shiny toy:
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- We read raw data from source system and load it into our DWH/Data Lake.
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- We do our transformations in the target system.
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- Schema changes become much more manageable.
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## Datawarehouses and data lakes
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### Datawarehouse
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DWH -> Any database that:
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- We use to ground our BI and reporting on.
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- Optimized for reads.
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- Typically structured in facts and dimensions.
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### Data Lake
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Decouple storage from compute. Use S3/Blob Storage/Hadoop HDFS for storage. Everything gets stored as files. Use a separate query engine, like Athena, Trino, Spark.
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### Data Lakehouse
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Just use both. A data lake with some DWH layer on top. Pretty much, a swamp of files with some governance, modelling tool sitting on top of it to control access and ease queries.
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## The modern data stack
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