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license: mit
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---
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license: mit
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---
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# Bixi's OpenData Modelisation
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Here is a huggingface dataset refined with [AntoineGiraud/dbt_bixi_opendata](https://github.com/AntoineGiraud/dbt_bixi_opendata) dbt-core project<br>
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that loads & transform [bixi OpenData](https://bixi.com/fr/donnees-ouvertes/) thanks to [DuckDB](https://duckdb.org/) 🦆🚀
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## Viz' exploration ideas
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I used Power BI to explore the transformed data offloaded to .parquet (~ 4.7 times lighter than .csv)
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After the pandemic, Montrealers realy went back to bixi 🥳
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## Data sources
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### Bixi Rentals OpenData ([link](https://bixi.com/fr/donnees-ouvertes/))
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- 🚲 **Rentals V1** : from 2014 to 2021
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> for station info, join to station
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yearly file with station_code
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- ⛽ **Stations V1** : from 2014 to 2021
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> 1 station code per year
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- 🚲 **Rentals V2** : from 2022 to 2024+
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> start/end station info on each rentals\
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*-> 2.7 times heavier `.csv`* 1.4Gb -> 0.5Gb\
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*-> 2.3 times heavier `.parquet` 250Mb -> 106Mb*
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### GIS referential
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- 🧭 **Municipal sectors** : from the OD 2013 survey (cf. [donnees quebec](https://www.donneesquebec.ca/recherche/dataset/artm-secteurs-municipaux-od13/resource/95ab084b-727e-4322-9433-0fed7baa690d))
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## Schema/DB steps :
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- **raw** : raw tables loaded as is from .csv
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- **stg** : intermediate tables
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- **dtm** : tables ready for analytics & reporting use
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### Loading
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DuckDB realy shines by it's speed & local OLAP capabilities 😎
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Here is 🚲 v1 rentals (2014 - 2021) load & offload to .parquet
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- `.csv` is **4.5** times heavier than `.parquet`
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- `.json` is **2.7** times heavier than `.csv`
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