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How a data-processing problem at Lyft became the basis for Eventual
Eventual's data processing engine Daft was inspried by the founders' experience working on Lyft's autonomous vehicle project.
GitHub4d
GitHub - btholath/consumer_spending: Generates a synthetic consumer spending dataset in CSV and Parquet formats with sampling methods (First-K, Random, Stratified). Ideal for ...
consumer_spending_project/ ├── data/ # Generated CSV, Parquet, and samples ├── generate_dataset.py # Creates the dataset ├── sample_methods.py # Sampling logic └── requirements.txt # Python ...
GitHub1d
GitHub - 5c077-60rd0n/retail-data-pipeline: A lightweight end-to-end data pipeline using Python to simulate the preprocesing and transformation flow for a machine learning ...
This step simulates the ingestion of multi-format data sources common in AI pipelines. 📄 Input Datasets: customers.csv — structured (tabular) customer data; products.json — semi-structured product ...