This project provides data tools and low friction access to versioned datasets within CDC's Center for Forecasting and Outbreak Analytics (CFA) virtual private cloud (VPC). It includes end-to-end ETL pipelines for configured data sources, custom workflows, schema validation patterns, and submodules that expand common data visualization and modeling/interpolation patterns. The project demonstrates best practices for data processing within the CFA environment.
- Configuration-driven ETL pipelines using TOML files
- SQL-based transformations using DuckDB, or Polar's built-in SQL context
- Azure Blob Storage integration for raw and transformed data storage
- Utilities for time-stamping and template loading
- Data validation and quality checks
- Custom workflows
- Tooling for easy access to Socrata Open Data API (e.i., data.cdc.gov) (copied and modified from here)
- Ability to initialize new dataset catalog libraries and maintain datasets in more than one repo
- Clone the repository and install dependencies with
poetry install(requirespoetry >= 2.0) - Ensure your have access to the relevant resources (e.g.,
az login --identity) - See which datasets are available:
from cfa.dataops import datacat print(datacat.__namespace_list__)
- See which versions of a dataset are available (blob endpoints for datasets will be namedL
.load,.extract, or.stage_##):print(datacat.{{dataset}}.load.get_versions())
- Get a specific version of a dataset:
df = datacat.{{dataset}}.load.get_dataframe() df.glimpse()
Read the Dataset User Guide for more information about accessing datasets.
Read the Dataset Developer Guide for information about how to run ETL to add new versions of an existing datasets and about how to create new datasets.
- Thomas Hladish [email protected] (CDC/OD/ORR/CFA)
- Phillip Rogers [email protected] (CDC/OD/ORR/CFA)(CTR)
- Ryan Raasch [email protected] (CDC/OD/ORR/CFA)(CTR)
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