Tags: data engineering*

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  1. This article provides Python tricks and techniques for data ingestion, validation, processing, and testing in data engineering projects. It offers practical solutions for streamlining the code, including tips for data validation, handling errors, and testing.
    2024-06-13 Tags: , by klotz
  2. An exploration of the benefits of switching from the popular Python library Pandas to the newer Polars for data manipulation tasks, highlighting improvements in performance, concurrency, and ease of use.
  3. An in-process analytics database, DuckDB can work with surprisingly large data sets without having to maintain a distributed multiserver system. Best of all? You can analyze data directly from your Python app.
  4. An article discussing a simple and free way to automate data workflows using Python and GitHub Actions, written by Shaw Talebi.
  5. Learn data engineering through free courses, tutorials, books, tools, guides, roadmaps, practice exercises, projects, and other resources.
  6. This article describes how to use GNU Emacs for quick data visualization in combination with Gnuplot. It provides a command that can be used to visualize the correlation of data without needing any setup or specific files. The article also includes an example of a command for generating a graph using a data range selected with a rectangle command copy-rectangle.
  7. - standardization, governance, simplified troubleshooting, and reusability in ML application development.
    - integrations with vector databases and LLM providers to support new applications -
    provides tutorials on integrating
  8. Notebooks are not enough for ML at scale
  9. Intro to Streamlit
    - Simple and complex Streamlit example
    - Data and state management in Streamlit apps
    - Data widgets for Streamlit apps
    - Deploying Streamlit apps
    2024-04-17 Tags: , , , by klotz
  10. 2024-01-11 Tags: , , , , , by klotz

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