spark_app_twitter
ibis
| spark_app_twitter | ibis | |
|---|---|---|
| 3 | 23 | |
| 60 | 4,241 | |
| - | 6.5% | |
| 0.0 | 10.0 | |
| almost 2 years ago | 1 day ago | |
| Python | Python | |
| - | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
spark_app_twitter
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Trying to dockerize an all python data engineering project
You can see the structure of everything in my repository: https://github.com/jmcmt87/spark_app_twitter
- GitHub - jmcmt87/spark_app_twitter: A data engineering project (Twitter monitor app)
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Portfolio Review: I'd like to start my career as a data engineer
I made this project on my own as a portfolio and I'd really appreciate any feedback or advice: https://github.com/jmcmt87/spark_app_twitter
ibis
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Show HN: Hashquery, a Python library for defining reusable analysis
I really don't understand the appeal of dbt vs a proper programming language. The templating approach leads to massive spaghetti. I look forward to trying out something like Ibis [0]
0: https://ibis-project.org/
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This Week In Python
ibis – portable Python dataframe library
- Ibis: The portable Python dataframe library
- FLaNK Stack 26 February 2024
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Quarto
The main benefit is that you get a Python (or R, Julia or Rust) interpreter. So you can evaluate code. A good example of the value of this is the Ibis docs which use Quarto: https://ibis-project.org/
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Polars – A bird's eye view of Polars
Ive found polars quite intuitive, though for python, I lean more towards [ibis](https://ibis-project.org/). The interface is nearly identical, but ibis has the benefit if building sql queries before pulling any actual data (like dbplyr) — whereas polars requires the data to be in-memory (at least for rdb’s, though correct me if Im wrong)
this to me seems like a good argument for only using ibis, but Im happy to be convinced otherwise
- Ibis – Universal Interface for Data Wrangling
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Vanna.ai: Chat with your SQL database
Please add Ibis Birdbrain https://ibis-project.github.io/ibis-birdbrain/ to the list. Birdbrain is an AI-powered data bot, built on Ibis and Marvin, supporting more than 18 database backends.
See https://github.com/ibis-project/ibis and https://ibis-project.org for more details.
- Ibis
What are some alternatives?
Traffic-Data-Analysis-with-Apache-Spark-Based-on-Mobile-Robot-Data - Mobile robot data were analyzed with Apache-Spark to extract five different statistical result such as travel time, waiting time, average speed, occupancy and density were produced.
snowflake-connector-python - Snowflake Connector for Python
astro - Astro SDK allows rapid and clean development of {Extract, Load, Transform} workflows using Python and SQL, powered by Apache Airflow. [Moved to: https://github.com/astronomer/astro-sdk]
PySpark-Boilerplate - A boilerplate for writing PySpark Jobs
DataEngineeringProject - Example end to end data engineering project.
Apache Impala - Apache Impala
portfolio_computerVision - Some of my projects on computer vision
pangres - SQL upsert using pandas DataFrames for PostgreSQL, SQlite and MySQL with extra features
DataScience_portfolio - This is my data science portfolio
sqlite_scanner - DuckDB extension to read and write to SQLite databases
astro-sdk - Astro SDK allows rapid and clean development of {Extract, Load, Transform} workflows using Python and SQL, powered by Apache Airflow.
katacoda