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Home Artificial Intelligence

How Information Engineering Developed since 2014 | by 💡Mike Shakhomirov | Jul, 2024

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July 29, 2024
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Prime tendencies to assist your knowledge pipelines scale with ease

💡Mike Shakhomirov

Towards Data Science

13 min learn

·

22 hours in the past

AI generated picture utilizing Kandinsky

On this dialogue, I intention to discover the evolving tendencies in knowledge orchestration and knowledge modelling, highlighting the developments in instruments and their core advantages for knowledge engineers. Whereas Airflow has been the dominant participant since 2014, the information engineering panorama has considerably reworked, now addressing extra subtle use circumstances and necessities, together with assist for a number of programming languages, integrations, and enhanced scalability. I’ll look at up to date and maybe unconventional instruments that streamline my knowledge engineering processes, enabling me to effortlessly create, handle, and orchestrate sturdy, sturdy, and scalable knowledge pipelines.

Over the last decade we witnessed a “Cambrian explosion” of varied ETL frameworks for knowledge extraction, transformation and orchestration. It’s not a shock that lots of them are open-source and are Python-based.

The most well-liked ones:

  • Airflow, 2014
  • Luigi, 2014
  • Prefect,2018
  • Temporal, 2019
  • Flyte, 2020
  • Dagster, 2020
  • Mage, 2021
  • Orchestra, 2023

READ ALSO

Scaling Characteristic Engineering Pipelines with Feast and Ray

Optimizing Token Era in PyTorch Decoder Fashions


Prime tendencies to assist your knowledge pipelines scale with ease

💡Mike Shakhomirov

Towards Data Science

13 min learn

·

22 hours in the past

AI generated picture utilizing Kandinsky

On this dialogue, I intention to discover the evolving tendencies in knowledge orchestration and knowledge modelling, highlighting the developments in instruments and their core advantages for knowledge engineers. Whereas Airflow has been the dominant participant since 2014, the information engineering panorama has considerably reworked, now addressing extra subtle use circumstances and necessities, together with assist for a number of programming languages, integrations, and enhanced scalability. I’ll look at up to date and maybe unconventional instruments that streamline my knowledge engineering processes, enabling me to effortlessly create, handle, and orchestrate sturdy, sturdy, and scalable knowledge pipelines.

Over the last decade we witnessed a “Cambrian explosion” of varied ETL frameworks for knowledge extraction, transformation and orchestration. It’s not a shock that lots of them are open-source and are Python-based.

The most well-liked ones:

  • Airflow, 2014
  • Luigi, 2014
  • Prefect,2018
  • Temporal, 2019
  • Flyte, 2020
  • Dagster, 2020
  • Mage, 2021
  • Orchestra, 2023
Tags: DataEngineeringEvolvedJulMikeShakhomirov

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