Titlebook: Data Engineering for Machine Learning Pipelines; From Python Librarie Pavan Kumar Narayanan Book 2024 Pavan Kumar Narayanan 2024 Artificial

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es and methodologies, to move through the next decade of dat.This book covers modern data engineering functions and important Python libraries, to help you develop state-of-the-art ML pipelines and integration code...The book begins by explaining data analytics and transformation, delving into the P

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te. This is where Polars excels. Polars is a fast data manipulation library programmed in Rust programming and integrates well with Python’s data analysis ecosystem. Polars supports lazy evaluation and automatic query optimization, enabling data engineers to handle even the most demanding data wrangling tasks easily.

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Mehrjahresprogramme Im Strassenbauis. In this chapter, we will explore CuDF, a GPU-accelerated data manipulation library that integrates well with the existing Python ecosystem. Before we jump into CuDF, let us also review the architecture of CPU- and GPU-based computations and concepts of GPU programming.

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Johannes M. Ruhland,Klaus D. Wildetform. Kafka helps build event streaming pipelines that can capture creation of new data and modification of existing data in real time and route it appropriately for various downstream consumption purposes. In this chapter, we will look at Kafka, its architecture, and how Kafka can help build real-time data pipelines.

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Beyond NNCI: International Facilities,e a workflow without DAGs. You can still define DAGs in Prefect though. The features, components, and typical workflow of Prefect appear different from what we have seen with Apache Airflow. As for which tool is a better choice, it depends on many factors like team, requirements, and infrastructure, among other things.

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