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The Lifecycle of Feature Engineering: From Raw Data to Model-Ready Inputs

KDnuggets

By Jayita Gulati on July 16, 2025 in Machine Learning Image by Editor In data science and machine learning, raw data is rarely suitable for direct consumption by algorithms. Understanding Raw Data Raw data contains inconsistencies, noise, missing values, and irrelevant details.

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Building End-to-End Data Pipelines: From Data Ingestion to Analysis

KDnuggets

Before trying to understand how to deploy a data pipeline, you must understand what it is and why it is necessary. A data pipeline is a structured sequence of processing steps designed to transform raw data into a useful, analyzable format for business intelligence and decision-making. Why Define a Data Pipeline?

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Build ETL Pipelines for Data Science Workflows in About 30 Lines of Python

KDnuggets

Common transformations include data type conversions, field mapping, aggregations, and the removal of duplicates or invalid records. Finally, the load phase transfers the now transformed data into the target system. The loading strategy depends on factors such as data volume, system performance requirements, and business needs.

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10 Python Math & Statistical Analysis One-Liners

KDnuggets

These one-liners show how to extract meaningful info from data with minimal code while maintaining readability and efficiency. Calculate Mean, Median, and Mode When analyzing datasets, you often need multiple measures of central tendency to understand your datas distribution.

Python 105
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Build Your Own Simple Data Pipeline with Python and Docker

KDnuggets

For our example, we will use the heart attack dataset from Kaggle as the data source to develop our ETL process. data:/data The YAML file above, when executed, will build the Docker image from the current directory using the available Dockerfile. simple_pipeline_container | Data Transformation completed.

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The Race For Data Quality in a Medallion Architecture

DataKitchen

It sounds great, but how do you prove the data is correct at each layer? How do you ensure data quality in every layer ? Bronze, Silver, and Gold – The Data Architecture Olympics? The Bronze layer is the initial landing zone for all incoming raw data, capturing it in its unprocessed, original form.

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Data Engineering Roadmap, Learning Path,& Career Track 2025

ProjectPro

Source: Image uploaded by Tawfik Borgi on (researchgate.net) So, what is the first step towards leveraging data? The first step is to work on cleaning it and eliminating the unwanted information in the dataset so that data analysts and data scientists can use it for analysis.