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Five Interesting Data Engineering Projects

KDnuggets

As the role of the data engineer continues to grow in the field of data science, so are the many tools being developed to support wrangling all that data. Five of these tools are reviewed here (along with a few bonus tools) that you should pay attention to for your data pipeline work.

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Go vs. Python for Modern Data Workflows: Need Help Deciding?

KDnuggets

Blog Top Posts About Topics AI Career Advice Computer Vision Data Engineering Data Science Language Models Machine Learning MLOps NLP Programming Python SQL Datasets Events Resources Cheat Sheets Recommendations Tech Briefs Advertise Join Newsletter Go vs. Python for Modern Data Workflows: Need Help Deciding?

Python 283
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Developing an End-to-End Automated Data Pipeline

Analytics Vidhya

This article was published as a part of the Data Science Blogathon. Introduction Data acclimates to countless shapes and sizes to complete its journey from a source to a destination. The post Developing an End-to-End Automated Data Pipeline appeared first on Analytics Vidhya.

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Getting Started with Data Pipeline

Analytics Vidhya

This article was published as a part of the Data Science Blogathon. Introduction These days companies seem to seek ways to integrate data from multiple sources to earn a competitive advantage over other businesses. The post Getting Started with Data Pipeline appeared first on Analytics Vidhya.

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Build a Data Cleaning & Validation Pipeline in Under 50 Lines of Python

KDnuggets

Instead of writing the same cleaning code repeatedly, a well-designed pipeline saves time and ensures consistency across your data science projects. In this article, well build a reusable data cleaning and validation pipeline that handles common data quality issues while providing detailed feedback about what was fixed.

Python 255
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How to Implement a Data Pipeline Using Amazon Web Services?

Analytics Vidhya

Introduction The demand for data to feed machine learning models, data science research, and time-sensitive insights is higher than ever thus, processing the data becomes complex. To make these processes efficient, data pipelines are necessary. appeared first on Analytics Vidhya.

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Automate Data Quality Reports with n8n: From CSV to Professional Analysis

KDnuggets

While most people associate workflow automation with business processes like email marketing or customer support, n8n can also assist with automating data science tasks that traditionally require custom scripting. Before diving into analysis, you need to understand what youre working with: How many missing values?