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Top 20 Data Engineering Project Ideas [With Source Code]

Analytics Vidhya

This article presents the top 20 data engineering project ideas with their source code. Whether you’re […] The post Top 20 Data Engineering Project Ideas [With Source Code] appeared first on Analytics Vidhya. Aspiring data engineers often seek real-world projects to gain hands-on experience and showcase their expertise.

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Automating dead code cleanup

Engineering at Meta

Meta’s Systematic Code and Asset Removal Framework (SCARF) has a subsystem for identifying and removing dead code. SCARF combines static and dynamic analysis of programs to detect dead code from both a business and programming language perspective. These are combined and form an augmented dependency graph.

Coding 119
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Code Review on Printed Paper: an Excerpt from the Twitoons Comic Book

The Pragmatic Engineer

Today’s newsletter closes with a full chapter from this book, visualizing when Elon Musk demanded all Twitter software engineers print out their code on paper (!!) and report for code review. Code review on printed paper: an excerpt from the Twitoons book A year ago, the end of October 2022 was a very turbulent time at Twitter.

Coding 191
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How to Develop Serverless Code Using Azure Functions?

Analytics Vidhya

Introduction Azure Functions is a serverless computing service provided by Azure that provides users a platform to write code without having to provision or manage infrastructure in response to a variety of events. Azure functions allow developers […] The post How to Develop Serverless Code Using Azure Functions?

Coding 239
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How to Normalize Relational Databases With SQL Code?

Analytics Vidhya

So, we are […] The post How to Normalize Relational Databases With SQL Code? If a corrupted, unorganized, or redundant database is used, the results of the analysis may become inconsistent and highly misleading. appeared first on Analytics Vidhya.

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An explosion in software engineers using AI coding tools?

The Pragmatic Engineer

GitHub surveyed 500 developers in the US for a sense of how they use AI coding tools. I examine the results and add context on how the survey was conducted.

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Drag, Drop, Analyze: The Rise of No-Code Data Science

KDnuggets

No-code or low-code functionalities in data science have gained significant traction in recent years. These solutions are well-proven and matured, and they make data science more accessible to a wider range of people.