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Data Engineering Projects

Start Data Engineering

Introduction Whether you are new to data engineering or have been in the data field for a few years, one of the most challenging parts of learning new frameworks is setting them up! Projects from least to most complex 3.2. Batch pipelines 3.3. Stream pipelines 3.4. Event-driven pipelines 3.5. LLM RAG pipelines 4. Conclusion 1.

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Python Essentials for Data Engineers

Start Data Engineering

Introduction Data is stored on disk and processed in memory Running the code Run on Codespaces Run on your laptop Using python REPL Python basics Python is used for extracting data from sources, transforming it, & loading it into a destination [Extract & Load] Read and write data to any system [Transform] Process data in Python or instruct (..)

Python 147
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What I’ve Learned After A Decade Of Data Engineering

Confessions of a Data Guy

After 10 years of Data Engineering work, I think it’s time to hang up the proverbial hat and ride off into the sunset, never to be seen again. Sometimes I wonder if I’ve learned anything […] The post What I’ve Learned After A Decade Of Data Engineering appeared first on Confessions of a Data Guy.

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What Data Engineers Really Do?

Analytics Vidhya

In a data-driven world, behind-the-scenes heroes like data engineers play a crucial role in ensuring smooth data flow. A data engineer investigates the issue, identifies a glitch in the e-commerce platform’s data funnel, and swiftly implements seamless data pipelines.

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Top 20 Data Engineering Project Ideas [With Source Code]

Analytics Vidhya

Data engineering plays a pivotal role in the vast data ecosystem by collecting, transforming, and delivering data essential for analytics, reporting, and machine learning. Aspiring data engineers often seek real-world projects to gain hands-on experience and showcase their expertise.

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Data Engineering Weekly #175

Data Engineering Weekly

Experience Enterprise-Grade Apache Airflow Astro augments Airflow with enterprise-grade features to enhance productivity, meet scalability and availability demands across your data pipelines, and more. As I have shared , its impact on data engineering is exciting.

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Docker Fundamentals for Data Engineers

Start Data Engineering

Most data projects use Docker to set up the data infra locally (and often in production). Introduction 2. Docker concepts 2.1. Define the OS and its configurations with an image 2.2. Use the image to run containers 2.2.1. Communicate between containers and local OS 2.2.2. Start containers with docker CLI or compose 3. Conclusion 1.