#180 – Data Engineering 101

Data engineering is a critical field in data science that involves preparing the « big data » infrastructure to be analyzed by data scientists. In this episode we are discussing the differences and how important each is with our guests.

👥 Guests

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– Mahmoud Fettal: https://twitter.com/mahmoudfettal

– Salim Jannah: https://www.linkedin.com/in/salim-janah

– Omaima Khalil: https://twitter.com/BadQuinn3

⏱️ Timeline

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0:00:00 – Introduction and welcoming

0:02:50 – What is data engineering?

0:08:43 – What are the key skills required for a data engineer?

0:16:40 – How does data engineering differ from data science?

0:20:00 – Data analyst vs data engineer vs data scientist

0:22:41 – What are the common tools used in data engineering?

0:28:57 – What are data pipelines?

0:34:54 – What challenges do data engineers face?

0:42:12 – Q&A

0:53:42 – How important is real -time data processing in data engineering?

1:02:35 – What is a data lake, and how does it differ from a data warehouse?

1:12:52 – How do data engineers use machine learning?

1:18:01 – Types of projects really involved with Data engineering

1:32:17 – What future trends should data engineers be aware of?

1:41:00 – Geeksblabla Picks

2:18:30 – Conclusion and Goodbye

🔗 Links

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– Apache Airflow vs Mage.ai: https://www.cidrdb.org/cidr2021/papers/cidr2021_paper17.pdf

– Lakehouse paper: https://medium.com/odicis-data-engineering/apache-airflow-vs-mage-ai-in-data-engineering-745c040a05e8

– Open Source Agent for Data Analysis: https://pandas-ai.com/

– Simplifying Data Engineering and Analytics with Delta: https://www.packtpub.com/product/simplifying-data-engineering-and-analytics-with-delta/9781801814867

🎤 Hosts

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– Meriem Zaid: https://twitter.com/_iMeriem

🔗 Follow us

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