#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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